Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1424&sort=-awardee_organization
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After the virus infects a cell, the viral replicase enzyme makes thousands of copies of the viral genome. This project will determine the molecular features of the SARS-CoV-2 RNA genome that enable it to be copied by the coronavirus replicase complex. Having determined those features, the PIs will use that knowledge to design genome-like molecules that may interfere with replication of normal genomes and thus have potential as novel therapeutic agents. The PIs will evaluate the effectiveness of the genome-like molecules at blocking virus replication in cells and in more complicated tissue culture that includes a variety of different cell types meant to mimic the conditions in the human lung. Additionally, the project will broaden participation in STEM by funding a Latina post-doctoral scholar to perform most of the experiments.The PIs will use a combination of biochemistry and culture to determine the minimal features of a SARS-CoV-2 replicon that enable it to be replicated. Next, the PIs will apply that knowledge to design defective-interfering RNA that can be replicated very efficiently yet does not encode infectious virus particles. Extending from natural defective-interfering particles in other viruses, this RNA will likely serve as a template for the coronavirus replicase. When an intact virus penetrates a cell that contains defective-interfering RNA, the defective interfering RNA will serve as a template for the virus replicase. Doing so will interfere with viral replication by occupying the replicase and consuming nucleotides that could otherwise be incorporated into new intact virus genomes. The interfering RNAs will be delivered using a virus-like particle system designed to enter cells expressing the SARS-CoV-2 receptor and the protease necessary for SARS-CoV-2 entry. The PIs will determine the extent to which defective-interfering particles block replication in simple tissue culture or in human airway epithelial organoids, which have multiple differentiated cell types iorganized similarly to those found in explanted tissue. The epithelial organoids are derived from healthy human tissue donors and represent some of the genetic diversity in human populations. Broader impacts include the potential for basic understanding of the interfering particles to contribute to developing similar particles to treat or prevent COVID-19.This RAPID award is made by the Genetic Mechanisms Program in the Division of Molecular and Cellular Biosciences to respond to the COVID-19 pandemic.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1591", "attributes": { "award_id": "2034234", "title": "EAGER: High-throughput early detection and analysis of COVID-19 plaque formation using time-lapse coherent imaging and deep learning", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7916" ], "program_officials": [ { "id": 4170, "first_name": "Steve", "last_name": "Zehnder", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-15", "end_date": "2022-08-31", "award_amount": 299947, "principal_investigator": { "id": 4172, "first_name": "Aydogan", "last_name": "Ozcan", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 4171, "first_name": "Hatice Ceylan", "last_name": "Koydemir", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Plaque assays are widely used for measuring the infectious concentration of viral samples and form a very important tool for vaccine development, especially for the evaluation of the performance of new vaccines at the exploratory and preclinical stages. This standard method is laborious and takes days to get the results, and is subject to human errors since it depends on manual plaque counting. Molecular techniques such as polymerase chain reaction (PCR or reverse transcription PCR) and western blots can be used to quantify the viral genome. However, none of these methods provide information about the infectivity of the virus and cannot measure plaque forming units. This proposal aims to create a computational sensor platform for accelerated testing of SARSCoV-2 viability and infectivity using deep learning-based plaque assays and achieve accurate and automated plaque forming unit (PFU) measurements within hours as opposed to days with standard plaque assays. The proposed computational imaging system will periodically capture coherent microscopic images of the cytopathogenic effects of viruses on cell cultures and analyze these time lapsed holographic images using deep neural networks (DNNs) for rapid detection of viral destruction of the cell monolayer. In addition to early and automated detection of plaque forming units, this unique platform will further make use of deep learning for high-throughput holographic image reconstruction of the assay volume to perform tile-scan imaging of the entire well plate within 5 min, corresponding to an imaging throughput of ~50 cm2/min. Powered by deep learning, this automated and cost-effective viral plaque monitoring platform can be transformative for a wide range of applications in microbiology and virology by significantly reducing the detection time without labeling or the need for an expert, or manual inspection. The project will also establish a complementary educational outreach program that will involve (1) public interviews and popular science articles in news media and internet; (2) undergraduate research opportunities in the PI’s laboratory involving minority students; and (3) graduate student training through organization of workshops, seminars and conferences. Furthermore, research projects, seminars and open house visits will serve undergrads and high school students (especially from minority groups) to interact with a cutting edge research environment, helping to increase their scientific curiosity and shaping their career goals in science and engineering.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": "1899", "attributes": { "award_id": "2027438", "title": "RAPID: Analysis of Multiscale Network Models for the Spread of COVID-19", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)" ], "program_reference_codes": [ "096Z", "7914", "9263" ], "program_officials": [ { "id": 5035, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-04-15", "end_date": "2022-03-31", "award_amount": 200000, "principal_investigator": { "id": 5037, "first_name": "Andrea L", "last_name": "Bertozzi", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5036, "first_name": "Mason A", "last_name": "Porter", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The current pandemic of coronavirus disease 2019 (COVID-19) has upended the daily lives of more than a billion people worldwide, and governments are struggling with the task of responding to the spread of the disease. Uncertainty in transmission rates and the outcomes of social distancing, \"shelter-at-home\" executive orders, and other interventions have created unprecedented challenges to the United States health care system. This project will address these issues directly using advanced mathematical modeling from dynamical systems, stochastic processes, and networks. The mathematical models, which are formulated with the specific features of COVID-19 in mind, will provide insights that are critical to people on the front lines who need to make recommendations for intervention strategies and human-behavior patterns to best mitigate the spread of this disease in a timely manner. The project will train a postdoctoral scholar, a PhD student, and two undergraduate students in the research needed to solve these complex problems. The standard approach for epidemic modeling, at the community scale and larger, is compartmental models in which individuals are in one of a small number of states (for example, susceptible, infected, recovered, exposed, latent), with individuals moving between states. The COVID-19 epidemic can be modeled in this way, with resistance as part of the dynamics. The simplest examples of such models for large populations are coupled ordinary differential equations that describe the fraction of a population in each of the states. To model the stochasticity of infection and latency, models with self-exciting point processes can be fit to real-world data. This project compares the dynamical systems and stochastic models of relevance to COVID-19 transmission. The models also incorporate network structure for the transmission pathways. The project extends prior research on contagions on multilayer networks by incorporating multiple transmission methods and coupling between the spread of the contagion itself and human behavior patterns. The project leverages high-resolution societal mixing patterns in epidemics, as they influence both (1) observations and demographics of who has been diagnosed with COVID-19 and (2) who transits the disease, sometimes without being diagnosed.This award is co-funded with the Applied Mathematics program and the Computational Mathematics program (Division of Mathematical Sciences), and the Office of Multidisciplinary Activities (OMA) program.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": "1934", "attributes": { "award_id": "2031187", "title": "RAPID: Dynamic Graph Neural Networks for Modeling and Monitoring COVID-19 Pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "096Z", "7364", "7914", "9251" ], "program_officials": [ { "id": 5137, "first_name": "Wei", "last_name": "Ding", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2023-05-31", "award_amount": 114000, "principal_investigator": { "id": 5138, "first_name": "Wei", "last_name": "Wang", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "The novel coronavirus, COVID-19, has become one of the biggest pandemics in human history and has generated lasting impacts on public health, society, and economy. The number of cases in the United States has passed 1 million with a total number of deaths over 50 thousand. There is an urgent need for research and development that can bring a predictive understanding of the spread of the virus, thereby enabling mitigation methods to alleviate the negative effects of COVID-19. Traditional epidemiological models usually take into consideration only a small number of features in building a prediction model, which may not be able to capture potential risk factors and effects of various intervention mechanisms of this new pandemic. In this project the investigators develop novel machine learning methods that can simultaneously model and predict the COVID-19 spread, detect and monitor risk factors, and evaluate effectiveness of interventions over time and space. The new model ingests and integrates heterogeneous and rapidly accumulating data across diverse sources, such as publications, news, census, social media, and outbreak observation trackers. It employs a new contextualized language model to accurately recognize named entities and relations from vast text data and build knowledge graphs to extract potential risk factors. A dynamic graph is constructed. Each location node may have a set of static and time-dependent attributes. Events, individual behaviors, social activities, interventions are mapped to activity nodes with edges connecting to the corresponding location nodes at the time. A novel dynamic graph neural network is trained to perform joint predictions of all locations over time. Activity nodes of significant attention weights represent major risk factors or effective intervention mechanisms. The project will result in public dissemination of the prediction model and all source codes, immediately benefiting the combat against COVID-19.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": "1964", "attributes": { "award_id": "2034809", "title": "RAPID: Social isolation during COVID-19: Effects on fear learning and implications for trauma", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)" ], "program_reference_codes": [ "096Z", "1332", "7914" ], "program_officials": [ { "id": 5231, "first_name": "Steven", "last_name": "Breckler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-15", "end_date": "2021-05-31", "award_amount": 116722, "principal_investigator": { "id": 5232, "first_name": "Naomi I", "last_name": "Eisenberger", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "In response to COVID-19, people across the United States have experienced extreme and abrupt shifts in their level of daily contact with others, forcing many people into a state of social isolation. This project examines whether the experience of social isolation amplifies the development of harmful and persistent fears in humans. The research further tests if social isolation reduces the ability to eliminate those fears once learned. This study utilizes a unique situational event (social isolation due to COVID-19), which cannot be replicated in the laboratory, to provide rare insight into how social isolation heightens the development of fear and if experiences of social connection can reverse these effects. This research will contribute to the growing understanding of the effects of social isolation on increasing fears and influencing the developmental course of trauma; it may also provide simple strategies to mitigate those effects.Research suggests that social isolation may influence long-term trauma by directly influencing fear learning processes. Socially isolated animals exhibit increases in fear responses that persist even when a threat is no longer present, are more likely to develop PTSD-like symptoms following trauma. Similarly, research suggests that socially isolated humans are at a greater risk of developing PTSD. To date, however, no work has directly examined the impact of extreme social isolation on fear learning in humans. Preliminary data suggests that feeling socially isolated appears to lead to persistent fears and poorer fear extinction, although these effects may be eliminated when individuals are reminded of their social support during fear extinction. This project will evaluate these hypotheses by sampling people from across the U.S. living in places where some form of ‘Stay at Home’ order is in place. The research examines whether the extreme situational social isolation brought about by COVID-19 leads to increased development of fear associations for neutral images paired with negative affective stimuli. The study also examines whether a two-week social connection intervention, during which individuals will be asked to do positive things for others to promote feelings of connectedness, improves fear extinction and decreases the development of fear associations in those most severely isolated. The findings of this work have the potential to shed light on how social isolation influences the course of trauma and reveal simple, low-cost, and accessible interventions to reduce this trauma that can be used immediately and in the future to ease the harmful side effects of necessary measures to address COVID-19 and similar events.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": "2013", "attributes": { "award_id": "2032310", "title": "RAPID: Biomimicry of SARS-CoV-2 and its consequences for infectivity and inflammation", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)" ], "program_reference_codes": [ "096Z", "7465", "7914" ], "program_officials": [ { "id": 5384, "first_name": "Steve", "last_name": "Clouse", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-07-01", "end_date": "2021-06-30", "award_amount": 200000, "principal_investigator": { "id": 5385, "first_name": "Gerard", "last_name": "Wong", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This research addresses two critically important questions concerning the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the novel coronavirus disease (COVID-19): Why is this virus so infectious, and why does it sometimes cause lethal inflammation? It is now well-known that COVID-19 infections can spread from hosts early, before onset of symptoms, which implies efficient viral entry and egress. The root causes of COVID-19 induced inflammation are less known, but cognate effects have been seen in other superviruses such as the coronavirus for the original severe acute respiratory syndrome (SARS), and the 1918 pandemic influenza virus. This research investigates the basic molecular mechanisms of how parts of this virus can mimic the functions of defensive molecules from the human innate immune system which restructure membranes and control inflammation, thereby allowing the virus to achieve both of the above effects. The multidisciplinary nature of the research will also provide training opportunities for students at multiple levels.The overall goal of this project is to perform basic research that may help mitigate the current COVID-19 crisis by using artificial intelligence informed fundamental biophysics, immunology, and virology. The project objectives are two-fold. 1: use machine learning methods to identify molecules encoded in the genome of the SARS-CoV-2 virus that remodel the membrane for viral entry and viral shedding, and design ways to turn off this activity by mimicking molecules found in bats, which harbor many coronaviruses and therefore have more evolved defenses against them. 2: Identify molecules from the SARS-CoV-2 virus that mimic molecules from the human innate immune system which are known to mediate potent inflammatory responses, thus allowing potential downstream design of molecular approaches to alleviate this type of inflammation. These ideas will be tested using small angle x-ray scattering (SAXS) experiments performed at state-of-the-art 3rd generation synchrotron radiation facilities in the US, as well as traditional immunological and antiviral experiments, including those done at high containment biosafety level 3 facilities designed to handle SARS-CoV-2. Expected results from this basic research project can provide translational guidance for design of COVID-19 treatment strategies, even if current vaccine candidates fail.This RAPID award is made by the Cellular Dynamics and Function Program in the Division of Molecular and Cellular Biosciences, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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": "2108", "attributes": { "award_id": "2032533", "title": "RAPID: Collaborative Research: Low-Cost, Non-invasive, Fast Sample Collection System for COVID-19 Viral Load Level Diagnosis: Point-of-Care and Environmental Testing", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)" ], "program_reference_codes": [ "096Z", "7914" ], "program_officials": [ { "id": 5677, "first_name": "Ron", "last_name": "Joslin", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-06-01", "end_date": "2021-12-31", "award_amount": 90000, "principal_investigator": { "id": 5680, "first_name": "Hossein P", "last_name": "Kavehpour", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 5678, "first_name": "Robert N", "last_name": "Candler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 5679, "first_name": "Nasim", "last_name": "Annabi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Pandemics can have a devastating impact on societies: collapsing local economies, halting trade, weakening national security and overwhelming healthcare capacity. To combat infectious pandemics, rapid, effective diagnostic testing combined with contact tracing and quarantine is needed. This will help officials manage infections while minimizing the effect of the disease on the economy, on society, and on our healthcare system. Further, effective sentinel monitoring of local environments can detect the presence of dangerous levels of virus, preventing mass spreading events. Unfortunately, the COVID-19 pandemic has exposed a critical weakness in health care security infrastructure: the deficiency in our ability to conduct rapid, simple, point-of-care diagnostic and environmental sample collection and testing. The goal in this research is to develop inexpensive, massively deployable rapid diagnostic and sentinel systems for detecting respiratory illness and airborne viral threats. Because the virus is transmitted through droplets in the breath, this system is expected to collect enough sample from one minute of breathing to be used in existing testing units. This technology is based on continuous dropwise condensation (CDC) which is capable of efficiently extracting particulate (viral) loads from humidified air within a minute. The collection system and supporting instrumentation is simple and can be readily integrated with a well-designed patient interface that is non-invasive, compatible with current rt-PCR sampling and which can be mass produced cheaply. Additionally, CDC, due to its surface collection method, can be modified readily into either a point-of-care, rapid diagnostic test, or into an environmental sentinel sampling/testing system.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": "2283", "attributes": { "award_id": "2113879", "title": "Collaborative Research: PIPP Workshop: Pandemic Readiness for Emerging Pathogens(PREP) to be Held February 15-19, 2021.", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Engineering (ENG)", "CCSS-Comms Circuits & Sens Sys" ], "program_reference_codes": [], "program_officials": [ { "id": 6261, "first_name": "Svetlana", "last_name": "Tatic-Lucic", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2021-02-01", "end_date": "2023-01-31", "award_amount": 31442, "principal_investigator": { "id": 6262, "first_name": "Brian", "last_name": "Wood", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This award will fund the Pandemic Readiness for Emerging Pathogens (PREP) workshop, planned for February 15-19, 2021. The PREP workshop will foster scientific discussion and catalyze innovation and partnerships to enhance our understanding of the challenges and potential solutions to rapid detection and assessment of emerging pathogens and infectious disease dynamics from the molecular to the ecological scale. Four panel topics are planned for this workshop: (1) The Challenges in Rapid and Accurate Detection and Assessment of Emerging Pathogens; (2) Approaches to Rapid and Accurate Detection and Assessment of Emerging Pathogens; (3) Monitoring Animal and Human Movements, High-Risk Interfaces for Disease Transmission, and At-risk Communities; (4) Data-Intensive Machine Learning and Modelling for Pandemic Preparedness. By integrating expertise in new sensor engineering, epidemiology, wildlife, and human health, ecology, quantitative social sciences, evolutionary biology, engineering, and computer science, new approaches in modeling, computational mathematics, data-enabled predictive science and decision making, and real-time intelligent sensing will be identified to transform infectious disease outbreak prediction capabilities. Subsequent to the workshop, the organizers plan to prepare reports summarizing the workshop for publication in high-profile general science outlets which can be used to inform the development of predictive intelligence for pandemic prevention.\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": "2552", "attributes": { "award_id": "2027277", "title": "ATD: Algorithms for Threat Detection in Knowledge Graphs", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "ATD-Algorithms for Threat Dete" ], "program_reference_codes": [], "program_officials": [ { "id": 7336, "first_name": "Huixia", "last_name": "Wang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 607050, "principal_investigator": { "id": 7338, "first_name": "Andrea", "last_name": "Bertozzi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 7337, "first_name": "P. Jeffrey", "last_name": "Brantingham", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "This project develops new mathematical algorithms and models involving knowledge graphs. A knowledge graph represents what is known about a subject in the form of labeled nodes and edges. More than simply labeled data, knowledge graphs organize data according to high-level meanings and assign globally unique identification to each node in the graph to match real-world entities. Much work on knowledge graphs treats databases and queries. In contrast, in the context of threat detection, this project focuses on algorithms that identify latent information in the graph and predictive models associated with data on the graph. The project will involve a combination of mathematical methods for subgraph isomorphism detection, time series analysis, agent-based and multiscale modeling, and pattern recognition. The project will train a postdoctoral scholar, PhD student, and six undergraduate researchers through involvement in the research.\n\nThis project brings together several different focused problems with large, multimodal, complex datasets. The data is organized into a knowledge graph in which additional information is added and absorbed as it becomes available. This project considers three types of knowledge graphs each for different applications: (1) knowledge graphs constructed from complex multi-part narratives; (2) knowledge graphs constructed from heterogeneous online content; and (3) knowledge graphs associated with large-scale human interaction dynamics such as a global pandemic. For (1), algorithms will be designed to identify important causal subgraphs. For (2), the project aims to identify threats in space and time based on templated patterns. For (3), desired goals are both a predictive ability for actions from a micro to macro scale along with tools to assess potential impact versus cost of preventative measures, from local to regional to country-wide scale.\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": "2558", "attributes": { "award_id": "2002313", "title": "Non-Markovian Diffusion Imaging", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "Chemical Measurement & Imaging" ], "program_reference_codes": [], "program_officials": [ { "id": 7356, "first_name": "Kelsey", "last_name": "Cook", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-09-01", "end_date": "2023-08-31", "award_amount": 399932, "principal_investigator": { "id": 7357, "first_name": "Louis", "last_name": "Bouchard", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 151, "ror": "", "name": "University of California-Los Angeles", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "With support from the Chemical Measurement & Imaging program and co-funding from the Atomic, Molecular, and Optical Physics - Experiment and Theory programs, Professor Bouchard at the University of California, Los Angeles is developing new methods to study gaseous diffusion - the random motions of molecules in gases. In traditional models of these processes, a snapshot (state) at any given moment is assumed to be independent of past collisions between molecules swimming in the gas. However, if the picture can be taken quickly enough, it is possible to detect molecules’ \"memory\" of collisions from the recent past, and thus to investigate the nature of surfaces encountered by the molecules. Dr. Bouchard's group is developing both experimental methods and the underlying theory to advance understanding in systems (e.g., catalysts and lungs) where the memory of these collisions are important. They use nuclear magnetic resonance (NMR) spectroscopy, the tool at the heart of Magnetic Resonance Imaging (MRI, an important medical diagnostic tool) which avoids the use of harmful ionizing radiation like x-rays. Graduate and undergraduate students engaged in this research receive broad interdisciplinary training, and contribute to the development of relevant introductory chemistry materials that will be made freely available to the public. \n\nSelf-diffusion in a dense gas of neutral molecules is non-Markovian and must be modeled by a Langevin equation with memory kernel. The Bouchard group has obtained experimental NMR spectroscopy results that are consistent with a generalized Langevin description, as confirmed by an unexpected temperature dependence of the NMR linewidth as well as dependence on inter-pulse spacing during Carr-Purcell-Meiboom-Gill (CPMG) experiments. While the new theory describes the results of free self-diffusion well, the diffusion behavior in the presence of boundaries has not yet been explored. Dr. Bouchard is now probing restricted diffusion in porous media possessing various types of boundaries, and developing new extensions of the underlying theory based on the stochastic calculus of bounded diffusions (sticky, reflecting, killing, and absorbing boundaries). The new NMR-based methods may shed new light into the kinetic theory of gases, benefitting chemical physics and medical imaging by offering new tools to extract information about pore structure and function in media such as porous rocks, soils, or lungs. The tools also show promise for both characterization of catalytically reactive surfaces and mass transport during reactions and provision of a better understanding of hyperpolarized gas MRI. Educational impacts will derive from the training and active participation of multiple students in the research as well as the creation of free online educational course materials disseminated via the institution's web site.\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 } } ], "meta": { "pagination": { "page": 1424, "pages": 1442, "count": 14420 } } }