Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1386&sort=title
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=title", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1419&sort=title", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1387&sort=title", "prev": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1385&sort=title" }, "data": [ { "type": "Grant", "id": "15057", "attributes": { "award_id": "5R44GM149095-02", "title": "Use of Time Series Biomarker and Clinical Data to Construct a Time Trajectory Host Response Map", "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": 31602, "first_name": "Sailaja", "last_name": "Koduri", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-06-01", "end_date": "2025-05-31", "award_amount": 1272176, "principal_investigator": { "id": 12385, "first_name": "Bobby", "last_name": "Reddy", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1193, "ror": "", "name": "Prenosis, Inc.", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 2017, "ror": "", "name": "PRENOSIS, INC.", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Principal Investigator/Program Director (Last, first, middle): Reddy, Jr., Bobby Project Summary: Sepsis is an incompletely understood clinical syndrome characterized by a dysregulated host response to infection. In partnership with 8 U.S. hospitals, Prenosis amassed one of the world’s largest datasets and biobanks that combines biomarker and clinical data for patients suspected of infection, housing over 70,000 plasma or serum samples from over 14,000 patients. We also curated a dataset of dense time-series data from each patient’s Electronic Medical Record (EMR), including demographics, vitals, lab results, interventions, outcomes, and many other parameters. To commercialize insights from these data, Prenosis built ImmunixTM, an FHR/HL7 compatible software platform for clinical deployment of diagnostics and clinical decision support tools. Under a previously awarded NIGMS grant (1R44GM139529), Prenosis built a testing pipeline to measure 40 critical protein biomarkers from biobanked samples. To date, we measured these biomarkers on only the initial sample per patient for 6,000 patients and combined these data with EMR clinical parameters to construct 8 endotypes of sepsis. The identification and classification of endotypes—groupings of patients with similar biologic and clinical features—is increasingly becoming recognized as a valuable methodologic approach to assessing patients with sepsis. To complete work for the existing grant, Prenosis will measure the baseline sample for additional patients to total about 10,000 patients to refine and validate the endotypes. In this project, Prenosis proposes to add a critical new dimension to the data by assaying and analyzing longitudinal, time-series biomarker data. We will leverage our pipeline to measure the 40 core biomarkers from 9,000 follow-up samples from ~3,400 patients that we have already collected and stored in the biobank. We will assess the additional value of longitudinal time-series biomarker measurements and clinical data. Initial feasibility data from over 1,000 measured samples demonstrates that longitudinal data provide additional powerful new biologic and prognostic insights. Analytics built upon these data have the potential to improve diagnostic and drug development products for sepsis and COVID. The overall hypothesis of this project is that longitudinal biomarkers will add a valuable biologic and prognostic dimension to endotypes for sepsis; and these longitudinal endotypes will better classify patients who may have a heterogeneous response to sepsis therapies.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "15761", "attributes": { "award_id": "1R43DA063255-01", "title": "User-Centric Development of a Detection Module for a Closed-Loop Solution to Opioid Overdose", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Drug Abuse (NIDA)" ], "program_reference_codes": [], "program_officials": [ { "id": 32828, "first_name": "LEONARDO MARIA", "last_name": "ANGELONE", "orcid": "", "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2025-08-01", "end_date": "2026-07-31", "award_amount": 399715, "principal_investigator": { "id": 26622, "first_name": "Vy", "last_name": "Le", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26623, "first_name": "Hyowon", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 2630, "ror": "", "name": "RESCUE BIOMEDICAL, LLC", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "This Phase I SBIR proposal aims to advance the commercialization of Rescue Biomedical LLC’s Automatic Antidote Delivery Device (A2D2) by first developing and commercializing its overdose detection component, the Automatic Detection Device (AD2). Despite growing awareness of opioid dangers, 81,083 opioid overdose deaths occurred in 2023, exacerbated by COVID-19. Fentanyl overdose deaths nearly doubled from 11.4 per 100,000 people in 2019 to 21.8 per 100,000 in 2021. These potent drugs can cause respiratory failure within minutes, leaving little time to administer antidotes like naloxone. This crisis has imposed immense financial burdens on the healthcare system and devastated thousands of families each year. The A2D2 is a comprehensive, closed-loop solution that combines a user-centric wearable with an implantable drug delivery system, automatically administering naloxone upon detecting opioid-induced respiratory depression. This device has the potential to (1) reduce the need for intensive treatment, (2) decrease hospitalization days, and (3) alleviate the financial burden on patients. These benefits extend to the healthcare industry by freeing up resources to address other health issues. Early customer discovery and regulatory interactions have led to the strategic decision to commercialize the detection component (AD2) separately and first. This approach provides customers with options and allows us to establish a market presence earlier, paving the way for the full A2D2 solution. Our initial focus will be on refining the detection software, strategizing go-to-market plan, and ensuring manufacturability. To achieve this, we propose the following Specific Aims for Phase I: Aim 1: Demonstrate User Acceptability: Understand patients’ willingness to wear the AD2 and validate the interface during the detection of an overdose. Aim 2. Assess Market Integration: Conduct targeted research to evaluate the market readiness and logistical considerations, including potential partnerships with clinics and marketing strategies. Aim 3. Determine Manufacturing Feasibility: Evaluate manufacturability while concurrently designing an early alpha prototype in a wearable form factor. In this Phase I SBIR proposal, Rescue Biomedical aims to validate the acceptability of an overdose detection device among OUD patients, ensuring it contacts emergency services for prompt medical intervention and has a sufficient alert notification interface. We will collaborate with RMS to quantify user willingness to adopt such devices. Additionally, we will identify stakeholders at private treatment centers, such as LCDCs and psychiatrists, to assess their openness to incorporating new solutions like the AD2. Finally, with the help of Kablooe Design, we will ensure the AD2 can be manufactured on a large scale, allowing us to refine the design early during development. The long-term objective of this project is to reduce opioid overdose-related deaths by providing a reliable, user-friendly device that can be integrated into the daily lives of high-risk patients. By addressing the urgent need for effective overdose detection and immediate prompt for medical intervention, this project aligns with the mission of the funding agency to improve public health and save lives. The research design includes a comprehensive approach to user adoption, market integration assessment, and manufacturability evaluation, ensuring the project's relevance and feasibility for broad-scale implementation.", "keywords": [ "Accident and Emergency department", "Accounting", "Address", "Adopted", "Adoption", "Agreement", "Antidotes", "Area", "Awareness", "COVID-19", "COVID-19 pandemic", "Cellular Phone", "Cessation of life", "Clinic", "Collaborations", "Computer software", "Dangerousness", "Detection", "Development", "Devices", "Drug Delivery Systems", "Drug Implants", "Ecosystem", "Effectiveness", "Emergency medical service", "Ensure", "Evaluation", "FDA approved", "Family", "Feedback", "Fentanyl", "Financial Hardship", "Funding Agency", "Goals", "Health", "Health Care Facility", "Health Care Industry", "Health Care Systems", "Hospitalization", "Hybrids", "Implant", "Implantable Infusion Pumps", "Injectable", "Intervention", "Knowledge", "Life", "Marketing", "Medical", "Mission", "Monitor", "Naloxone", "Nervous System Trauma", "Notification", "Opioid", "Organ failure", "Outpatients", "Overdose", "Overdose reversal", "Patients", "Persons", "Pharmaceutical Preparations", "Phase", "Prevalence", "Privatization", "Psychiatrist", "Public Health", "Readiness", "Reporting", "Research", "Research Design", "Resources", "Respiration", "Respiratory Failure", "Self Administration", "Small Business Innovation Research Grant", "Symptoms", "System", "Targeted Research", "Time", "Treatment Protocols", "United States", "Urine", "User Compliance", "Ventilatory Depression", "Wrist", "capsule", "commercialization", "design", "detection platform", "drug testing", "experience", "fentanyl overdose", "follow-up", "high risk", "illicit opioid", "improved", "innovation", "manufacturability", "manufacture", "mortality", "opioid epidemic", "opioid induced respiratory depression", "opioid mortality", "opioid overdose", "opioid use disorder", "overdose death", "pandemic disease", "prevent", "preventable death", "prototype", "public health emergency", "sensor", "subcutaneous", "success", "synthetic opioid", "tertiary care", "tissue oxygenation", "treatment center", "user-friendly", "wearable device", "willingness" ], "approved": true } }, { "type": "Grant", "id": "10594", "attributes": { "award_id": "1R44DA056277-01", "title": "User-centric development of closed-loop therapy for opioid overdose", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "National Institute on Drug Abuse (NIDA)" ], "program_reference_codes": [], "program_officials": [ { "id": 12311, "first_name": "LEONARDO MARIA", "last_name": "Angelone", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-30", "end_date": "2023-08-31", "award_amount": 319995, "principal_investigator": { "id": 26622, "first_name": "Vy", "last_name": "Le", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 26623, "first_name": "Hyowon", "last_name": "Lee", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1950, "ror": "", "name": "RESCUE BIOMEDICAL, LLC", "address": "", "city": "", "state": "IN", "zip": "", "country": "United States", "approved": true }, "abstract": "This Fast-Track SBIR proposal will help make significant inroads towards commercializing Rescue Biomedical LLC's automatic antidote delivery device (A2D2) in order to combat opioid overdose-related deaths. Despite increasing awareness of the dangers associated with opioids, there were more than 70,000 deaths due to opioid overdose in 2020. In addition, the rising popularity of illegal synthetic opioids and social isolation due to the ongoing COVID-19 pandemic is fueling a third wave of opioid-related deaths. Since these illegal drugs are orders of magnitude more potent than morphine and can cause respiratory failure in minutes, there is very little time for users to identify and respond to the symptoms of overdose with antidotes like naloxone. The proposed A2D2 is a low-cost and simple subcutaneous drug delivery capsule that can automatically detect opioid- induced respiratory failure and release naloxone in a closed-loop manner. It will extend the lifetime of overdosed patients so that they can receive proper medical care. In this Fast-Track SBIR proposal, Rescue Biomedical seeks to validate its hypothesis that there is a significant demand for a wearable-implantable combinational device for automatically detecting and treating opioid overdose. In Phase I, Rescue Biomedical will work with its collaborator at Indiana University School of Medicine to quantify users' willingness to use wearable and implantable devices. Furthermore, clinicians and other stakeholders (e.g., Licensed Chemical Dependency Counselor) will be identified to understand their willingness to recommend advanced harm reduction devices. Finally, Rescue Biomedical will leverage the cohort of these stakeholders to validate the overall system design to ensure high compliance and usability. In Phase II, efforts will be focused on technical integration and performing a pivotal pre-clinical experiment to demonstrate the closed-loop capabilities to reverse the effects of opioid overdose. Moreover, Rescue Biomedical will focus efforts for commercialization including regulatory submissions for IND/IDE submission and performing design verification and validation based on user feedback.", "keywords": [ "Accident and Emergency department", "Address", "Antidotes", "Awareness", "COVID-19 pandemic", "Caring", "Cellular Phone", "Centers for Disease Control and Prevention (U.S.)", "Cessation of life", "Climate", "Data", "Detection", "Development", "Development Plans", "Devices", "Dimensions", "Dose", "Drug Addiction", "Drug Delivery Systems", "Emergency Situation", "Ensure", "Feedback", "Fentanyl", "Goals", "Harm Reduction", "Health care facility", "Human", "Hybrids", "Indiana", "Injectable", "Intramuscular", "Knowledge", "Life", "Medical", "Modeling", "Monitor", "Morbidity - disease rate", "Morphine", "Mus", "Naloxone", "Nervous System Trauma", "Opioid", "Organ failure", "Outpatients", "Overdose", "Patients", "Persons", "Pharmaceutical Preparations", "Phase", "Phase I Clinical Trials", "Prevalence", "Professional counselor", "Provider", "Recovery", "Repression", "Research", "Research Personnel", "Respiration", "Respiratory Failure", "Savings", "Self Administration", "Small Business Innovation Research Grant", "Social isolation", "Symptoms", "System", "Testing", "Time", "Translations", "Universities", "Urine", "User Compliance", "Validation", "Ventilatory Depression", "Work", "base", "capsule", "cohort", "combat", "commercialization", "cost", "design", "design verification", "drug testing", "experimental study", "follow-up", "illicit opioid", "implantable device", "in vivo", "medical schools", "meetings", "method development", "minimally invasive", "novel", "opioid mortality", "opioid overdose", "opioid therapy", "opioid use disorder", "pandemic disease", "pre-clinical", "preclinical evaluation", "prevent", "respiratory", "subcutaneous", "success", "synthetic opioid", "tertiary care", "tissue oxygenation", "tool", "usability", "verification and validation", "wearable device", "willingness" ], "approved": true } }, { "type": "Grant", "id": "4354", "attributes": { "award_id": "1440149", "title": "USGCRP Support for International START Secretariat", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Integrat & Collab Ed & Rsearch" ], "program_reference_codes": [], "program_officials": [ { "id": 14817, "first_name": "Maria", "last_name": "Uhle", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2015-02-01", "end_date": "2021-01-31", "award_amount": 3923577, "principal_investigator": { "id": 14819, "first_name": "Jon", "last_name": "Padgham", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 1270, "ror": "", "name": "START International, Inc.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 14818, "first_name": "Sarah E", "last_name": "Schweizer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1270, "ror": "", "name": "START International, Inc.", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "This award provides support from the US Global Change Research Program for the Secretariat operations of the global change SysTem for Analysis, Research, and Training (START). This organization is an essential component of the US Global Change Research Program strategic plan as it relates to international engagement. START?s mission \"to increase opportunities for research, education and training that strengthen scientific capacities in developing countries to understand, communicate and motivate action on critical global change challenges\" addresses all of the US Global Change Research Program strategic goals. The funding will enable START Secretariat to continue developing and implementing programs that advance ?research-driven capacity building? in Africa and Asia. This approach emphasizes experiential learning by doing, particularly for early-career scientists, in which targeted skill building and networking opportunities are integrated into research on regional impacts and risks stemming from global change. START?s work focuses on critical challenges at the intersection of global/climate change and sustainable development, including disaster risk reduction, land-use/land-cover change, biodiversity conservation, urban development, human health, water resources management, and agriculture and food security.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10348", "attributes": { "award_id": "1R21HD107409-01A1", "title": "Using a discrete choice experiment to determine preferences for STI testing models for Black adolescent males", "funder": { "id": 4, "ror": "https://ror.org/01cwqze88", "name": "National Institutes of Health", "approved": true }, "funder_divisions": [ "Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)" ], "program_reference_codes": [], "program_officials": [ { "id": 24440, "first_name": "Ronna", "last_name": "Popkin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-09-01", "end_date": "2024-08-31", "award_amount": 261752, "principal_investigator": { "id": 26309, "first_name": "Melissa", "last_name": "Kottke", "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": "Of the 26 million new sexually transmitted infections (STI) in 2018, almost half were among adolescents (15-19 years old) and young adults (20-24 years old). Millions of cases remain undiagnosed and untreated, particularly among male teens. Less than 3% of sexually active male teens have had an STI test in the past 12 months versus 26% of their female peers. Male teens are more likely to forego testing than female teens due to greater confidentiality concerns, cost/insurance barriers, and lower self-perceived risk. COVID-19 has exacerbated barriers to STI care, as testing services have plummeted by 66% since March 2020. There is great urgency to develop innovative and effective methods to screen adolescent males for STIs: as males are largely asymptomatic, their untreated STIs can be transmitted unknowingly to other sexual partners. Untreated STIs can lead to male and female infertility and increase one’s susceptibility to contracting HIV. The objective of this R21 is to use an innovative, user-centered approach, a discrete choice experiment, to determine the preferences for STI testing models among Black adolescent males. In a discrete choice experiment (DCE), a methodology that has been widely used to inform the design/adoption of health services, respondents are presented with two services with different attributes and are asked to choose which one they prefer (e.g., Testing Model A or B). This series of choice-tasks elucidates the relative importance of certain attributes as well as acceptable trade- offs. Importantly, DCEs can be used to highlight shared but distinct preferences within different groups of Black adolescent males, which may justify the development of multiple, tailored STI testing models. The Early- Investigator-led team, with extensive expertise in recruiting Black youth, will accomplish this objective through two aims: Aim 1 will build the DCE collaboratively with a community-based Youth Advisory Board comprised of 15 Black male adolescents from Atlanta, GA. Qualitative methods such as interviews, focus groups, and pile- sort exercises will be used to define the testing attributes (e.g., test location, time till results) which are most important to youth; these attributes will be integrated into the experimental design of the DCE. Aim 2 will leverage multi-platform community-based and online strategies to recruit a heterogenous sample of 500 Black male adolescents to participate in a 15-minute online DCE. A latent class analysis will be used to elucidate preference heterogeneity for STI testing, explore groups or classes with distinct attribute preferences, and assess associated behaviors/characteristics. The research approach will be anchored in DCE best practices and in the Phenomenological Variant of Ecological Systems Theory, a framework that recognizes the unique contexts in which Black youth develop. This R21 innovatively explores a growing health disparity in a population often neglected in research. Aligning with the NICHD Strategic Plan, this study will engage Black adolescent males in STI services critical to support their own sexual health and their partners’. Future research will create and test the acceptability and cost-effectiveness of diverse STI testing models in increasing testing rates among youth.", "keywords": [ "19 year old", "Adolescent", "Adoption", "Area", "Behavior", "Behavioral", "Black Populations", "Black race", "COVID-19", "Caring", "Characteristics", "Choice Behavior", "Clinic", "Clinical", "Communities", "Comprehension", "Contracts", "Data", "Development", "Ecosystem", "Ectopic Pregnancy", "Epidemic", "Exercise", "Experimental Designs", "Female", "Female infertility", "Focus Groups", "HIV", "HIV Infections", "Health", "Health Services", "Heterogeneity", "Human immunodeficiency virus test", "Infection", "Infertility", "Insurance", "Intervention", "Interview", "Lead", "Life", "Location", "Male Adolescents", "Male Infertility", "Methodology", "Methods", "Minority Groups", "Modeling", "National Institute of Child Health and Human Development", "Nature", "Outcome", "Participant", "Pelvic Inflammatory Disease", "Personal Satisfaction", "Population", "Predisposition", "Privatization", "Process", "Qualitative Methods", "Research", "Research Personnel", "Respondent", "Risk", "Sampling", "Self Administration", "Series", "Services", "Sexual Health", "Sexual Partners", "Sexually Transmitted Diseases", "Strategic Planning", "Structure", "Surveys", "Systems Theory", "Teenagers", "Testing", "Time", "Variant", "Vulnerable Populations", "Youth", "base", "behavioral economics", "black men", "cognitive testing", "cost", "cost effectiveness", "design", "empowered", "experience", "experimental study", "health disparity", "high risk", "home test", "infection rate", "infection risk", "innovation", "male", "men", "men who have sex with men", "metropolitan", "neglect", "neonatal outcome", "peer", "preference", "recruit", "sexually active", "testing services", "transmission process", "uptake", "young adult", "young man" ], "approved": true } }, { "type": "Grant", "id": "13662", "attributes": { "award_id": "2154295", "title": "Using a Well-Controlled Heterogeneous Permeability Field to Study Its Role on Miscible Density-Driven Convection in Porous Media", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "Hydrologic Sciences" ], "program_reference_codes": [], "program_officials": [ { "id": 29875, "first_name": "Hendratta", "last_name": "Ali", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-10-01", "end_date": null, "award_amount": 367758, "principal_investigator": { "id": 29876, "first_name": "Cheng", "last_name": "Chen", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 1113, "ror": "https://ror.org/02z43xh36", "name": "Stevens Institute of Technology", "address": "", "city": "", "state": "NJ", "zip": "", "country": "United States", "approved": true }, "abstract": "Injection of carbon dioxide (CO2) into deep saline aquifers is a promising solution to lessen global climate change. Injected CO2 dissolves in aquifer water and increases its density. Increased density affects the water flow and the mobility of the injected CO2. This mobility is also influenced by the permeability of the aquifer. The permeability of deep aquifers varies greatly in space and affects the ways in which the fluids move. This research uses 3D printing technologies to build experimental setups that can reproduce the complex characteristics of deep aquifers and study how variable permeability influence the density-driven movement of fluids in porous media. Results from this research will benefit society by providing needed information for efficient management of CO2 injection in deep aquifers. This is critical to understanding the feasibility of using carbon capture and storage in deep aquifers as a viable technology to mitigate CO2 emissions, global warming and climate change. The project will also serve to broaden the education and training of graduate and undergraduate students, increase public scientific literacy, engage women and minority students, and develop partnership with industry and local business.<br/><br/>The research objective of this project is to use high-resolution 3D printing technologies to overcome the challenges encountered by conventional experimental methods, in order to: 1) validate the influence of permeability on the critical Rayleigh-Darcy number and critical time for the onset of miscible density-driven convection, 2) construct a known and well-controlled heterogeneous permeability field to study its role on the onset of miscible density-driven convection, and 3) investigate how heterogeneous permeability fields dictate the later-time flow patterns and mass transfer rates. Specifically, computer modeling is used to generate particle assemblies, which are referred to as \"digital sediment\" blocks. The pore structural information will be imported into a lattice Boltzmann simulator as internal boundary conditions of flow modeling for permeability calculation. These \"digital sediment\" blocks will then be fabricated using high-resolution 3D printing to construct the desired permeability structure. In this project, the heterogeneity structure of a permeability field is characterized by permeability variation and correlation length. An experimental analogue fluid system equipped with high-speed cameras will be used to measure the convective mass transfer rate under various combinations of permeability variance and correlation length. The 3D-printed \"digital sediment\" blocks have known and well-controlled permeabilities and are reusable for a different heterogeneous permeability field. These advantages facilitate the construction of heterogeneous porous media and thus increase the total number of laboratory experiments that can be conducted, which is critical for satisfying the ergodicity requirement and makes the fluid system a valuable experimental analogue for validating analytical and numerical findings. Generated knowledge is transformative and will contribute to the study of other density-driven convection processes in heterogeneous porous media. Reinforced by the research plan, the outreach plan will target different educational settings to increase public scientific literacy, engage women and minority students in STEM, and prepare students to contribute to a modern workforce.<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": "11811", "attributes": { "award_id": "2242538", "title": "Using AI to Expand the Job Search of Displaced Workers in the Aftermath of the Covid-19 Crisis", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "Economics" ], "program_reference_codes": [], "program_officials": [ { "id": 639, "first_name": "Kwabena", "last_name": "Gyimah-Brempong", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2023-08-01", "end_date": "2025-07-31", "award_amount": 503800, "principal_investigator": { "id": 27669, "first_name": "Achyuta", "last_name": "Adhvaryu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 27668, "first_name": "Anant", "last_name": "Nyshadham", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 169, "ror": "", "name": "Regents of the University of Michigan - Ann Arbor", "address": "", "city": "", "state": "MI", "zip": "", "country": "United States", "approved": true }, "abstract": "This award will support research that uses artificial intelligence (AI) and machine learning to improve matching of low skilled workers to available jobs. While the US job market recovered well from the COVID 19 pandemic disruption, the unemployment rate remains relatively high for under-represented groups even as unfilled vacancies have risen and stayed high. This suggests a mismatch between the information available to jobseekers and employers. This research will use field experimental methods to investigate whether AI-assisted algorithmic matching of skills and psychometric skills to on-line vacancies can help job seekers get better job matches. This research project has the potential to improve the functioning of the labor market for workers at the lower end of the skill distribution, hence increase employment for this group of workers. Besides providing evidence on the mechanisms through which AI and computational algorithms can be used to improve labor market efficiency, the results of this research can provide guidance on policies to increase employment, labor productivity, and economic growth. Because the research focuses on low wage workers, the results can decrease poverty as well as decrease income inequality and help establish the US as a global leader in poverty reduction policies. \n\nThis project leverages data on job seeker characteristics and the requirements of jobs posted by online job search engines and use a randomized control trial (RCT) design to investigate whether assigning an AI-assisted algorithmic matching to job postings improves job matching. The RCT design has three treatment arms: (i) job offers in the job-seekers field in no particular order; (ii) vacancies in no particular order but with predicted match quality; and (iii) vacancies sorted to best match backgrounds and skills of jobseekers with predicted match quality. The control group are jobs listed in no particular order and without a match quality attached. The RCT will have a sample of 2600 job seekers, with heterogeneity across gender and space, recruited via advertisements posted on Monster.com’s social media accounts. Comparison of the first group with the control group will answer the questions of whether treatment reduces search cost hence improves matching, while comparing outcomes for the second and third arms to those of the control group and the first arm will answer the questions as to whether treatment help to lower the cost of incomplete information and improves job match. Besides providing evidence on the mechanisms through which AI and computational algorithms can be used to improve the functioning of labor markets, the results of this research project can provide guidance on policies to increase employment, increase productivity, and economic growth.\n\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "10932", "attributes": { "award_id": "5F32AG071110-02", "title": "Using an Online Video Game to Predict Functional and Cognitive Decline within the MindCrowd Electronic Cohort", "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": 20821, "first_name": "JONATHAN W.", "last_name": "KING", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2024-12-31", "award_amount": 69802, "principal_investigator": { "id": 23627, "first_name": "Andrew", "last_name": "Hooyman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 912, "ror": "", "name": "ARIZONA STATE UNIVERSITY-TEMPE CAMPUS", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 912, "ror": "", "name": "ARIZONA STATE UNIVERSITY-TEMPE CAMPUS", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite the best scientific efforts, no new treatment for Alzheimer’s Disease (AD) has been approved by the FDA since 2003. However, this may not be due to the treatment under investigation but rather clinical heterogeneity within the study sample, as a subset of participants may not have AD or experience little to no decline in AD symptoms. Thus, to improve the likelihood of success, AD clinical trials must homogenize or enrich their study sample with individuals who will experience more rapid decline and are biologically- confirmed with AD. One enrichment solution is related to the degree of performance change a person experiences due to repeated exposure of a screening assessment, known as a practice effect. Practice effects can be used to inform prognosis, diagnosis and treatment response in AD. We have recently designed an online video game, called SuperG, that uses finger coordination to assess individual practice effects without supervision in less than 7 minutes. We intend to deploy our online video game into MindCrowd, an electronic cohort of >100,000 participants worldwide designed with the infrastructure for remote, large-scale, and widely- distributed research to discover and study early biomarkers of AD. The long-term goal of this project is to merge the PI’s experience in learning and video game development with his interest in AD-focused research to enhance his career in creating next-generation, ‘crowd-sourced’ screening procedures to enrich the AD clinical trial enterprise. The overall objective of this application is to utilize the MindCrowd electronic cohort to determine how learning capacity, assayed with SuperG, relates to changes in cognition and daily function over time, while providing valuable mentorship for the PI in motor-cognitive interactions, electronic cohorts and practice effects in the context of aging. Based on extensive published and pilot work from the mentorship team, the central hypothesis is that practice effects on SuperG will predict one-year changes in cognition and daily function among MindCrowd older adults. Since SuperG game play is easily collected online, the rationale for this proposed research in a distributed electronic cohort offers an affordable and efficient means to enrich clinical trials in AD. There are two independent aims within this proposal. First, we will determine the extent that SuperG practice effects predict one-year cognitive change in older adults. Second, we will determine the extent that SuperG practice effects predict one-year functional change in older adults. If successful, this project will provide cognitive aging research with a novel online screening tool that has the potential to enrich future Alzheimer’s Disease and Related Dementia clinical trials. This project also incorporates the PI’s career goals and training activities concerning: motor-cognitive interactions, electronic cohorts, and practice effects, together providing for independence in the establishment of a “virtual” lab with modern capabilities. Further, the ‘socially-distanced’ nature of this project is particularly relevant in the context of COVID-19 and will allow for the safe inclusion of participants remotely.", "keywords": [ "Age", "Aging", "Alzheimer&apos", "s Disease", "Alzheimer&apos", "s disease related dementia", "Alzheimer’s disease biomarker", "Behavioral", "Biological Assay", "COVID-19", "Characteristics", "Clinical Trials", "Cognition", "Cognitive", "Cognitive aging", "Collaborations", "Consent", "Data", "Diagnosis", "Education", "Elderly", "Electronics", "FDA approved", "Fingers", "Future", "Goals", "Health", "Home", "Impaired cognition", "Individual", "Infrastructure", "Investigation", "Learning", "Life Style", "Measures", "Mentorship", "Modernization", "Motor", "Nature", "Neuropsychology", "Paired-Associate Learning", "Participant", "Patient Self-Report", "Pattern", "Performance", "Persons", "Play", "Process Assessment", "Prognosis", "Public Health", "Publishing", "Questionnaires", "Research", "Risk Factors", "Sampling", "Sampling Studies", "Screening procedure", "Signal Transduction", "Social Distance", "Supervision", "Surveys", "Symptoms", "Techniques", "Testing", "Time", "Training Activity", "Video Games", "Vision", "Work", "arm", "career", "clinical heterogeneity", "cognitive change", "cognitive function", "cognitive testing", "cohort", "crowdsourcing", "daily functioning", "design", "early detection biomarkers", "efficacy trial", "experience", "functional decline", "game development", "handheld mobile device", "improved", "innovation", "interest", "next generation", "non-demented", "novel", "recruit", "screening", "sex", "success", "tool", "treatment response", "virtual laboratory" ], "approved": true } }, { "type": "Grant", "id": "7817", "attributes": { "award_id": "1F32AG071110-01A1", "title": "Using an Online Video Game to Predict Functional and Cognitive Decline within the MindCrowd Electronic Cohort", "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": 20821, "first_name": "JONATHAN W.", "last_name": "KING", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2022-01-01", "end_date": "2024-12-31", "award_amount": 66390, "principal_investigator": { "id": 23627, "first_name": "Andrew", "last_name": "Hooyman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 912, "ror": "", "name": "ARIZONA STATE UNIVERSITY-TEMPE CAMPUS", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 912, "ror": "", "name": "ARIZONA STATE UNIVERSITY-TEMPE CAMPUS", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite the best scientific efforts, no new treatment for Alzheimer’s Disease (AD) has been approved by the FDA since 2003. However, this may not be due to the treatment under investigation but rather clinical heterogeneity within the study sample, as a subset of participants may not have AD or experience little to no decline in AD symptoms. Thus, to improve the likelihood of success, AD clinical trials must homogenize or enrich their study sample with individuals who will experience more rapid decline and are biologically- confirmed with AD. One enrichment solution is related to the degree of performance change a person experiences due to repeated exposure of a screening assessment, known as a practice effect. Practice effects can be used to inform prognosis, diagnosis and treatment response in AD. We have recently designed an online video game, called SuperG, that uses finger coordination to assess individual practice effects without supervision in less than 7 minutes. We intend to deploy our online video game into MindCrowd, an electronic cohort of >100,000 participants worldwide designed with the infrastructure for remote, large-scale, and widely- distributed research to discover and study early biomarkers of AD. The long-term goal of this project is to merge the PI’s experience in learning and video game development with his interest in AD-focused research to enhance his career in creating next-generation, ‘crowd-sourced’ screening procedures to enrich the AD clinical trial enterprise. The overall objective of this application is to utilize the MindCrowd electronic cohort to determine how learning capacity, assayed with SuperG, relates to changes in cognition and daily function over time, while providing valuable mentorship for the PI in motor-cognitive interactions, electronic cohorts and practice effects in the context of aging. Based on extensive published and pilot work from the mentorship team, the central hypothesis is that practice effects on SuperG will predict one-year changes in cognition and daily function among MindCrowd older adults. Since SuperG game play is easily collected online, the rationale for this proposed research in a distributed electronic cohort offers an affordable and efficient means to enrich clinical trials in AD. There are two independent aims within this proposal. First, we will determine the extent that SuperG practice effects predict one-year cognitive change in older adults. Second, we will determine the extent that SuperG practice effects predict one-year functional change in older adults. If successful, this project will provide cognitive aging research with a novel online screening tool that has the potential to enrich future Alzheimer’s Disease and Related Dementia clinical trials. This project also incorporates the PI’s career goals and training activities concerning: motor-cognitive interactions, electronic cohorts, and practice effects, together providing for independence in the establishment of a “virtual” lab with modern capabilities. Further, the ‘socially-distanced’ nature of this project is particularly relevant in the context of COVID-19 and will allow for the safe inclusion of participants remotely.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "14187", "attributes": { "award_id": "2100027", "title": "Using Cloud Technologies to Develop the Data Analysis Skills of Community College Students", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Directorate for STEM Education (EDU)", "Advanced Tech Education Prog" ], "program_reference_codes": [], "program_officials": [ { "id": 1983, "first_name": "Paul", "last_name": "Tymann", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-09-01", "end_date": null, "award_amount": 299994, "principal_investigator": { "id": 30760, "first_name": "Esma", "last_name": "Yildirim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 30760, "first_name": "Esma", "last_name": "Yildirim", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 1096, "ror": "", "name": "CUNY Queensborough Community College", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "The greater New York City (NYC) area is one of the world’s leading financial and cultural centers. High-tech jobs are a key driver of NYC’s place in the US economy and the need for high tech workers is growing. From 2008 to 2018, jobs in this sector rose 45%, with jobs in the data analytics category representing 30% of that total growth. The current rate at which students graduate with skills in data analytics is not keeping pace with the demand for skilled data analytics technicians. This project aims to increase the number of students graduating with the skills necessary to enter the data analytics workforce. It will do so by improving the data analytics knowledge and skills of students at a diverse urban community college. The project intends to increase the participation of women and individuals from communities that are not yet equitably represented in the technical workforce. It is expected that the project will help prepare students for the predicted employment opportunities and contribute to better productivity, problem solving, and effectiveness in the workplace.<br/><br/>Working in collaboration with the Business Industry Leadership Team at Queensborough Community College, the project team intends to enhance the ability of the College to recruit, educate, and graduate a diverse group of students to help meet regional employment needs. Participating students will be offered a summer boot camp in Data Science/Analysis, a year-long undergraduate research experience following the summer camp, and a series of workshops focused on skills for internship applications and job interviews. To meet role models and increase their sense of belonging in Data Science/Analysis, students will also participate in a seminar series focused on Data Science/Analysis careers that will feature professionals from diverse backgrounds and from both academia and industry. The project will also offer preparation for the AWS Certified Cloud Practitioner certification. It is expected that this initiative will lay the foundation to establish a degree program in Data Science/Analysis at Queensborough Community College. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy.<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": 1386, "pages": 1419, "count": 14184 } } }