Grant List
Represents Grant table in the DB
GET /v1/grants?sort=-other_investigators
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-other_investigators", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-other_investigators", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=-other_investigators", "prev": null }, "data": [ { "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 } }, { "type": "Grant", "id": "15673", "attributes": { "award_id": "1R01HL172872-01A1", "title": "Targeting Angiopoietin-like 4 (ANGPTL4) in Severe Community Acquired Pneumonia", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Heart Lung and Blood Institute (NHLBI)" ], "program_reference_codes": [], "program_officials": [ { "id": 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": "2025-04-04", "end_date": "2029-01-31", "award_amount": 827909, "principal_investigator": { "id": 32515, "first_name": "William A", "last_name": "Altemeier", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32516, "first_name": "Pavan Kumar", "last_name": "Bhatraju", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 159, "ror": "https://ror.org/00cvxb145", "name": "University of Washington", "address": "", "city": "", "state": "WA", "zip": "", "country": "United States", "approved": true }, "abstract": "Community-acquired pneumonia (CAP) is a common cause of morbidity and mortality in hospitalized patients but therapeutics are limited. In response, identification of modifiable pathways to alter host response and improve outcomes in patients with severe CAP has been highlighted as a NHLBI research priority. Our research group has identified angiopoietin-like 4 (ANGPTL4) as a potential mediator in adverse outcomes in CAP from viral and bacterial pathogens. We have generated preliminary data in a discovery proteomic analysis of 5000 different plasma proteins. We found that ANGPTL4 was one of the top proteins associated with fewer ventilator free days and worse hospital mortality in severe CAP due to COVID-19. Next, in a multi-center cohort, we replicated these findings in COVID-19 that higher ANGPTL4 concentrations were associated with worse clinical outcomes, and obtained preliminary evidence that ANGPTL4 is also associated with outcomes in severe CAP due to bacteria3. We also have generated data that genetically targeting Angptl4 is protective in mice with severe influenza, a finding that is supported by pre-clinical data that inhibition of ANGPTL4 signaling through a monoclonal antibody is protective in viral pneumonia. In addition, independent research groups have also found that ANGPTL4 is associated with clinical outcomes in severe CAP. Together, these findings support our hypothesis that ANGPTL4 expression is a significant determinant of outcomes from CAP, independent of pathogen type, and that modulation can lead to improved clinical outcomes. To further examine this hypothesis, we will use complementary clinical and pre-clinical studies in the following aims. In Aim 1, we will determine the relationship between plasma ANGPTL4 levels and outcomes in a hospitalized population with varying severity at enrollment (acute care and ICU) and pathogen type (viral and bacterial). In Aim 2, we will infer causal relationships between ANGPTL4 concentrations and risk for pulmonary and extra- pulmonary organ dysfunction using a non-overlapping 2-sample Mendelian randomization genetic approach. In Aim 3, we will evaluate the role of ANGPTL4 in pre-clinical models of viral and bacterial pneumonia and determine the relative contributions of the proteolytically processed cANGPTL4 and nANGPTL4 peptides. The outstanding qualifications of our team in the fields of sepsis, community acquired pneumonia, molecular epidemiology, and pre-clinical models uniquely position us to deliver an integrated molecular view of host response in CAP that is not only responsive to the challenges in severe CAP care identified by global leaders, but could fundamentally alter paradigms of patient care in severe CAP. The long-term goals are to delineate the role of ANGPTL4 in severe CAP through understanding which clinical outcomes are most closely linked with ANGPTL4 levels through epidemiological and genetic causal inference analyses and to understand the cell of origin and relative contributions of different cleavage products of ANGPTL4 through pre-clinical studies.", "keywords": [ "ANGPTL4 gene", "Acute Renal Failure with Renal Papillary Necrosis", "American", "Bacteria", "Bacterial Pneumonia", "Biometry", "C-terminal", "COVID-19", "COVID-19 pandemic", "COVID-19 pneumonia", "Caring", "Cells", "Cessation of life", "Chest", "Clinical", "Clinical Research", "Clinical Trials", "Data", "Development", "Enrollment", "Epidemiology", "Follow-Up Studies", "Functional disorder", "Genetic", "Genetic Models", "Genotype", "Goals", "Health Care Costs", "Hospital Mortality", "Hospitalization", "Hour", "Human Genetics", "Immune response", "Infection", "Influenza", "Link", "Lung", "Measures", "Mechanical ventilation", "Mediator", "Mendelian randomization", "Metabolic Diseases", "Molecular", "Monoclonal Antibodies", "Morbidity - disease rate", "Mus", "N-terminal", "National Heart Lung and Blood Institute", "Organ", "Outcome", "Pathway interactions", "Patient Care", "Patients", "Peptides", "Permeability", "Plasma", "Plasma Proteins", "Pneumonia", "Population", "Positioning Attribute", "Pre-Clinical Model", "Preclinical data", "Process", "Protein Secretion", "Proteins", "Proteomics", "Pulmonary Inflammation", "Pulmonology", "Qualifying", "Research", "Research Priority", "Resolution", "Risk", "Role", "SARS-CoV-2 infection", "Sample Size", "Sampling", "Sepsis", "Severities", "Severity of illness", "Shock", "Signal Transduction", "Societies", "Source", "Streptococcus pneumoniae", "Testing", "Therapeutic", "Translational Research", "Variant", "Vascular Permeabilities", "Ventilator", "Viral", "Viral Pneumonia", "Virus", "acute care", "adverse outcome", "antimicrobial", "bacterial community", "biobank", "biological heterogeneity", "clinical heterogeneity", "clinical phenotype", "clinical translation", "cohort", "community acquired pneumonia", "epidemiological model", "experimental study", "genetic approach", "genetic epidemiology", "improved", "improved outcome", "influenza infection", "insight", "lipoprotein lipase", "molecular modeling", "mortality", "mouse model", "multidisciplinary", "pathogen", "pathogenic bacteria", "pathogenic virus", "patient population", "patient response", "pneumonia model", "pre-clinical", "preclinical study", "protein expression", "response" ], "approved": true } }, { "type": "Grant", "id": "15665", "attributes": { "award_id": "2444914", "title": "I-Corps: Translation Potential of an Online Healthcare Information (OHI) Trust Badge", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "I-Corps" ], "program_reference_codes": [], "program_officials": [ { "id": 602, "first_name": "Ruth", "last_name": "Shuman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-11-15", "end_date": null, "award_amount": 50000, "principal_investigator": { "id": 32174, "first_name": "Ankur", "last_name": "Chattopadhyay", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32173, "first_name": "Seth A", "last_name": "Adjei", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1396, "ror": "https://ror.org/01k44g025", "name": "Northern Kentucky University", "address": "", "city": "", "state": "KY", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this I-Corps project is the development of an online knowledge recommender tool and trust badge for consumers. Health misinformation remains a serious societal threat. Since the emergence of the COVID-19 pandemic, reports show on average that 8 out of 10 Americans search for online healthcare information (OHI), and 4 out of 10 Americans cannot correctly identify false healthcare claims. The goal of the new technology is to help alleviate confusion amongst consumers caused by the overwhelming amount of OHI, and to help OHI providers boost their reputation as a trustworthy source. The tool is designed to combat misinformation by proactively serving a wide spectrum of stakeholders who regularly deal with OHI content. The I-Corps project will focus on the specific issues and public challenges of endorsements in addition to fact checking of OHI content and contributing to a better understanding of the needs of people who use and/or provide OHI content. This solution serves as a foundation for a consultancy service providing platform offering advice plus training to OHI consumers and OHI providers. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a software tool that will serve online healthcare information (OHI) users by providing machine learning-based classification and certification of OHI content trustworthiness. Research has shown that machine learning-based classifiers can process OHI claims and classify them as fact or fake, but such solutions have not been directly integrated into web browsers and have been trained with primarily textual cues from mostly unimodal datasets. This technology addresses these limitations and is designed as a machine learning driven online knowledge recommender tool, prototyped as a web extension utility, which can be directly embedded into web browsers to seamlessly report trustworthiness of any OHI content. the solution is designed as a trust badge model for easy certification of web content and can function both as an online content classifier. This capability may allow both OHI consumers and OHI providers to validate and tag OHI websites' trustworthiness. Additionally, the solution is trained with multimodal data, that includes both textual and visual cues (e.g., image elements, graphic contents, and infographics), unlike existing solutions that do not include visual cues or image artifacts. 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": "15662", "attributes": { "award_id": "2401975", "title": "Excellence in Research: a PEC-AbP Dual Signal Amplification Method and its Mechanistic Study of Signal Transduction for DNA Sensing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "HBCU-EiR - HBCU-Excellence in" ], "program_reference_codes": [], "program_officials": [ { "id": 961, "first_name": "Aleksandr", "last_name": "Simonian", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-12-01", "end_date": null, "award_amount": 599991, "principal_investigator": { "id": 32171, "first_name": "Peng", "last_name": "He", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32170, "first_name": "Jianjun", "last_name": "Wei", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 708, "ror": "", "name": "North Carolina Agricultural & Technical State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "DNA sensing techniques have been widely applied in daily life such as medical diagnosis, biowarfare defense, forensic science, and environmental monitoring, and were significantly promoted during the past pandemic, e.g., reverse transcription polymerase chain reaction (RT-PCR) test for COVID-19. Rapid DNA detection with high sensitivity, specificity, and accuracy is in high demand, however limited by signal readout. This project is aimed at developing an innovative dual signal amplification method by integrating two different signal amplification methods, i.e., materials science- and optical-based. The research goals are to strengthen signal readouts and build field-friendly DNA sensors that are amenable to point-of-need applications with ultrasensitivity. The discovery of fundamental science and transformative technology will potentially enable a reliable multiplexed high-throughput DNA analysis platform that may greatly benefit health care in society and facilitate research and applications in biomedical and life science. The scientific learning of this interdisciplinary research performed at the HBCU (NC A&T) and MSI (UNC Greensboro) will advance sensing mechanism understanding, instruct and train students especially underrepresented students, in research and education, and engage K-12 STEM educators and students in science. Genetic information with or without variation coded within nucleic acids, indicating an illness or health outcome, is termed a nucleic acid biomarker, thus plays a crucial role in precision medicine. Sensitive and selective detection of nucleic acid biomarkers with rapid signal amplification is the key for early screening and diagnosis of human diseases. This project is aimed at developing an innovative dual signal amplification method and understanding the signal transduction mechanism for enhanced DNA sensing. The work is built on the seamless integration between amplification-by-polymerization (AbP) in DNA sensing for optical clarity change on surface based on effective mass growth upon DNA recognition and in-planar metallic film nanoarrays for plasmon-exciton coupling (PEC) optical enhancement. The research will be conducted in three stages to (1) fully explore the potential of the AbP-PEC dual signal amplification platform, (2) investigate the fundamental mechanism of the amplified signal transduction pertaining to the AbP-produced film thickness and plasmonic nanoslit structure, and (3) optimize the AbP-PEC platform for a portable DNA sensor in point-of-care diagnostics. The outcome may be transformative towards a multiplexed, rapid, highly sensitive, visible (by naked eyes) analysis of DNA in biofluids. 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": "15659", "attributes": { "award_id": "2416898", "title": "BPC-AE: STARS Computing Corps: Extending a National Community of Practice for Developing BPC Change Leaders", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "CISE Education and Workforce" ], "program_reference_codes": [], "program_officials": [ { "id": 7542, "first_name": "Subrata", "last_name": "Acharya", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": null, "award_amount": 5924905, "principal_investigator": { "id": 3184, "first_name": "Jamie", "last_name": "Payton", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 277, "ror": "https://ror.org/00kx1jb78", "name": "Temple University", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 15557, "first_name": "Tiffany M", "last_name": "Barnes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 29210, "first_name": "Susan", "last_name": "Fisk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 29675, "first_name": "Clarissa A", "last_name": "Thompson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32167, "first_name": "Veronica M", "last_name": "Catete", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 228, "ror": "https://ror.org/05e74xb87", "name": "New Jersey Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "The New Jersey Institute of Technology will extend STARS Computing Corps BPC Alliance. STARS aims to address the challenge of increasing the number and representation of Black, Hispanic, and women students who graduate with computing degrees and who remain in the field of computing after graduation. STARS serves as a national resource for transforming computer science and artificial intelligence education. Through this extension, STARS will continue its national community of practice and associated resource center to build capacity in college computing departments for developing more inclusive computing and AI educational experiences. This work builds upon a multi-year study, which provided evidence that the STARS Computing Corps approach is effective for broadening participation in computing goals, and indicates the value of a community of practice that engages college computing and AI faculty and students with a shared commitment. Ultimately, the work of STARS has the potential to increase student persistence in computing and AI research, degree programs, and careers. STARS creates significant knowledge, institutional, and human resources that can increase the reach of BPC research to a larger audience of researchers, educators, and K-20 students, and builds capacity to dramatically increase the number of people taking action in efforts to broaden participation in computing. STARS conferences, programs, and networks propagate evidence-based BPC approaches and advance peer-reviewed BPC scholarship. The key indicator for STARS impact is increased persistence for Black, Hispanic, and women students (and intersections thereof) in computing degree programs in institutions of higher education.This extension will 1) include new members and partnerships that expand the reach of STARS and that emphasize participation of Black and Hispanic students and faculty; 2) build capacity for evidence-based BPC practices for K12-university partnerships; 3) establish connections to STARS Alumni in industry to support professional networking and mentoring for current STARS students and to promote the persistence of STARS Alumni in the computing workforce. The project will also research: 4) how the STARS system of BPC interventions have longitudinal impacts on persistence in computing degree programs and the computing workforce with sample sizes that uniquely enable analyses of differential impacts at intersections of race, ethnicity, and gender, 5) how to adapt interventions to consider the changing landscape of needs for BPC, including changing university demographics, legislation that impacts BPC initiatives in higher education, the impacts of COVID on college student and faculty engagement, and the need to advance AI education, and 6) how to provide inclusive computing education experiences in the context of HBCUs, eHSIs, and community colleges. Finally, this extension will enable further research on broadening participation in computing, by providing early research opportunities for undergraduate students from underrepresented groups in computing and advancing dissemination of BPC research through the RESPECT and STARS Celebration conferences. 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": "15657", "attributes": { "award_id": "2436332", "title": "MPOPHC: Incorporation of Game Theory Tools to Improve the Policy Making to Mitigate Epidemics of Respiratory Diseases", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [ { "id": 622, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": null, "award_amount": 360000, "principal_investigator": { "id": 32166, "first_name": "Gokce", "last_name": "Dayanikli", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32165, "first_name": "Pamela P", "last_name": "Martinez", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "During the COVID-19 pandemic, it was observed that individuals did not always follow mitigation policies closely. Instead, they behaved according to their own objectives, where demographic and socioeconomic factors seemed to have influenced their responses to the set policies. Therefore, this project aims to improve the policymaking processes to mitigate the transmission of respiratory pathogens by incorporating the individuals’ decision-making and socio-demographic heterogeneities. To do this, the investigators propose to develop and study game theoretical mathematical models, as well as simulation tools and numerical approaches that can be adapted to specific public health problems of interest to practitioners and researchers. These tools will be made publicly available. This project will also involve interdisciplinary training for graduate students in applied mathematics, statistics, operations research, epidemiology, and quantitative biology. To model many interacting agents, the investigators will develop and study extensions of mean field games (MFGs). First, they will focus on building multi-population MFGs and graphon games to incorporate socio-demographic heterogeneities while finding the Nash equilibrium responses of individuals under different disease mitigation policies (e.g., vaccination policies and non-pharmaceutical interventions). Furthermore, different equilibrium notions to incorporate altruism in the populations will be explored through the introduction of mixed multi-population MFGs that include both cooperative and non-cooperative individuals. Later, the investigators will focus on finding optimal mitigation policies by using Stackelberg MFGs that include the optimization of a regulator (e.g., a governmental institution). The extensions of Stackelberg MFGs that include heterogeneities in the mean field populations, altruistic behaviors, and possible state variables for the regulator will be developed and analyzed. Surveys and analyses of publicly available data will be conducted to calibrate and parameterize the mathematical models to capture real-life patterns. Finally, numerical approaches and simulation toolboxes will be implemented to solve large dimensional and more complex models, which will allow policymakers to adapt and parametrize our models according to their specific needs. This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD). 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": "15655", "attributes": { "award_id": "2436340", "title": "MPOPHC: Quantitative design of effective testing-based policies through infection trajectory modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [ { "id": 622, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": null, "award_amount": 968765, "principal_investigator": { "id": 32163, "first_name": "Stephen", "last_name": "Kissler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32162, "first_name": "Daniel B", "last_name": "Larremore", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 172, "ror": "", "name": "University of Colorado at Boulder", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Diagnostic tests play a crucial role in the management of infectious disease transmission. Testing is the fastest most reliable way to inform a person whether they are infected, and thus whether they should adjust their behavior to prevent onward spread. Testing policies have long contributed to public health, including in the control of HIV, tuberculosis, and malaria. During the COVID-19 pandemic, various test-based policies were successful, including pre-event screening (e.g., testing before entering a sporting event), traveler screening (e.g., testing before boarding a flight), and regular screening (e.g., weekly testing at universities). Such policies could also help control the spread of other existing and novel respiratory pathogens. However, we currently lack a robust, data-driven framework to estimate the potential impact of testing-based infection control strategies in general. To fill this gap, this project will develop a flexible modeling framework to simulate how different testing policies might perform for various pathogens, tests, and human behavioral scenarios. This project will also develop the statistical tools needed to infer how diagnostic test results, infectiousness, and behavior relate to one another, informed by data on SARS-CoV-2 and other respiratory pathogens. To maximize the impact of these findings, this project will build mature, open-source software products to compare testing-based policies, accompanied by tutorials for policymakers and a new open-source data hub to consolidate information relevant to testing-based policies. The successful completion of this project will improve our ability to control existing respiratory pathogens and enhance our preparedness for future pandemics. Fundamental to this project is the characterization of how infectiousness, detectability, symptoms, and behaviors change over the course of a respiratory infection – a collection of features called an infection trajectory. While the details of an infection trajectory can be omitted for some types of policy assessments, testing-based policies depend critically on an accurate and statistical understanding of infection trajectories. Infection trajectory-based models allow for the separation of individual-level features of disease transmission from the between-host dynamics, permitting a “plug-and-play” approach to policy design, without compromising the ability to tailor solutions to local needs and populations. This project’s policy modeling framework will develop a stochastic description of infection trajectories, represented by a joint distribution of an infection’s measurable variables. This will allow the researchers to assess variability in policy outcomes and to identify cross-policy interactions. This project will develop a framework to infer infection trajectory distributions from multimodal data and will deploy that framework to guide the design of studies for collecting new infection trajectory data. Finally, this project will create a suite of software, educational, and data tools for informing infection trajectories and associated policies. For the public health policy community, successful completion of this project will produce new, high-quality policy design models and assessment tools, complemented by educational and interactive exploration webpages. For the scientific community, this project will provide statistical tools and data sharing standards for infection trajectory data, supporting advances in virology and modeling. This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD). 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": "15654", "attributes": { "award_id": "2434162", "title": "Equipment: Course-Based Undergraduate Research Experiences: Engaging Historically Underrepresented Students Using Stress Block Image Correlation Simulation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "HSI-Hispanic Serving Instituti" ], "program_reference_codes": [], "program_officials": [ { "id": 2964, "first_name": "Sonja", "last_name": "Montas-Hunter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": null, "award_amount": 185515, "principal_investigator": { "id": 32161, "first_name": "Ariful", "last_name": "Bhuiyan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32158, "first_name": "Jana M", "last_name": "Willis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32159, "first_name": "Magdy", "last_name": "Akladios", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32160, "first_name": "Serkan", "last_name": "Caliskan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 590, "ror": "https://ror.org/01t817z14", "name": "University of Houston - Clear Lake", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Educational Instrumentation (EI) track aims to engage students in course-based undergraduate research experiences (CUREs) in a lab-led freshman physics course (PHYS 2425) along with eight other courses. This approach will provide students with valuable opportunities to conduct various hypothesis-driven research projects related to fatigue loading using the ElectroForce (EF) 3330 equipment and an in-house developed innovative fixture called the Stress Block (SB). Economic shifts, societal changes, alternative career paths, and the lingering effects of COVID-19 have all impacted undergraduate enrollment. With a strong job market for non-degree roles, more high school graduates are considering direct entry into the workforce. However, in today’s competitive job market, the value of higher education remains critical, offering specialized skills and advantages that can elevate quality of life. For many physics and mechanical engineering undergraduates, insufficient high school preparation can create obstacles in problem-solving and understanding complex measurements, often hindering success in rigorous university programs. CUREs will provide essential support by allowing students to apply theoretical concepts to real-world scenarios, reinforcing understanding through hands-on learning. This experience will also enable students to build a supportive network with faculty and peers, contributing to their professional growth. Through active participation, CUREs will foster a sense of ownership and deeper engagement with learning. The SBICS-CUREs project, utilizing ElectroForce (EF) 3330 and Digital Image Correlation (DIC) technology, is designed to enhance these experiences while also contributing to reducing the gender gap in STEM fields. The hypothesis for this project proposes that integrating the ElectroForce (EF) 3330 equipment with a custom-designed Stress Block (SB) fixture in a freshman physics course will significantly enhance students’ understanding and application of hypothesis-driven research. The specific aims are to (1) connect the SB attachment to the ElectroForce (EF) 3330 and (2) apply DIC techniques on samples tested with this setup. The methodology includes six steps: 3D printing samples, speckle deposition for image correlation, setting up a GoPro for reference images, mounting the SB fixture on the ElectroForce (EF) 3330, applying sinusoidal loads, and conducting DIC analysis to assess deformation and strain. This process gives students hands-on testing and simulation experience, bridging theoretical knowledge with real-world applications. Reflective learning is central to this project, utilizing DIC software like Ncorr, a free tool for full-field, non-contact optical measurements of deformation and strain in mechanical components. Findings will be shared through conferences, peer-reviewed publications, and YouTube videos, with plans to connect with industry leaders like Boeing, KBR, and agencies such as NASA and National Science Foundation. Additionally, partnerships with local Independent School Districts (ISDs) will enable high school students to participate, building a recruitment pipeline for UHCL STEM programs. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims. 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": "15654", "attributes": { "award_id": "2434162", "title": "Equipment: Course-Based Undergraduate Research Experiences: Engaging Historically Underrepresented Students Using Stress Block Image Correlation Simulation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "HSI-Hispanic Serving Instituti" ], "program_reference_codes": [], "program_officials": [ { "id": 2964, "first_name": "Sonja", "last_name": "Montas-Hunter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": null, "award_amount": 185515, "principal_investigator": { "id": 32161, "first_name": "Ariful", "last_name": "Bhuiyan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32158, "first_name": "Jana M", "last_name": "Willis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32159, "first_name": "Magdy", "last_name": "Akladios", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32160, "first_name": "Serkan", "last_name": "Caliskan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 590, "ror": "https://ror.org/01t817z14", "name": "University of Houston - Clear Lake", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Educational Instrumentation (EI) track aims to engage students in course-based undergraduate research experiences (CUREs) in a lab-led freshman physics course (PHYS 2425) along with eight other courses. This approach will provide students with valuable opportunities to conduct various hypothesis-driven research projects related to fatigue loading using the ElectroForce (EF) 3330 equipment and an in-house developed innovative fixture called the Stress Block (SB). Economic shifts, societal changes, alternative career paths, and the lingering effects of COVID-19 have all impacted undergraduate enrollment. With a strong job market for non-degree roles, more high school graduates are considering direct entry into the workforce. However, in today’s competitive job market, the value of higher education remains critical, offering specialized skills and advantages that can elevate quality of life. For many physics and mechanical engineering undergraduates, insufficient high school preparation can create obstacles in problem-solving and understanding complex measurements, often hindering success in rigorous university programs. CUREs will provide essential support by allowing students to apply theoretical concepts to real-world scenarios, reinforcing understanding through hands-on learning. This experience will also enable students to build a supportive network with faculty and peers, contributing to their professional growth. Through active participation, CUREs will foster a sense of ownership and deeper engagement with learning. The SBICS-CUREs project, utilizing ElectroForce (EF) 3330 and Digital Image Correlation (DIC) technology, is designed to enhance these experiences while also contributing to reducing the gender gap in STEM fields. The hypothesis for this project proposes that integrating the ElectroForce (EF) 3330 equipment with a custom-designed Stress Block (SB) fixture in a freshman physics course will significantly enhance students’ understanding and application of hypothesis-driven research. The specific aims are to (1) connect the SB attachment to the ElectroForce (EF) 3330 and (2) apply DIC techniques on samples tested with this setup. The methodology includes six steps: 3D printing samples, speckle deposition for image correlation, setting up a GoPro for reference images, mounting the SB fixture on the ElectroForce (EF) 3330, applying sinusoidal loads, and conducting DIC analysis to assess deformation and strain. This process gives students hands-on testing and simulation experience, bridging theoretical knowledge with real-world applications. Reflective learning is central to this project, utilizing DIC software like Ncorr, a free tool for full-field, non-contact optical measurements of deformation and strain in mechanical components. Findings will be shared through conferences, peer-reviewed publications, and YouTube videos, with plans to connect with industry leaders like Boeing, KBR, and agencies such as NASA and National Science Foundation. Additionally, partnerships with local Independent School Districts (ISDs) will enable high school students to participate, building a recruitment pipeline for UHCL STEM programs. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims. 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": "15654", "attributes": { "award_id": "2434162", "title": "Equipment: Course-Based Undergraduate Research Experiences: Engaging Historically Underrepresented Students Using Stress Block Image Correlation Simulation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Unknown", "HSI-Hispanic Serving Instituti" ], "program_reference_codes": [], "program_officials": [ { "id": 2964, "first_name": "Sonja", "last_name": "Montas-Hunter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-01-01", "end_date": null, "award_amount": 185515, "principal_investigator": { "id": 32161, "first_name": "Ariful", "last_name": "Bhuiyan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 32158, "first_name": "Jana M", "last_name": "Willis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32159, "first_name": "Magdy", "last_name": "Akladios", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 32160, "first_name": "Serkan", "last_name": "Caliskan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 590, "ror": "https://ror.org/01t817z14", "name": "University of Houston - Clear Lake", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Educational Instrumentation (EI) track aims to engage students in course-based undergraduate research experiences (CUREs) in a lab-led freshman physics course (PHYS 2425) along with eight other courses. This approach will provide students with valuable opportunities to conduct various hypothesis-driven research projects related to fatigue loading using the ElectroForce (EF) 3330 equipment and an in-house developed innovative fixture called the Stress Block (SB). Economic shifts, societal changes, alternative career paths, and the lingering effects of COVID-19 have all impacted undergraduate enrollment. With a strong job market for non-degree roles, more high school graduates are considering direct entry into the workforce. However, in today’s competitive job market, the value of higher education remains critical, offering specialized skills and advantages that can elevate quality of life. For many physics and mechanical engineering undergraduates, insufficient high school preparation can create obstacles in problem-solving and understanding complex measurements, often hindering success in rigorous university programs. CUREs will provide essential support by allowing students to apply theoretical concepts to real-world scenarios, reinforcing understanding through hands-on learning. This experience will also enable students to build a supportive network with faculty and peers, contributing to their professional growth. Through active participation, CUREs will foster a sense of ownership and deeper engagement with learning. The SBICS-CUREs project, utilizing ElectroForce (EF) 3330 and Digital Image Correlation (DIC) technology, is designed to enhance these experiences while also contributing to reducing the gender gap in STEM fields. The hypothesis for this project proposes that integrating the ElectroForce (EF) 3330 equipment with a custom-designed Stress Block (SB) fixture in a freshman physics course will significantly enhance students’ understanding and application of hypothesis-driven research. The specific aims are to (1) connect the SB attachment to the ElectroForce (EF) 3330 and (2) apply DIC techniques on samples tested with this setup. The methodology includes six steps: 3D printing samples, speckle deposition for image correlation, setting up a GoPro for reference images, mounting the SB fixture on the ElectroForce (EF) 3330, applying sinusoidal loads, and conducting DIC analysis to assess deformation and strain. This process gives students hands-on testing and simulation experience, bridging theoretical knowledge with real-world applications. Reflective learning is central to this project, utilizing DIC software like Ncorr, a free tool for full-field, non-contact optical measurements of deformation and strain in mechanical components. Findings will be shared through conferences, peer-reviewed publications, and YouTube videos, with plans to connect with industry leaders like Boeing, KBR, and agencies such as NASA and National Science Foundation. Additionally, partnerships with local Independent School Districts (ISDs) will enable high school students to participate, building a recruitment pipeline for UHCL STEM programs. The HSI Program aims to enhance undergraduate STEM education and build capacity at HSIs. Projects supported by the HSI Program will also generate new knowledge on how to achieve these aims. 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": 1, "pages": 1392, "count": 13920 } } }