Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=4&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=1424&sort=other_investigators", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=5&sort=other_investigators", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=3&sort=other_investigators" }, "data": [ { "type": "Grant", "id": "15223", "attributes": { "award_id": "1R01AG087296-01", "title": "Alzheimer's Special Care Units in Nursing Homes: Racial and Ethnic Disparities, Resident Outcomes, and State Policies", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Aging (NIA)" ], "program_reference_codes": [], "program_officials": [ { "id": 27518, "first_name": "THERESA YOUNGJOO", "last_name": "Kim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": "2028-05-31", "award_amount": 424184, "principal_investigator": { "id": 27405, "first_name": "Huiwen", "last_name": "Xu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 265, "ror": "https://ror.org/03czfpz43", "name": "Emory University", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true }, "abstract": "Alzheimer's special care units (SCUs) are a promising care model for nursing home residents with Alzheimer's Disease & Related Dementias (ADRD). SCUs provide higher quality care and improve outcomes for residents with ADRD. Our preliminary analysis further found that, in facilities with an SCU, the disparities in 3-month hospitalization rates and pressure ulcers between Hispanic and White residents were eliminated or greatly reduced. Despite the benefits of SCUs, racial and ethnic minority residents are less likely to access SCUs than White residents, suggesting that lack of SCU access may be a mechanistic pathway responsible for disparities in outcomes. Currently, SCUs are available in only 14% of nursing homes and access varies substantially across states. State Medicaid policies and SCU regulations can incentivize or disincentivize nursing homes to develop SCUs. By analyzing national Medicare claims and resident assessment data, as well as unique Ohio surveys of SCUs and resident and family satisfaction with care, we propose to understand the extent to which racial and ethnic differences in SCU access contribute to disparities in outcomes, and the associations of current state policies and regulations with SCU availability. The specific aims are: Aim 1) To examine disparities in access to Alzheimer's SCUs among Black and Hispanic residents with ADRD; Aim 2) To understand SCU access as a pathway to disparities in health outcomes among Black and Hispanic residents with ADRD; and Aim 3) To investigate which state policies are associated with increased availability of SCUs. The primary analyses will study the 819,415 newly-admitted long-stay residents with ADRD in 15,305 nursing homes from 2011 to 2019. The decomposition method will uncover factors that explain disparities in SCU access among Black and Hispanic residents, and mediation analyses will assess how differences in SCU access contribute to racial and ethnic disparities in health outcomes. Dominance analyses will evaluate the contribution of specific SCU characteristics (physical environment, staffing, and physician involvement) to health outcomes and resident and family satisfaction, as well as reduced racial and ethnic disparities. We will also analyze 2020- 2024 data to examine whether our findings hold during and after the COVID-19 pandemic. Hierarchical Generalized Linear Mixed Models and Difference-in-Differences method will explore which state policies (e.g., supplementary payments for SCU care, Medicaid payment-to-cost ratios, regulations about staffing or training) are associated with SCU availability. Understanding the role of SCU access in racial and ethnic disparities in ADRD-related outcomes can inform policymakers as they seek to mitigate disparities in nursing home care.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15169", "attributes": { "award_id": "1R01HS029862-01A1", "title": "Effects of COVID-19 Related Medicaid Policy Changes in the Marshallese COFA Migrant Population", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Agency for Healthcare Research and Quality (AHRQ)" ], "program_reference_codes": [], "program_officials": [ { "id": 24040, "first_name": "Fred", "last_name": "Hellinger", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": "2028-06-30", "award_amount": 372996, "principal_investigator": { "id": 31753, "first_name": "Jennifer Audrey", "last_name": "Andersen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 772, "ror": "", "name": "UNIV OF ARKANSAS FOR MED SCIS", "address": "", "city": "", "state": "AR", "zip": "", "country": "United States", "approved": true }, "abstract": "Access to healthcare is a persistent public policy concern, particularly for Marshallese Compact of Free Association (COFA) migrants in the United States. This research addresses the impact of Medicaid policy changes, prompted by the COVID-19 pandemic, on healthcare access for Marshallese COFA migrants residing in Northwest Arkansas, where the largest settlement of this population (~15,000) exists. Despite their eligibility for Medicaid under the 1986 RMI-US COFA agreement, subsequent legislative changes, notably the 1996 Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA), resulted in a significant portion (approximately 50%) of the Marshallese population being devoid of healthcare coverage. Even after the enactment of the Affordable Care Act and Medicaid expansion in 2014, which did not reinstate Medicaid coverage for COFA migrants, these disparities persisted. The Consolidated Appropriations Act of December 2020 reinstated Medicaid access after a 25-year gap. However, the effectiveness of this policy change in ensuring enrollment and optimizing healthcare service utilization remains unknown. The overall objective of this study is to determine the effect of Medicaid policy changes enacted in response to the COVID-19 pandemic for Marshallese COFA migrants. We will leverage our long-standing community-engaged relationship with the Marshallese community in Arkansas to collect primary data to generate important data on the barriers and facilitators to Medicaid enrollment for Marshallese COFA migrants, and to inform effective community-based interventions. Our Specific Aims are: Aim 1: Examine the Medicaid enrollment process and identify barriers and facilitators to healthcare for Marshallese newly eligible under Medicaid policy changes. We will conduct four focus groups with 50 Marshallese to qualitatively explore barriers and facilitators to Medicaid enrollment and accessing healthcare services. Aim 2: Conduct a needs assessment to assess barriers and facilitators to inform community-based interventions to improve Medicaid enrollment and use of primary and preventative healthcare services. We will develop and administer a survey to 750 Marshallese to assess the need for community-based interventions to increase enrollment and the use of healthcare services. The survey will focus on barriers and facilitators to Medicaid enrollment and primary/preventative healthcare utilization, and use the themes that emerge in Aim 1 to direct the selection of additional existing validated survey measures. The study's findings will contribute essential information for the development of community-based interventions tailored to enhance Medicaid enrollment and healthcare service utilization among COFA migrants and other underserved populations. The established rapport with the Marshallese community uniquely positions us to implement and evaluate these interventions, fostering equitable healthcare delivery.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15176", "attributes": { "award_id": "1K01AI182501-01", "title": "Applying a Targeted Machine Learning and Causal Inference Approach to Analyzing Long-Term Sequelae of COVID-19 Infection Through the National COVID Cohort Collaborative.", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 6125, "first_name": "Timothy A.", "last_name": "Gondre-Lewis", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-04", "end_date": "2029-08-31", "award_amount": 134923, "principal_investigator": { "id": 31760, "first_name": "Zachary", "last_name": "Butzin-Dozier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1079, "ror": "", "name": "UNIVERSITY OF CALIFORNIA BERKELEY", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "/ ABSTRACT Candidate: I am an epidemiologist in the Division of Biostatistics at the University of California, Berkeley School of Public Health, and I completed my Ph.D. in Epidemiology in August 2022 at UC Berkeley. Since my graduation, I have worked with the Center for Targeted Machine Learning and Causal Inference (CTML) to apply cutting-edge biostatistical and causal inference methods to pressing COVID-19 research questions using data from the National COVID Cohort Collaborative (N3C). I led a group of CTML epidemiologists and biostatisticians in the NIH Long COVID Computational Challenge (L3C) competition, where we were honored with third place for our ensemble machine learning model that accurately predicted the risk of Long COVID diagnosis based on individual electronic health record (EHR) data in N3C. I aim to become a leader in the application of innovative biostatistical, causal inference, and machine learning methods to impactful research questions related to infectious disease epidemiology. Environment: In order to attain my career goals, my training and mentorship plan will focus on recent advances in biostatistics, causal inference, and data science methods (Targeted Machine Learning) as well as immunology and infectious disease epidemiology. I have assembled an interdisciplinary team of expert biostatisticians, epidemiologists, and clinicians who will support my training. Alan Hubbard (primary mentor) and Mark van der Laan (co-mentor) will provide expert guidance and mentorship on biostatistics, data science, and causal inference. Rena Patel (co-mentor) and Jack Colford (scientific advisor) will provide mentorship and guidance in infectious disease epidemiology and immunology. Research: Researchers and clinicians have made enormous progress in understanding, preventing, and treating acute COVID-19 infection, but there is considerable uncertainty regarding the factors associated with long-term sequelae of COVID-19 infection. Although vaccination is a key strategy for COVID-19 epidemic control, little is known regarding the role of COVID-19 vaccination timing relative to COVID-19 infection (i.e., up-to-date vaccinations and boosters) in preventing long-term sequelae of infection, and the lack of objective Long COVID biomarkers hampers our ability to evaluate, prevent, and treat Long COVID. In Aim 1, I will evaluate the relationship between vaccination timing and Long COVID diagnosis in order to determine an optimized vaccination schedule to minimize Long COVID. In Aim 2, I will assess the relationship between COVID-19 vaccination timing and individual long-term sequelae of COVID-19 infection. In Aim 3, I will assess mediation of the relationship between acute COVID-19 infection and Long COVID via interleukin 6 (IL-6) to evaluate a biological mechanism of interest. I will apply Targeted Machine Learning methods to achieve these aims, which will prepare me for an R01-level application to apply these methods to research questions in infectious disease epidemiology.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15304", "attributes": { "award_id": "1R01LM014156-01A1", "title": "Optimizing mRNA sequences with deep neural networks", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Library of Medicine (NLM)" ], "program_reference_codes": [], "program_officials": [ { "id": 31895, "first_name": "Catherine Mary", "last_name": "Farrell", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-26", "end_date": "2028-07-31", "award_amount": 351000, "principal_investigator": { "id": 31896, "first_name": "Xiaobo", "last_name": "Zhou", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 788, "ror": "", "name": "UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic has presented new challenges to individuals world-wide. Since the first reports of infections in the US more than 90 million individuals have become infected and more than 1 million have died. SARS-CoV-2 genome has various open reading frames (ORFs) encoding 16 non-structural proteins (NSPs), 4 structural proteins and several accessory proteins. The genome of RNA virus can easily generate mutations as virus spreads. The constant emergence of new mutations in SARS-CoV-2 is the major challenge for the ongoing development of antiviral drug and broad neutralizing antibodies. The two mRNA vaccines from Pfizer/BioNTech and Moderna are moderate effective, 45 to 75 percent at protecting people from in preventing infection from the delta variant, and both of them have received emergency use authorization. More seriously, the omicron variant was first detected in southern Africa and quickly expanded to the whole world. According to a recent study, traditional dosing regimens of COVID-19 vaccines available in the US do not produce antibodies capable of recognizing and neutralizing the Omicron variant. The global data shows the coronavirus pandemic is far from over. Thus, more variants are expectable and some of them may escape the immune response produced after vaccination. How to keep the efficacy of existing mRNA vaccines on variants is challenging us. Aside from SARS- CoV-2, mRNA medicines against cancer and other infectious disease, such as Ebola, Zika virus, and influenza, are advancing through clinical trials. The goal of this project is to develop an integrated deep learning model to optimize 5'UTR, codon usage, and 3'UTR at same time that enables users to design the optimal mRNA sequence to enhance protein expression level, thus to improve the efficacy of mRNA medicines. mRNA medicines hold great promise for the treatment of a wide variety of disease, extending from prophylactics to therapeutics for infectious diseases, cancer, and genetic disease. mRNA medicines have several beneficial features: safety, efficacy, production, and speed. Multiple factors are involved to regulate the stability and efficiency of mRNA, including 5' untranslated region (UTR), 3' UTR, codon et al, and several in silico approaches have being developed to optimize these factors respectively However, as these factors always function together during the translation of mRNA, and individual optimization is insufficient. Thus, a novel integrated deep learning model for these factors is needed to comprehensively enhance the stability and efficiency of mRNA medicine. In silico optimization of mRNA vaccine provides a fast methodology to investigate all possible integration of the ORF, 5' UTR and 3'UTR and identify the optimal mRNA vaccine. The interdisciplinary team proposed to develop the following aims: (1) developing deep learning models for 5' UTR, codon, and 3' UTR respectively, and integrated model for systemic optimization of 5' UTR, codon, and 3' UTR; and (2) experimentally validate the integrated models by designing the 5'UTR, codon, and 3'UTR sequence for representative.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15339", "attributes": { "award_id": "1R24GM153920-01", "title": "MIDAS Coordination Center - Year 6-10", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 12060, "first_name": "Han", "last_name": "Nguyen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-15", "end_date": "2029-06-30", "award_amount": 1474323, "principal_investigator": { "id": 24349, "first_name": "HARRY S", "last_name": "HOCHHEISER", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 848, "ror": "", "name": "UNIVERSITY OF PITTSBURGH AT PITTSBURGH", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "For nearly 20 years, the Models of Infectious Disease Agent Study (MIDAS) research network has been an active community of infectious disease modeling researchers. The opening of the MIDAS to the broader community and the COVID-19 pandemic spurred an intense increase in interest, with membership growing from 152 researchers in 2019 to more than 1000 in 2023. Since 2019, the MIDAS Coordination Center (MCC) led by the University of Pittsburgh has worked to support the community. The MCC has provided the community with resources in support of ID modeling, through a FAIR catalog of infectious disease modeling resources, including a curated archive of COVID data as provided by public health agencies, and through the creation of more than 200 COVID-19 and Mpox datasets in the standards-compliant Project Tycho data format. Community support efforts included an active website; special-interest groups for Latin-American researchers and students; support for the COVID-19 and Flu Scenario Modeling Hubs; monthly webinars; and events including the Workshop to Increase Diversity in Mathematical Modeling and Public Health and the MIDAS annual meeting. To increase the rigor of modeling efforts, the MCC team developed and validated a checklist for information needed to ensure the reproducibility of infectious disease modeling efforts. In the next phase of the MCC, leading modelers from the University of Virginia, Johns Hopkins Bloomberg School of Public Health, and the University of Maryland will join forces with the team at the University of Pittsburgh to expand the community focus of the MIDAS network and promote further advances in modeling research. The new MCC will: 1) develop a community- focused process to extend the MIDAS catalog with software, educational, and modeling results resources; 2) Expand the collection of Tycho-formatted gold standard datasets and related data; 3) Organize community-based activities, including workshops, working groups, educational activities and challenges; 4) Train the next generation of infectious disease researchers; 5) organize coordination and outreach activities, including efforts aimed at engaging with public health officials, mapping of community activities; hosting monthly webinars; and planning an annual meeting; and 6) conduct novel research into the development of model description frameworks and taxonomies in support of rigorous evaluation of modeling approaches. A focus on responsiveness to community-needs will ensure the relevance and impact of MCC efforts.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15296", "attributes": { "award_id": "1R25AI175011-01A1", "title": "Integrated Training Program in Vaccinology (ITP-Vax)", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of Allergy and Infectious Diseases (NIAID)" ], "program_reference_codes": [], "program_officials": [ { "id": 31247, "first_name": "MADELYN", "last_name": "Reyes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-15", "end_date": "2029-07-31", "award_amount": 352643, "principal_investigator": { "id": 31887, "first_name": "Sharon Mei", "last_name": "Tennant", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 793, "ror": "", "name": "UNIVERSITY OF MARYLAND BALTIMORE", "address": "", "city": "", "state": "MD", "zip": "", "country": "United States", "approved": true }, "abstract": "Vaccination has had a profound impact on human health and has eradicated or almost eradicated once feared diseases such as smallpox and polio and substantially decreased morbidity and mortality due to pneumococcus, measles and pertussis amongst many others. The utility and cost effectiveness of vaccination has been shown multiple times and most recently during the COVID-19 pandemic. SARS-CoV-2 has had a devastating effect on humankind causing significant morbidity and mortality as well as major disruptions to the economy, education, the supply chain and mental health. However, the extremely rapid development and deployment of multiple COVID-19 vaccines has allowed society to return to a semblance of normality. These vaccines were developed because of the large amount of money invested by governments to de-risk development and the many dedicated vaccinologists (laboratory personnel, clinical trialists, nurses, regulatory affairs specialists, statisticians etc. in academia, government and industry) who were committed to working on a common goal. Additionally, novel vaccine platforms had been in development for many years so the knowledge about how these platforms could be harnessed for COVID-19 was already present. In order to be ready for the next pandemic, we need to ensure that there are sufficient individuals entering and staying in the field of vaccinology so that they can develop new platforms, evaluate and dissect immune responses, perform clinical trials and have a broad understanding of the entire vaccine development process. The overarching goal of the Integrated Training Program in Vaccinology (ITP-Vax) is to encourage more trainees to join the field of vaccinology, particularly under-represented minorities (URM), and to enable our existing outstanding early-career vaccinologists to excel in mentorship and to become fully independent. To achieve this goal, we propose the following aims: Aim 1) To provide training in mentorship to early-career investigators and assist them on their path to independence in vaccinology, and Aim 2) To provide comprehensive training in vaccinology to post-baccalaureate or Master’s level students who are intending to apply for a PhD or medical school in the next 1-2 years and who seek a career in vaccinology. We will also actively engage and recruit URM’s for ITP-Vax so that we can ultimately improve diversity in vaccinology.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15160", "attributes": { "award_id": "2413049", "title": "Collaborative Research: SaTC: EDU: A Socially-Distant Cloud-Based Hardware Security Educational Platform", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "IUSE" ], "program_reference_codes": [], "program_officials": [ { "id": 31735, "first_name": "ChunSheng", "last_name": "Xin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-08-15", "end_date": null, "award_amount": 213100, "principal_investigator": { "id": 31737, "first_name": "Aydin", "last_name": "Aysu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 245, "ror": "https://ror.org/04tj63d06", "name": "North Carolina State University", "address": "", "city": "", "state": "NC", "zip": "", "country": "United States", "approved": true }, "abstract": "The main goal of this project is to develop and deliver remote experiments utilizing cloud-based resources aimed at educating a broad audience of students and practitioners in hardware security. In the post-COVID era, it is imperative to develop online education platforms for remote training of both students and the workforce in the field of Hardware Security. Recent advances in this field and FPGA-based cloud servers have enabled an opportunity to move related experiments to an online format that only requires a standard computer and internet connection by the students. Teaching “hardware” security in a socially distanced format poses significant challenges. Essential experiments for teaching key concepts in hardware security necessitate multiple evaluation boards and physical equipment such as voltage supplies, oscilloscopes, multimeters, and function generators. To adapt these experiments for an online platform, the project will explore innovative methods to execute or emulate them using the cloud ecosystem. This project addresses a critical gap by developing a fully online hardware security training module accessible to students and professionals worldwide. <br/><br/>This project proposes various comprehensive experiments testing different notions in hardware security. The framework will be designed for both undergraduate and graduate students in the electrical engineering, computer engineering, and computer science departments, leveraging courses developed by the PIs in their respective institutions. The proposed infrastructure includes preparing detailed experiments for instructors with walkthrough documents and organizing student assignments for independent completion. This setup supports not only teaching but also facilitates independent research upon assignment completion. Supplemented with video instructions, these experiments will constitute a comprehensive training module, equipping participants with the necessary skills and knowledge to address complex challenges in this emerging domain, thereby instilling preparedness and confidence. <br/><br/>This award is co-funded by the NSF Improving Undergraduate STEM Education (IUSE: EDU) Program. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is further supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case, cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.<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": "15375", "attributes": { "award_id": "1R15GM157661-01", "title": "Molecular Mechanism of Folding of Nsp12 and Assembly of the SARS-CoV-2 RNA Polymerase Complex by the Cytosolic Chaperonin CCT", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 24785, "first_name": "ANDRE W.", "last_name": "PHILLIPS", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-18", "end_date": "2027-08-31", "award_amount": 447548, "principal_investigator": { "id": 31976, "first_name": "BARRY M", "last_name": "WILLARDSON", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 436, "ror": "https://ror.org/047rhhm47", "name": "Brigham Young University", "address": "", "city": "", "state": "UT", "zip": "", "country": "United States", "approved": true }, "abstract": "The COVID-19 pandemic created the greatest infectious threat to global health in 100 years, and monumental efforts have been made by the scientific community to combat the SARS-CoV- 2 virus. This proposal seeks to extend this effort by investigating a mechanism by which SARS- CoV-2 hijacks the host cell chaperone system to replicate itself. We have evidence that the SARS-CoV-2 RNA polymerase (RdRp) co-opts the cytosolic chaperonin containing TCP-1 (CCT, also called TRiC) to assemble the active polymerase complex. CCT is a large (1 MDa) protein-folding machine that plays a major role in the cellular chaperone network responsible for maintaining the proteome in good working condition. It uses ATP hydrolysis-driven conformational changes to assist cytosolic proteins with multiple domains, complex folding trajectories, or obligate binding partners to achieve their native state and assemble into complexes. In addition to folding cellular proteins, CCT has been shown to bind several viral proteins and contribute to viral replication of HIV, hepatitis C, influenza A, rabies, Zika and reovirus. These observations show that CCT is a common host chaperone used by diverse viruses to fold viral proteins, assemble viral complexes, and support viral replication. Based on these findings, we initiated an investigation of the role of CCT in SARS-CoV-2 replication. Here, we present robust preliminary evidence indicating that the SARS-CoV-2 non-structural protein 12 (Nsp12), the catalytic subunit of the RNA polymerase, is folded by CCT and that CCT contributes to RdRp complex formation and SARS-CoV-2 replication. In Aim 1, we propose to thoroughly test this hypothesis using multiple experimental approaches. In Aim 2, we propose to determine high-resolution structures of the complex between Nsp12 and CCT. We have isolated an Nsp12 folding intermediate bound to CCT and have determined preliminary structures of the complex by cryogenic electron microscopy (cryo-EM). Further cryo-EM analysis will yield a high- resolution structure of the Nsp12-CCT complex, which will be invaluable in guiding the design of therapeutics to block Nsp12 folding by CCT, inhibit formation of the RdRp complex, and disrupt viral replication.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15312", "attributes": { "award_id": "1R35GM152454-01", "title": "Endothelial mechanisms of multiorgan dysfunction", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute of General Medical Sciences (NIGMS)" ], "program_reference_codes": [], "program_officials": [ { "id": 22508, "first_name": "CHIEN-CHUNG", "last_name": "Chao", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-15", "end_date": "2029-08-31", "award_amount": 409583, "principal_investigator": { "id": 31899, "first_name": "Alejandro Pablo", "last_name": "Adam", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2530, "ror": "", "name": "ALBANY MEDICAL COLLEGE", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Three different fates await the millions of critically ill patients admitted to intensive care units every year. Close to 30% will recover without obvious sequelae, 15% succumb to the acute illness, and the remainder 55% will develop various degrees of long-term impairments in cognitive, immune, cardiovascular, or renal functions, leading to increased overall mortality. These sequelae are diagnosed under the umbrella term post-intensive care syndrome (PICS). We lack the knowledge to improve acute survival, and to predict and treat PICS. Largely, therapies for septic shock and other critially ill patients are limited to infectious source control and hemodynamic support. Severe systemic inflammatory reactions, including sepsis, often lead to shock, organ failure and death, in part through an acute release of cytokines that promote vascular dysfunction. The current body of work, including our own research, strongly argues for a critical role for the endothelium in determining the outcomes of critical illness through expression of multiple proteins to promote disseminated intravascular coagulopathy, leukostasis and edema. However, simply blocking cytokine activity does not improve survival, in large part due to the immunosuppresive actions of these treatments. It is imperative to rethink the problem. We posit that a better understanding of the endothelial mechanisms downstream of cytokine signaling will lead to improved therapies to prevent organ damage and mortality without interfering with the required pathogen clearance. Little is known about the endothelial signaling pathways regulating the transcriptional profile in failing organs. This proposal is designed to take full advantage of the innovative tools and knowledge we developed during the last several years to ask fundamental mechanistic questions on the role of endothelial signaling and transcriptional responses during severe inflammation. Sourcing of human primary endothelial cells in-house allows us to perform mechanistic studies in a cost-effective manner, a panel of endothelial-specific transgenic mice enables us to study key regulators of transcription in the context of multiorgan dysfunction, and clinical collaborators provide us with unique human specimens to ensure the translatability of our research. Our prior findings of a critical role for the IL6-STAT3-SOCS3 signaling axis in the endothelium provides a strong scientific basis for the proposed working model, and our new unpublished bioinformatics analysis of endothelial translatome of failing organs suggest several novel IL6 effectors of endotheliopathy, providing initial targets for further research. We aim at determining which changes dictate the severity of acute shock (and thus short-term survival), and which lead to long-term consequences well beyond the resolution of the initial shock. Key questions driving our research are: 1) What are the effectors downstream of a cytokine storm that we can target to limit organ dysfunction without limiting the immune response? 2) What are the main drivers of long-term consequences and chronic inflammation after shock recovery? 3) How can we take advantage of the complexity of the endothelial response to tailor it towards a pro-immune response while limiting the collateral damage? The outcome of our efforts in answering these critical questions is the discovery of key determinants of organ failure. The knowledge gained may lead to innovative non-immunosuppressive therapeutic strategies to limit organ dysfunction.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15096", "attributes": { "award_id": "2403380", "title": "Collaborative Research: SHF: Medium: SCIOPT: Toward Certifiable Compression-Aware SciML Systems", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)", "Software & Hardware Foundation" ], "program_reference_codes": [], "program_officials": [ { "id": 2785, "first_name": "Almadena", "last_name": "Chtchelkanova", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-10-01", "end_date": null, "award_amount": 272992, "principal_investigator": { "id": 31636, "first_name": "Martin", "last_name": "Burtscher", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 204, "ror": "", "name": "Texas State University - San Marcos", "address": "", "city": "", "state": "TX", "zip": "", "country": "United States", "approved": true }, "abstract": "The future of science-enabled discoveries critically relies on the speed of high-performance simulations conducted at large scales and high resolutions. Unfortunately, lacking such performance and scale, current approaches cannot keep up with the backlog of problems in areas of paramount societal consequence, such as climate science and the spread of pandemics. A principal reason for these shortfalls is the rising cost of moving huge amounts of simulation data between supercomputer memories and processors – a cost that increasingly dwarfs the time spent in actual computations. Thus, developing techniques to reduce the volume of data exchanged without sacrificing accuracy is key to future progress in computation-enabled research. Such data reduction is even more important in the emerging area of Scientific Machine Learning (SciML), where simulations are assisted by artificial intelligence (AI) based surrogate models, an area where the data exchange needs are often much higher. The investigators’ expertise in scientific machine learning, data compression, compilers, and program correctness will be central in our collaboration to help SciOPT achieve its goal of fast and reliable AI-assisted scientific simulations. The impact of this project will be to establish new technologies that reduce data volume without sacrificing accuracy in both high-performance computing and the emerging area of SciML. These technologies, in turn, translate directly into societal benefits such as improved healthcare and safer environments. The project will broaden participation in this area through undergraduate research plans that reach out to students from groups underrepresented in computing.<br/><br/>This research project, entitled SciOPT, will principally rely on data compression to reduce the amount of data moved: simulation data will be compressed before transmission and decoded upon reception before applying computations. The investigators will also pursue the potentially even more impactful approach of compressing the data and applying computations directly on the compressed data. SciOPT will evaluate both of these approaches in the context of challenging SciML applications that are currently bottlenecked by data exchanges. To ensure higher degrees of automation and productivity, SciOPT will develop efficient compiler-based methods to manage compressed data layout and locality. Moreover, it will automatically generate high-speed compression algorithms that are tailored to the data. To ensure the veracity of the computational results produced by these compressed-data simulations, SciOPT will include rigorous correctness-checking methods at multiple stages to guard the overall simulation workflows.<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 } } ], "meta": { "pagination": { "page": 4, "pages": 1424, "count": 14236 } } }