Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1385&sort=program_officials
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=program_officials", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1424&sort=program_officials", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1386&sort=program_officials", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1384&sort=program_officials" }, "data": [ { "type": "Grant", "id": "15267", "attributes": { "award_id": "1R01MD019506-01", "title": "Mobile Health for Migrant Health (mHealth-4-Mhealth): Assessing the Effectiveness of Implementing an mHealth Program to Increase COVID-19 Testing and Treatment Among Rural Migrant Families", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Minority Health and Health Disparities (NIMHD)" ], "program_reference_codes": [], "program_officials": [ { "id": 31480, "first_name": "Lynne Slaughter", "last_name": "Padgett", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-15", "end_date": "2028-02-28", "award_amount": 614873, "principal_investigator": { "id": 12381, "first_name": "Russell James", "last_name": "McCulloh", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 628, "ror": "https://ror.org/00thqtb16", "name": "University of Nebraska Medical Center", "address": "", "city": "", "state": "NE", "zip": "", "country": "United States", "approved": true }, "abstract": "Migrant agricultural workers and their families face formidable challenges in accessing COVID-19 testing, treatment, and preventive measures due to geographic, cultural, linguistic, and social isolation from community institutions and resources. Our research team has partnered with migrant families in rural Nebraska to improve access to COVID-19 testing, healthcare, and community response resources by implementing the Mobile Health for Migrant Health (mHealth-4-Mhealth) program. This program collaborates with state and local health departments and the Title IC Nebraska Migrant Education Program and seeks to mitigate direct and indirect negative effects of the pandemic by providing mHealth-guided decision support for at-home COVID-19 antigen testing and infection management coupled with mHealth-assisted social determinants of health (SDOH) screening and response protocols to connect families to community resources and aid. Our experience to date highlights the feasibility and acceptability of mHealth-based public health interventions to engage marginalized communities in COVID-19 responses and facilitate access to community and health systems resources. The research objective of this study is to evaluate the impact of mHealth-4-Mhealth program implementation on migrant families’ utilization of COVID-19 testing, treatment, and related healthcare resources by achieving three specific aims. Aim #1: Determine the effectiveness of self-directed decision support via an mHealth tool and human-assisted systems navigation for engaging rural migrant families in COVID-19 (1a) testing and (1b) treatment. Aim #2: Identify SDOH and other implementation factors at participant, household, and community levels associated with program adoption among rural migrant families. Aim #3: Determine the cost- effectiveness of implementing mHealth and systems navigation to increase COVID-19 (3a) testing and (3b) treatment among rural migrant families. We will conduct a prospective, cluster-randomized clinical trial among Nebraska rural migrant families. Households will be randomized to one of three study arms to first receive free at-home antigen test kits for varying periods of baseline observation followed by addition of our mHealth tool and then the tool combined with human-assisted systems navigation. A fourth study arm will consist of existing mHealth-4-mHealth families who already receive all program components. Primary outcomes will be COVID-19 test and antiviral therapy utilization. Secondary outcomes will include COVID-19 vaccination and unplanned healthcare utilization. We will analyze these outcomes across intervention states. For Aim 2 we will assess the association of SDOH and other implementation factors that may contribute to rural migrant families’ use (adoption) of program interventions. For Aim 3 we will determine the incremental cost-effectiveness of program implementation using the mHealth tool with or without systems navigation. Results will provide evidentiary support to scale the intervention to migrant-serving programs nationally, inform health policy development, and drive future programs aimed at improving community resilience in public health emergencies.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14839", "attributes": { "award_id": "2409868", "title": "On Iteratively Regularized Alternating Minimization under Nonlinear Dynamics Constraints with Applications to Epidemiology", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 31483, "first_name": "Troy D.", "last_name": "Butler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 200000, "principal_investigator": { "id": 7209, "first_name": "Alexandra", "last_name": "Smirnova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 300, "ror": "", "name": "Georgia State University Research Foundation, Inc.", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 10457, "first_name": "Xiaojing", "last_name": "Ye", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 300, "ror": "", "name": "Georgia State University Research Foundation, Inc.", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "How widely has the virus spread? This important and often overlooked question was brought to light by the recent COVID-19 outbreak. Several techniques have been used to account for silent spreaders along with varying testing and healthcare seeking habits as the main reasons for under-reporting of incidence cases. It has been observed that silent spreaders play a more significant role in disease progression than previously understood, highlighting the need for policymakers to incorporate these hidden figures into their strategic responses. Unlike other disease parameters, i.e., incubation and recovery rates, the case reporting rate and the time-dependent effective reproduction number are directly influenced by a large number of factors making it impossible to directly quantify these parameters in any meaningful way. This project will advance iteratively regularized numerical algorithms, which have emerged as a powerful tool for reliable estimation (from noise-contaminated data) of infectious disease parameters that are crucial for future projections, prevention, and control. Apart from epidemiology, the project will benefit all real-world applications involving massive amounts of observation data for multiple stages of the inversion process with a shared model parameter. In the course of their theoretical and numerical studies, the PIs will continue to create research opportunities for undergraduate and graduate students, including women and students from groups traditionally underrepresented in STEM disciplines. A number of project topics are particularly suitable for student research and will be used to train some of the next generation of computational mathematicians.<br/><br/>In the framework of this project, the PIs will develop new regularized alternating minimization algorithms for solving ill-posed parameter-estimation problems constrained by nonlinear dynamics. While significant computational challenges are shared by both deterministic trust-region and Bayesian methods (such as numerical solutions requiring solutions to possibly complex ODE or PDE systems at every step of the iterative process), the team will address these challenges by constructing a family of fast and stable iteratively regularized optimization algorithms, which carefully alternate between updating model parameters and state variables.<br/><br/>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": "14846", "attributes": { "award_id": "2409903", "title": "Development of novel numerical methods for forward and inverse problems in mean field games", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "COMPUTATIONAL MATHEMATICS" ], "program_reference_codes": [], "program_officials": [ { "id": 31483, "first_name": "Troy D.", "last_name": "Butler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 298862, "principal_investigator": { "id": 31525, "first_name": "Yat Tin", "last_name": "Chow", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 153, "ror": "", "name": "University of California-Riverside", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Mean field games is the study of strategic decision making in large populations where individual players interact through a certain quantity in the mean field. Mean field games have strong descriptive power in socioeconomics and biology, e.g. in the understanding of social cooperation, stock markets, trading and economics, biological systems, election dynamics, population games, robotic control, machine learning, dynamics of multiple populations, pandemic modeling and control as well as vaccination distribution. It is therefore essential to develop accurate numerical methods for large-scale mean field games and their model recovery. However, current computational approaches for the recovery problem are impractical in high dimensions. This project will comprehensively study new computational methods for both large-scale mean field games and their model recovery. The comprehensive plans will cover algorithmic development, theoretical analysis, numerical implementation and practical applications. The project will also involve research on speeding up the forward and inverse problem computations to speed up the computation for mean field game modeling and turn real life mean field game model recovery problems from computationally unaffordable to affordable. The research team will disseminate results through publications, professional presentations, the training of graduate students at the University of California, Riverside as well as through public outreach events that involve public talks and engagement with high school math fairs. The goals of these outreach events are to increase public literacy and public engagement in mathematics, improve STEM education and educator development, and broaden participation of women and underrepresented minorities.<br/><br/>The project will provide novel computational methods for both forward and inverse problems of mean field games. The team will (1) develop two new numerical methods for forward problems in mean field games, namely monotone inclusion with Benamou-Brenier's formulation and extragradient algorithm with moving anchoring; (2) develop three new numerical methods for inverse problems in mean field games with only boundary measurements, namely a three-operator splitting scheme, a semi-smooth Newton acceleration method, and a direct sampling method. Both theoretical analysis and practical implementations will be emphasized. In particular, numerical methods for inverse problems for mean field games, which is a main target of the project, will be designed to work with only boundary measurements. This represents a brand new field in inverse problems and optimization. The project will also seek the simultaneous reconstruction of coefficients in the severely ill-posed case when only noisy boundary measurements from one or two measurement events are available.<br/><br/>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": "15150", "attributes": { "award_id": "2408877", "title": "Structure Preserving Optimization Algorithms and Digital Twins", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "OFFICE OF MULTIDISCIPLINARY AC" ], "program_reference_codes": [], "program_officials": [ { "id": 31483, "first_name": "Troy D.", "last_name": "Butler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-01", "end_date": null, "award_amount": 275000, "principal_investigator": { "id": 10620, "first_name": "Harbir", "last_name": "Antil", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 239, "ror": "https://ror.org/02jqj7156", "name": "George Mason University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 239, "ror": "https://ror.org/02jqj7156", "name": "George Mason University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "Optimization problems constrained by physics are ubiquitous. These problems are nonlinear, nonsmooth and contain unknown parameters. The physics describing the constraints are partial differential equations (PDEs) which are multiscale, multiphysics, and geometric in nature. They capture many realistic scenarios: control of pathogen propagation like COVID-19, blood flow in aneurysms, determining weakness in structures, and vortex control in nuclear reactors. This project will study optimization problems constrained by PDEs that can incorporate data to make decisions that are resilient to uncertainty. The proposed methods will provide new insights into nonsmooth nonconvex optimization, and they will be applied to applications such as identifying weakness in structures (e.g., bridges and nuclear plants). The results of this research will be shared with the community via publications and research talks. The outcomes of this research will benefit scientists working in multiple research areas such as numerical analysis, optimization, structural engineering and bioengineering. A PhD student will be fully supported by the project. <br/> <br/>Particular focus of the project is on risk-averse optimization problems where the PDEs contain uncertainty arising from modeling unknown quantities (coefficients, boundary conditions, etc.) as random variables and dynamic optimization problems. The project will develop: (i) Inexact adaptive Semismooth Newton and Trust-region methods to solve these optimization problems; (ii) Primal dual methods for risk-averse optimization problems with general constraints; (iii) Applications to problems where inexactness arise from finite element discretization. Thrusts (i) and (iii) will enable interaction between finite element discretization and optimization solvers leading to structure preserving algorithms. Additionally, Thrust (ii) will lead to different penalty parameters for different constraints and will allow inexact solves at each iteration which is essential for large systems. This will enable a new paradigm for widely used penalty-based methods. Algorithms for high-dimensional nonsmooth risk-averse optimization will help overcome curse of dimensionality for similar problems.<br/><br/>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": "14815", "attributes": { "award_id": "1R44NS137943-01", "title": "Manufacturing RNA-based CAR T cells to combat autoantibody-associated autoimmune disorders (AAAD)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Neurological Disorders and Stroke (NINDS)" ], "program_reference_codes": [], "program_officials": [ { "id": 31493, "first_name": "FLOY ANNETTE", "last_name": "Gilchrist", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-07", "end_date": "2026-05-31", "award_amount": 1496118, "principal_investigator": { "id": 31494, "first_name": "Christopher M", "last_name": "Jewell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1703, "ror": "", "name": "CARTESIAN THERAPEUTICS, INC.", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "Autoantibody-associated autoimmune diseases (AAADs) are disorders in which dysfunctional plasma cells (PCs) secrete autoantibodies that attack healthy tissue. Many AAADs attack the nervous system, such as myasthenia gravis (MG), neuromyelitis optica, chronic inflammatory demyelinating polyneuropathy, Lambert- Eaton syndrome, and necrotizing autoimmune myopathy. This R44 (Clinical Trial Not Allowed) will support manufacturing of a new RNA-based cell therapy for neurological AAADs, and analysis of biomarkers from patients who received the therapy. Chimeric antigen receptor T cell (CAR-T) therapy eliminates pathogenic cells, such as PCs, by expressing engineered chimeric antigen receptors (CAR) in T cells isolated from a patient; the T cells are then reinfused to kill the CAR’s target. All approved CAR T drugs rely on gene transfer by DNA, which permanently modifies the genome. This poses several problems. DNA-modified CAR-T cells multiply exponentially because CAR DNA replicates; this can cause life-threatening inflammation called cytokine release syndrome (CRS). These CAR-T cells also require pre-treatment chemotherapy to create a niche for cell proliferation. DNA-modified CAR-T cells are expensive to make and use clinically, because frequent adverse events require close patient monitoring. These hurdles have limited CAR-T therapy only to advanced cancers. Cartesian Therapeutics has designed a new approach that replaces DNA with RNA to achieve transient, tunable CAR expression (rCAR-T). Unlike DNA-modified CAR T cells, CAR-encoding RNA cannot replicate and decays in predictable fashion. Thus, the patient’s exposure to rCAR-T cells is determined by cell dose. This avoids CRS and eliminates lymphodepleting chemotherapy. These advances create a versatile, clinically-validated means to treat neurological AAADs. We have developed an rCAR-T that targets B cell maturation antigen (BCMA). This marker is expressed on PCs – cells that produce autoantibodies in AAADs. This therapy – “Descartes-08” – transiently expresses anti-BCMA CAR to target BCMA+ PCs after infusion. MG is an AAAD in which BCMA+ PCs secrete autoantibodies that attack the neuromuscular junction, causing severe muscle weakness. In the first successful Phase 2a trial of a cell therapy to treat autoimmunity (NCT04146051), Descartes-08 conferred potent, safe, and long-lasting improvement in MG patients for 12 months or more, even though treatment was only 6-weeks (Lancet Neurology 2023). These benefits were achieved without the toxicity, inpatient stays, or lymphodepletion needed in DNA-based CAR-Ts. Here we propose key steps to develop rCAR-T as a new cell therapy option to treat neurological AAADs. The aims are i) manufacture Descartes-08 product lots to complete the Phase 2 MG clinical trial, ii) Identify clinical correlates in patient samples from ongoing randomized placebo-controlled trial (RCT) and iii) manufacture Descartes-08 lots for expanded access and basket trial patients with other neurological AAADs. Completing these aims will position Descartes-08 to become the first commercial rCAR-T product for a variety of AAADs.", "keywords": [ "Address", "Advanced Malignant Neoplasm", "Adverse event", "Antigens", "Autoantibodies", "Autoimmune", "Autoimmune Diseases", "Autoimmunity", "Autologous", "Automobile Driving", "B-Lymphocytes", "Biological Markers", "Blood specimen", "CAR T cell therapy", "CD8-Positive T-Lymphocytes", "Cell Maturation", "Cell Proliferation", "Cell Separation", "Cell Therapy", "Cell secretion", "Cells", "Cholinergic Receptors", "Chronic", "Chronic Inflammatory Demyelinating Polyneuropathy", "Clinical", "Clinical Trials", "Complement", "Complement Inactivators", "DNA", "DNA biosynthesis", "Data", "Disease", "Dose", "Engineering", "Enrollment", "Exposure to", "Frequencies", "Funding", "Gene Transfer", "Genome", "Goals", "IL27RA gene", "Immune", "Immune system", "Immunosuppressive Agents", "Inflammation", "Inflammatory", "Infusion procedures", "Inpatients", "Lambert-Eaton Myasthenic Syndrome", "Life", "Longterm Follow-up", "Lupus", "Malignant Neoplasms", "Measures", "Messenger RNA", "Monitor", "Monoclonal Antibodies", "Muscle Weakness", "Myasthenia Gravis", "Myopathy", "National Institute of Neurological Disorders and Stroke", "Nervous System", "Neurologic", "Neurology", "Neuromuscular Junction", "Neuromyelitis Optica", "Outcome", "Outpatients", "Pathogenicity", "Patient Monitoring", "Patients", "Pharmaceutical Preparations", "Phase", "Phase II Clinical Trials", "Phenotype", "Placebo Control", "Placebos", "Plasma Cells", "Positioning Attribute", "Process", "Proliferating", "RNA", "Receptor Signaling", "Regulatory T-Lymphocyte", "Relapse", "Safety", "Sampling", "Serum", "Small Business Innovation Research Grant", "T cell clonality", "T-Lymphocyte", "T-cell receptor repertoire", "Technology", "Testing", "Therapeutic", "Tissues", "Toxic effect", "Transfection", "Work", "chemotherapy", "chimeric antigen receptor", "chimeric antigen receptor T cells", "clinical effect", "combat", "commercialization", "cost", "cytokine", "cytokine release syndrome", "design", "engineered T cells", "experience", "fighting", "immune modulating agents", "improved", "manufacture", "neonatal Fc receptor", "new technology", "novel strategies", "participant enrollment", "patient biomarkers", "phase II trial", "phase III trial", "preconditioning", "randomized placebo controlled trial", "receptor expression", "response", "standard of care", "symptom treatment" ], "approved": true } }, { "type": "Grant", "id": "14828", "attributes": { "award_id": "1K23DK139454-01", "title": "Structural Racism as a \"Third hit\" on kidney outcomes of Black individuals with APOL1 risk alleles", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)" ], "program_reference_codes": [], "program_officials": [ { "id": 31506, "first_name": "RAQUEL CHARLES", "last_name": "Greer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-05-23", "end_date": "2029-01-31", "award_amount": 194940, "principal_investigator": { "id": 31507, "first_name": "Dinushika", "last_name": "Mohottige", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 625, "ror": "https://ror.org/04a9tmd77", "name": "Icahn School of Medicine at Mount Sinai", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Black individuals have for decades been disproportionately impacted by kidney failure and rapid progression of kidney disease when compared to their White counterparts. Black individuals with APOL1 high-risk alleles are particularly vulnerable to accelerated chronic kidney disease (CKD) progression and kidney failure. However, these high-risk genotypes only occur in about 12-14% of Black individuals, and they do not guarantee CKD progression or kidney failure. Other risk factors, such as hypertension, HIV, and COVID-19, are thought to confer additional second-hit risks. Structural racism (SR)—defined as discriminatory policies and practices promoted through reinforcing systems (e.g., housing, wealth, health care) –– is also widely understood to be a contributor to racial disparities in kidney health. I hypothesize that SR acts as a “third hit” which contributes to excess risk of adverse kidney health outcomes among Black individuals with APOL1 risk alleles. Through four complementary aims, I will characterize the effects of structural racism on kidney health among Black individuals with high-risk APOL1 alleles and design and test a patient-centered intervention to mitigate effects of SR on health outcomes. In Aim 1, I will engage a multidisciplinary stakeholder board to collaborate in the analysis and interpretation of mixed-methods studies in Aims 2 and 3, and in the design and evaluation of a patient-centered pilot intervention in Aim 4. In Aim 2, I will quantify the longitudinal effects of SR with poor kidney health leveraging 3 large APOL1-enriched cohort studies. In Aim 3, I will characterize the experiences of structural racism of Black patients with APOL1 who have CKD and their clinicians with SR in health settings and their communities using qualitative analyses (photovoice, focus groups, semi-structured interviews). In Aim 4, in collaboration with the stakeholder board, I will pilot a patient-centered, navigator-led intervention designed to mitigate the effects of structural racism on kidney health. Throughout the award period, I will pursue training in advanced epidemiologic and statistical science, including longitudinal analysis and multilevel modeling, and develop skills in patient-centered clinical trial design and execution. Training goals and research aims are aligned and integrated to support a holistic experience. The robust training and world-class mentorship supported by this award, and Mount Sinai's enriched training environment and extensive resources, will prepare me for a career as an independent investigator dedicated to mitigating the devastating impact of structural racism on kidney health and eliminating kidney health disparities.", "keywords": [ "APOL1 gene", "Acceleration", "Address", "Advocate", "Albuminuria", "Alleles", "Award", "Black Populations", "Black race", "COVID-19", "Caregivers", "Caring", "Chronic Kidney Failure", "Clinical", "Clinical Trials Design", "Cohort Studies", "Collaborations", "Communities", "Complex", "Cross-Sectional Studies", "Dedications", "Development", "Diabetes Mellitus", "Dimensions", "Disease Progression", "End stage renal failure", "Environment", "Epidemiology", "Equity", "Evaluation", "Focus Groups", "Foundations", "Funding", "Future", "Goals", "HIV", "Harm Reduction", "Health", "Healthcare", "Heart", "Housing", "Hypertension", "Individual", "Intervention", "Interview", "Kidney", "Kidney Diseases", "Kidney Failure", "Legal", "Link", "Lived experience", "Longitudinal Studies", "Measures", "Mentors", "Mentorship", "Methods", "Outcome", "Participant", "Patient Outcomes Assessments", "Patients", "Penetrance", "Persons", "Pharmacy facility", "Policies", "Pollution", "Poverty", "Prevalence", "Race", "Research", "Research Personnel", "Resources", "Risk", "Risk Factors", "Science", "Social Environment", "Structural Racism", "Structure", "System", "Techniques", "Testing", "Training", "Waiting Lists", "black patient", "career", "clinical risk", "cohort", "comorbidity", "design", "disorder risk", "experience", "feasibility testing", "health disparity", "high risk", "implementation intervention", "improved", "intervention mapping", "longitudinal analysis", "multidisciplinary", "multilevel analysis", "patient engagement", "patient oriented", "pilot test", "primary care clinician", "racial bias", "racial disparity", "recruit", "risk variant", "skills", "social", "therapy design" ], "approved": true } }, { "type": "Grant", "id": "14843", "attributes": { "award_id": "2334581", "title": "BRC-BIO: Development of two biochemical tools to study Potato virus Y infection in plants", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "NFE-New Faculty Enhancement" ], "program_reference_codes": [], "program_officials": [ { "id": 31520, "first_name": "David J.", "last_name": "Klinke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": null, "award_amount": 498983, "principal_investigator": { "id": 31521, "first_name": "Erin", "last_name": "Weber", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2501, "ror": "https://ror.org/00wkay776", "name": "Carthage College", "address": "", "city": "", "state": "WI", "zip": "", "country": "United States", "approved": true }, "abstract": "The impact of pathogens, specifically viruses, on living systems, has never been more greatly appreciated than during the age of COVID-19. There is great public awareness of the importance of understanding how viruses infect animal hosts and spread throughout a community. However, host-pathogen interactions in plant systems are less well understood, despite the considerable impact of plants on our lives. This project focuses on Potato virus Y (PVY) that can cause an 80% loss in the yield of potatoes (Solanum tuberosum), the fourth-most important crop worldwide. To improve understanding of how this virus circumvents host immunity and spreads through the host, the PI proposes to develop easy-to-use biochemical tools to detect and modify this plant pathogen and to better understand the molecular drivers of viral infectivity. Detecting viral spread will enable identifying early outbreaks of this plant disease to mitigate the impact on the associated agricultural industry. Understanding the molecular drivers of viral infectivity will aid in developing resistant commercial cultivars. The project also provides a framework for introducing undergraduates to viral biology using model organisms and to classic biochemistry techniques in the context of experiential learning. <br/><br/>Building upon the PI’s prior work with plant models and investigating host-viral interaction, this project aims to develop and validate two biochemical tools to address two major hurdles to studying how PVY establishes infection in plant hosts: the inability to track the virus as it spreads through the host and the ability to identify the contributions of specific viral sequences to circumventing host immunity. The first tool, a synthetic PVY viral clone, will facilitate the targeted design of chimeric viruses, enabling the identification of the genetic determinants of viral infection. The second tool, an enzymatic reporter probe, will enable viral detection earlier in infection. Together, these tools will enable investigating the virus-host interactions PVY uses to hijack the host and spread throughout the plant. Understanding the protein- protein interactions that enable PVY to establish infection is essential to developing resistant cultivars. To build research capacity, the project will engage students in cross-disciplinary research as part of laboratory-based and course-based activities.<br/><br/>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": "14850", "attributes": { "award_id": "2345178", "title": "Partnering to Recruit, Engage, Prepare, and Support New STEM Teachers for North Dallas Area Schools", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "Robert Noyce Scholarship Pgm" ], "program_reference_codes": [], "program_officials": [ { "id": 31530, "first_name": "Patrice", "last_name": "Waller", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-01", "end_date": null, "award_amount": 1199934, "principal_investigator": { "id": 16099, "first_name": "Mary", "last_name": "Urquhart", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 16094, "first_name": "John", "last_name": "Zweck", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 16097, "first_name": "Kate", "last_name": "York", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31531, "first_name": "Katherine", "last_name": "Donaldson", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 199, "ror": "", "name": "University of Texas at Dallas", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "This project aims to respond to the national need of preparing and retaining high-quality teachers. Teacher shortages in science, technology, engineering, and mathematics (STEM) are becoming increasingly dire in the aftermath of the Covid-19 pandemic and its impact on attitudes regarding teaching. New strategies are needed to rise to the challenges of recruiting, preparing, and retaining K–12 teachers. This program seeks to partner to recruit, engage, prepare, and support (PREPS) new teachers in the north Dallas area to increase the number of well-prepared science and mathematics teachers and retain them in the teaching profession. The PREPS project intends to investigate and disseminate effective strategies for recruitment of STEM majors into the teaching profession. PREPS also intends to investigate and disseminate effective strategies for support and retention of these newly prepared STEM teachers. <br/><br/>This project at the University of Texas at Dallas (UT Dallas) School of Natural Sciences and Mathematics includes partnerships with at least three high-need independent school districts (ISDs) in the north Dallas area of the Dallas-Fort Worth Metroplex (DFW): Garland ISD, Mesquite ISD, and Richardson ISD. The PREPS project is part of the UTeach Dallas STEM teacher certification preparation program. Project goals include strengthening collaborations with partner ISDs for 1) quality field experiences for preservice teachers, 2) effective induction support for new teachers, 3) investigation of new pathways for recruitment into and/or completion of teacher preparation, and 4) identification of barriers to new teacher retention and strategies to address these barriers. At the university, project goals include 1) targeted recruitment into teacher preparation of STEM majors in critical teacher shortages in mathematics and the sciences, 2) identification and dissemination of effective post-pandemic messaging for teacher recruitment from a pool of undergraduate STEM majors, 3) investigation of a potential partnership with the UT Dallas School of Science and Engineering for recruitment into the teaching profession of STEM majors in additional critical shortage areas such as computer science, and 4) systematic use of data for continuous improvement. UTeach Dallas PREPS also intends to explore direct recruitment of students in local, diverse, high-needs high schools into mathematics and science majors at UT Dallas and into UTeach Dallas. The project plans to disseminate findings to multiple university-based teacher preparation national and statewide networks. UTeach Dallas PREPS intends to provide up to 54 scholarships and 40 internships, with recipients anticipated to directly impact STEM learning for up to 27,000 students in their first five years of teaching in the Dallas-Fort Worth Metroplex. Participants are expected to positively impact K–12 students through internships and in their field experiences beginning as early as their first university semester. This Track 1: Scholarships and Stipends project is supported through the Robert Noyce Teacher Scholarship Program (Noyce). The Noyce program supports talented STEM undergraduate majors and professionals to become effective K-12 STEM teachers and experienced, exemplary K-12 teachers to become STEM master teachers in high-need school districts. It also supports research on the effectiveness and retention of K-12 STEM teachers in high-need school districts.<br/><br/>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": "14851", "attributes": { "award_id": "2341932", "title": "HNDS-I: Developing a Large-Scale Data Platform for Processing Algorithms for Epidemic Modeling", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Human Networks & Data Sci Infr" ], "program_reference_codes": [], "program_officials": [ { "id": 31532, "first_name": "May", "last_name": "Yuan", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-06-01", "end_date": null, "award_amount": 998028, "principal_investigator": { "id": 31534, "first_name": "Duncan", "last_name": "Watts", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 1043, "first_name": "VICTOR M", "last_name": "PRECIADO", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true } ] }, { "id": 31533, "first_name": "Hamed", "last_name": "Hassani", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 232, "ror": "https://ror.org/00b30xv10", "name": "University of Pennsylvania", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project develops a data platform that collects data and tools for analysis that can be used to study, prevent and respond to pandemics. The name of this platform is the Data Access Platform for Human Mobility in Epidemiology (DAPHME). The DAPHME platform contains global positioning satellite (GPS) datasets that are privatized, so that data can be made available to researchers and policy-makers at minimal cost and without the need for extensive computing resources. Because more people will have access to the data and to repositories of tools for processing and analyzing the data, more people will be able to contribute to the understanding of the spread and prevention of disease. DAPHME promises to significantly improve the nation's capacity to model and mitigate the effects of future pandemics and is a strategic investment in public health infrastructure.<br/><br/>The DAPHME project improves access to mobility data through several coordinated actions. First, it creates and arranges data sharing agreements with various location data providers for platform users. Second, it reduces data access costs by providing analytical tools and servers, along with a subscription model that helps keep the platform financially self-sustaining. Third, it develops methods and mobility metrics specifically for epidemiological research to make analyses more efficient. Finally, it builds a community of researchers through networking to enhance their research projects and encourages the widespread development of new code and methods. The project will positively impact the replication of research results, the validation and broader use of data, and the effectiveness of tools designed to combat public health emergencies.<br/><br/>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": "14856", "attributes": { "award_id": "1R37CA285794-01A1", "title": "Targeting tumoral Lactobacillus iners to improve outcomes in cervical cancers", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Cancer Institute (NCI)" ], "program_reference_codes": [], "program_officials": [ { "id": 31541, "first_name": "Avraham", "last_name": "Rasooly", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-07-01", "end_date": "2029-06-30", "award_amount": 660742, "principal_investigator": { "id": 31542, "first_name": "Lauren Elizabeth", "last_name": "Colbert", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1420, "ror": "", "name": "UNIVERSITY OF TX MD ANDERSON CAN CTR", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "Cervical cancer remains the second most common cancer killer of women worldwide, with an annual incidence of more than 600,000 and an annual death rate of 300,000. Further, cervical cancer disproportionately affects communities of medically underserved and minority women within the US and abroad, and improved therapies are urgently needed. Primary and secondary prevention approaches are also variably effective – even prior to the COVID-19 pandemic, just 1 in 8 girls was vaccinated against human papillomavirus (HPV), the cause of 90% of cervical cancers. Vaccination rates also dropped sharply during the pandemic, even in the United States. Current World Health Organization (WHO) estimates of HPV vaccine uptake rates is 21%. The vast majority of cervical cancers that are diagnosed after primary and secondary prevention fail are locally advanced cervical cancer (LACC). Approximately 40% of women diagnosed with LACC will relapse and die of disease even with standard-of-care curative treatment, cisplatin-based chemoradiotherapy (CRT). CRT has remained the standard-of-care for more than two decades, and novel approaches have failed to improve outcomes. We have identified a critical prognostic factor, a bacteria called Lactobacillus iners (L. iners), in the cervical tumor microbiome, which rewires tumor metabolism to utilize lactate and is associated with treatment resistance and poor survival. Further, commensal Lactobacilli (lactic acid bacteria) in other tumor sites often driven by lactate, such as head and neck and lung cancers, also appear to lead to treatment resistance and poor survival. Our objective is to understand specifically how L. iners and other lactic acid bacteria influence cancer cell and immune cell metabolism using state-of-the-art proximity proteomics and mass cytometry (Aim 1). We will also test novel therapeutic approaches to target either tumor resident bacteria by eliminating or replacing specific bacterial species (Aim 2), or metabolic effects of tumor resident bacteria via local bacterial engineering or systemic metabolism targeting anti-cancer therapies (Aim 3). Targeting cervical tumor bacteria as a therapeutic (“Bugs as Drugs”) is a paradigm-shifting idea, capitalizing on the relative simplicity of the cervicovaginal microbiome and its tendency to be dominated by Lactobacillus species, and not only will this study lead to improved microbiome- based therapeutics to improve outcomes in cervical cancer, but this proof-of-concept model could be used to inform tumor microbiome-based therapeutics across cancer types.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1385, "pages": 1424, "count": 14236 } } }