Grant List
Represents Grant table in the DB
GET /v1/grants?page%5Bnumber%5D=1384&sort=-end_date
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SARS-CoV-2, the cause of COVD-19, is a zoonotic disease, and the COVID-19 pandemic has highlighted the importance of both zoonotic diseases, and mutual trust between public health institutions and the public whose health they are intended to safeguard. To effectively control zoonoses, we need a better understanding of exactly how they are transmitted, and how trust—and its absence—influences that transmission. Brucellosis is a zoonosis caused by a bacteria that is present worldwide, including the US. The most serious form is caused by the bacteria Brucella melitensis, which is spread by sheep and goats when a person drinks or eats milk or cheese that hasn’t been pasteurized, or when people assist a sheep or goat who is giving birth. In animals, the disease causes pregnancy losses and reduced milk production. In humans, the disease also causes pregnancy losses, as well as fever, headaches, back pain, physical weakness, and fatigue that can last for months or even years. In some cases, severe neurological and heart effects can also be seen. The project leverages the strong US-Israel research collaboration to advance the knowledge of the more-than-bio-physical drivers of interspecies disease transmission, focusing on Brucella melitensis but generalizable to other zoonotic diseases. <br/><br/>This project works with Bedouin communities in southern Israel, where Brucella burden is among the highest in the world, second only to Syria pre-war and likely worsening since. These communities exhibit extremely high levels of institutional distrust and experience ongoing urbanization. This provides a model setting for examining how distrust, urbanization, and zoonoses—a triad being replicated throughout the world—collectively impact humans, animals, and livelihoods. The research tests the hypothesis that institutional distrust and population displacement to urban centers increase the density of human-animal contact networks, facilitating the transmission of brucellosis. Objective 1 aims to measure human-animal contact networks among six Bedouin communities in southern Israel using qualitative data, quantitative data, and experience-based knowledge. These data support Objective 2 to model synthetic human-animal networks and develop a new method for generating Brucella genomes, applied to samples collected from humans, livestock, and environments. Subsequent tasks for Objective 3 include fitting and validating an epidemic network model using these synthetic networks and Brucella genomes and applying this model to test the research hypothesis by exploring counterfactual scenarios defined by distrust and urbanization, developed through participatory methods. These methods and insights afford broad applicability beyond this empirical setting, to other Brucella systems and zoonotic diseases throughout the world.<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": "15111", "attributes": { "award_id": "2419913", "title": "Collaborative Research: Understanding the Time Use, Role Behaviors, and Wellbeing of Employees Working From Home", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Social, Behavioral, and Economic Sciences (SBE)", "SBP-Science of Broadening Part" ], "program_reference_codes": [], "program_officials": [ { "id": 30591, "first_name": "Songqi", "last_name": "Liu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 112625, "principal_investigator": { "id": 3658, "first_name": "Kristen", "last_name": "Shockley", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 160, "ror": "", "name": "University of Georgia Research Foundation Inc", "address": "", "city": "", "state": "GA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 273, "ror": "https://ror.org/02v80fc35", "name": "Auburn University", "address": "", "city": "", "state": "AL", "zip": "", "country": "United States", "approved": true }, "abstract": "Technology first spurred a telecommuting movement in the 1970s. The COVID-19 pandemic accelerated the movement into highspeed. The future of work includes a substantial number of workers who will work from home. Moreover, organizations continue to grapple with how to meet business demands while also accommodating employees who wish to work remotely. These changes have prompted the need to better understand fully remote and hybrid (i.e., some days in the office and some days from home) work modes and the needs of those who work from home. As such, the project team is analyzing existing daily time use data as well as collecting additional data intended to shine a light on how the location of work alters daily activities (e.g., exercise, sleep), transitions (e.g., switching from work to nonwork roles), and overall employee wellbeing. The findings will be used to provide actionable insights to managers and inform the development of interventions to support remote workers.<br/> <br/>The project’s objective is to reveal a better understanding of the day-to-day experiences associated with hybrid work and how those experiences differ from work done away from home. The project involves two studies. Study 1 is a time use study based on data from the American Time Use Study that enables the research team to examine time use (activity and location) sequence patterns and transitions in 15-minute increments across the course of a 24-hr period. Study 2 is a 10-day experience sampling study that includes multiple psychological assessments per day, coupled with objective metrics collected via wearable devices that aims to capture features of the work environment, job, and individual, as well as within-person day-to-day transitions and daily wellbeing. Results from the research will contribute to the understanding of challenges and benefits associated with the location of work and have practical implications for organizations and managers.<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": "15112", "attributes": { "award_id": "2421378", "title": "IHBEM: The evolution of human behaviors in the context of emerging diseases and novel vaccines", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "MATHEMATICAL BIOLOGY" ], "program_reference_codes": [], "program_officials": [ { "id": 1173, "first_name": "Joseph", "last_name": "Whitmeyer", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 474521, "principal_investigator": { "id": 31657, "first_name": "Nicole", "last_name": "Creanza", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 31656, "first_name": "Glenn F", "last_name": "Webb", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 189, "ror": "https://ror.org/02vm5rt34", "name": "Vanderbilt University", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "This project analyzes mathematical models that incorporate the interaction between human behavior and infection transmission of epidemic diseases. The goal is to inform public health policy decisions that are implemented when a major epidemic is spreading throughout a population. The models encompass social acceptance or resistance to public interventions such as social distancing, public closings, individual isolation, mask wearing, and vaccination. The models simulate evolving disease dynamics and address how different policies affect the control of the epidemic progression. The project advances inter-disciplinary perspectives that facilitate more accurate and applicable models of epidemic diseases. Broader Impacts of this project include a mini-unit on public health integrated in K-12 science and math courses and a bridge program. Mentorship is also an emphasis, notably in a bridge program between Masters and PhD levels to increase diversity in science.<br/><br/><br/>Three classes of models are developed: (1) Agent based models that track individual behavior connected to vaccine hesitancy and public vaccination information; (2) Multi-layered discrete time network models that access the impact of pandemic related cultural shifts and risk perception of disease spread and vaccination acceptance; (3) Compartment differential equations models that incorporate dynamic changes in individual chronological age related human behavior and individual vaccination stages. Data are obtained from the Centers of Disease Control and Prevention, the New York State Department of Health, the National Center for Immunization, and other epidemic data sources.<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": "15113", "attributes": { "award_id": "2414382", "title": "Collaborative Research: The causes and consequences of Higher Order Interactions (HOI)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Animal Behavior" ], "program_reference_codes": [], "program_officials": [ { "id": 31658, "first_name": "Kim L.", "last_name": "Hoke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 926511, "principal_investigator": { "id": 31659, "first_name": "Noa", "last_name": "Pinter-Wollman", "orcid": null, "emails": "", "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": "By developing a framework to study higher order interactions, i.e., simultaneous interactions, the funded work will provide novel tools to analyze complex systems. The COVID-19 pandemic was challenging to control because people could catch the disease from accumulating many short exposures to multiple infected people, i.e., from higher order interactions, which are rarely considered in epidemiological models. Similarly, the efficient transfer of goods was another casualty of the pandemic due to supply-chain disruptions. Higher order interactions, in which goods are exchanged simultaneously, can substantially expedite the transfer of goods and increase the robustness and resilience of supply-chains to disruptions. The general framework that will be developed in this grant will use a tractable biological system to develop mathematical tools to study the causes and consequences of higher order interactions. The mathematical models and tools developed will be general, to allow application to other systems, such as communication, disease transmission, and social learning. Public health and bioeconomics are two examples of fields that can benefit from the funded work. The work will be published in general journals with a wide interdisciplinary readership and the analysis code will be made publicly available. Both PIs have a strong track record of recruiting and facilitating the success of students from groups that are unrepresented in the sciences and this commitment to mentoring a diverse population of trainees in interdisciplinary work will continue. To further disseminate the work to the general public, podcast episodes will be produced and distributed widely. <br/><br/>Collective outcomes, such as the social behavior of animals, emerge from interactions among system components. While substantial work has been devoted to examining the intricate network of interactions among animals, these interactions are described and analyzed as dyadic events. However, multiple individuals can interact simultaneously. For example, an alarm call is broadcast to multiple individuals at once rather than through multiple one-on-one interactions. Despite the important conceptual and functional differences between dyadic and higher order interactions, there are only few methodological approaches that emphasize the higher order nature of social interactions. The proposed work will examine the causes and consequences of higher order interactions, and the feedback between them, by adapting and implementing existing mathematical tools from algebraic topology, simplicial sets, in novel ways. Specifically, the aims include to determine the conditions under which higher order interactions emerge; to examine the consequences of higher order interactions; and to investigate feedback between causes and consequences of higher order interactions to uncover potential evolutionary pathways for their emergence. Social insects are an especially powerful system for examining the questions in the proposal because of the profound fitness consequences of interactions among individuals for the group. Therefore, the proposed work will use foraging and food transmission of Argentine ants (Linepithema humile) as a model system to examine the internal and external causes and consequences of higher order interactions. Project outcomes will enable innovative approaches to fundamental and generalizable questions which are currently beyond our reach.<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": "15114", "attributes": { "award_id": "2412389", "title": "PIPP Phase II: Community Empowering Pandemic Prediction and Prevention from Atoms to Societies (COMPASS)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "PIPP-Pandemic Prevention" ], "program_reference_codes": [], "program_officials": [ { "id": 2558, "first_name": "Joanna", "last_name": "Shisler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 18000000, "principal_investigator": { "id": 31663, "first_name": "TM", "last_name": "Murali", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 5048, "first_name": "Linsey C", "last_name": "Marr", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31660, "first_name": "Anthony", "last_name": "Atala", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31661, "first_name": "Adam S", "last_name": "Lauring", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31662, "first_name": "Julie M", "last_name": "Gerdes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 244, "ror": "", "name": "Virginia Polytechnic Institute and State University", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The Center for Community Empowering Pandemic Prediction and Prevention from Atoms to Societies (COMPASS) center is a partnership among Virginia Tech, Cornell University, Meharry Medical College, the University of Michigan, and the Wake Forest University School of Medicine that seeks to address the challenges and gaps in our understanding of how pathogens may cause pandemics. COMPASS researchers will develop computational and experimental methods relevant to pandemic prediction and prevention and strive to effectively communicate scientific findings to the public. Computational models will seek to predict the barriers that prevent a pathogen from infecting a new host. In parallel, the center will seek to create new methods for designing tissue-mimetic organoids that will enable detailed investigations of how infections can harm organs while also facilitating the development of new drugs. Additional computational models developed in the center will have the ability to predict how well a pathogen survives in different environments, its potential to spread rapidly, and what disinfection strategies are most effective. COMPASS activities will tie all scientific endeavors with efforts to effectively communicate pandemic science with non-scientists and policy makers. Development of bidirectional feedback loops between researchers and community members will underlie the research conducted in the center. The center’s inclusivity efforts will lead to increased participation by underrepresented groups in educational and training activities. By bringing together a diverse set of participants to address a global challenge, the COMPASS Center hopes to achieve a higher level of public acceptance in the knowledge it gains and disseminates.<br/><br/>The COMPASS center seeks to forecast and control future pandemics by addressing the grand challenge of uncovering the genetic, molecular, cellular, and chemical rules of life underlying pathogen-host interactions through community-based and ethically grounded research. COMPASS researchers will create foundation machine learning models that address how a pathogen may lower host barriers to infect a cell, how it persists in the environment, and how drugs that have already been approved may be utilized to treat infections. In parallel, COMPASS scientists will generate novel organoid systems to serve as robust platforms to study pathogen life cycles and to test therapies. The focus on ethically grounded research will result in COMPASS scientists who can effectively communicate complex research to non-technical audiences, work with vulnerable groups to identify key ethical and equity concerns of pandemic research and use community-academic feedback to uniquely reflect reciprocal knowledge exchange between researchers and the public. The outcomes of COMPASS foundational research will inform a diverse set of use-inspired research projects across industry, federal agencies, and international organizations, resulting in a robust public-private ecosystem to provide solutions to diverse problems in pandemic science. The COMPASS Center will incorporate robust education and training plans to actively build the next generation of talent for a diverse workforce ready to deal with pandemic threats. Through multiple innovative approaches, COMPASS will empower varying age groups of future professionals and the public in pandemic science.<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": "15115", "attributes": { "award_id": "2421289", "title": "IHBEM: No One Lives in a Bubble: Incorporating Group Dynamics into Epidemic Models", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Mathematical and Physical Sciences (MPS)", "Human Networks & Data Sci Res" ], "program_reference_codes": [], "program_officials": [ { "id": 622, "first_name": "Zhilan", "last_name": "Feng", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 1000000, "principal_investigator": { "id": 31666, "first_name": "Babak", "last_name": "Heydari", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 4270, "first_name": "Daniel", "last_name": "O'Brien", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": "['Infection risk']", "approved": true, "websites": "['https://cssh.northeastern.edu/bari/projects/covid-19-in-boston/']", "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true } ] }, { "id": 31664, "first_name": "Gabor P", "last_name": "Lippner", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31665, "first_name": "Silvia", "last_name": "Prina", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 184, "ror": "https://ror.org/04t5xt781", "name": "Northeastern University", "address": "", "city": "", "state": "MA", "zip": "", "country": "United States", "approved": true }, "abstract": "The dynamics of human behavior play a crucial role in the spread of epidemics. While much research has focused on individual reactions to risks and policies, this project examines how groups of people, such as households, communities, or organizations, demonstrate coordinated risk-mitigating behavior and make collective decisions during an epidemic. These group-level behaviors can significantly impact the trajectory of an epidemic, beyond what can be captured by aggregating individual behaviors. By studying group behaviors, such as the formation of social bubbles and changes in risk-mitigating norms and conventions, this research aims to create better mathematical models that reflect real-world social interactions. These models will help scientists and policymakers develop more effective strategies for managing epidemics, ultimately saving lives and reducing social and economic impacts. Additionally, insights from this research could inform policies on a range of issues including gun violence, opioid abuse, disaster response, and community resilience, where group behaviors play a critical role.<br/><br/>The research concentrates on two main questions: 1) How can mathematical models and scalable computational algorithms be created to incorporate group-level behavioral responses in epidemic models? 2) How much do group-level responses significantly influence pandemic trajectories, and what are the resulting policy implications? The team plans to jointly work on several interconnected research thrusts. They will build mathematical foundations using a three-level network model and cooperative game theory to incorporate group-level behavioral responses, such as the formation and transformation of pandemic social bubbles and localized risk-mitigating norms within pandemic models. Next, they will create computational models that enable scalable and interpretable execution of these network-based approaches, developing dynamic networks using geospatial data and designing network downscaling algorithms to improve simulation efficiency. The team will use causal identification based on various natural experiments to estimate the input parameters of the models, focusing on empirically measuring perceived risk, peer effects on interaction networks, and the formation of social bubbles. Finally, they will implement and validate the model comprehensively at the county level in the US and at a more granular level in Boston neighborhoods, examining the policy implications of group-level behavioral responses. This award is co-funded by DMS (Division of Mathematical Sciences), SBE/SES (Directorate of Social, Behavioral and Economic Sciences, Division of Social and Economic Sciences), and SBE/BCS (Directorate of Social, Behavioral and Economic Sciences, Division of Behavioral and Cognitive Sciences).<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": "15116", "attributes": { "award_id": "2414383", "title": "Collaborative Research: The causes and consequences of Higher Order Interactions (HOI)", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Biological Sciences (BIO)", "Animal Behavior" ], "program_reference_codes": [], "program_officials": [ { "id": 31658, "first_name": "Kim L.", "last_name": "Hoke", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 729936, "principal_investigator": { "id": 31667, "first_name": "Nina", "last_name": "Fefferman", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 190, "ror": "", "name": "University of Tennessee Knoxville", "address": "", "city": "", "state": "TN", "zip": "", "country": "United States", "approved": true }, "abstract": "Public health and bioeconomics are two examples of fields that can benefit from the funded work. By developing a framework to study higher order interactions, i.e., simultaneous interactions, the funded work will provide novel tools to analyze complex systems. The COVID-19 pandemic was challenging to control because people could catch the disease from accumulating many short exposures to multiple infected people, i.e., from higher order interactions, which are rarely considered in epidemiological models. Similarly, the efficient transfer of goods was another casualty of the pandemic due to supply-chain disruptions. Higher order interactions, in which goods are exchanged simultaneously, can substantially expedite the transfer of goods and increase the robustness and resilience of supply-chains to disruptions. The general framework that will be developed in this grant will use a tractable biological system to develop mathematical tools to study the causes and consequences of higher order interactions. The mathematical models and tools developed will be general, to allow application to other systems, such as communication, disease transmission, and social learning. The work will be published in general journals with a wide interdisciplinary readership and the analysis code will be made publicly available. Both PIs have a strong track record of recruiting and facilitating the success of students from groups that are unrepresented in the sciences and this commitment to mentoring a diverse population of trainees in interdisciplinary work will continue. To further disseminate the work to the general public, podcast episodes will be produced and distributed widely.<br/><br/>Collective outcomes, such as the social behavior of animals, emerge from interactions among system components. While substantial work has been devoted to examining the intricate network of interactions among animals, these interactions are described and analyzed as dyadic events. However, multiple individuals can interact simultaneously. For example, an alarm call is broadcast to multiple individuals at once rather than through multiple one-on-one interactions. Despite the important conceptual and functional differences between dyadic and higher order interactions, there are only few methodological approaches that emphasize the higher order nature of social interactions. The proposed work will examine the causes and consequences of higher order interactions, and the feedback between them, by adapting and implementing existing mathematical tools from algebraic topology, simplicial sets, in novel ways. Specifically, the aims include to determine the conditions under which higher order interactions emerge; to examine the consequences of higher order interactions; and to investigate feedback between causes and consequences of higher order interactions to uncover potential evolutionary pathways for their emergence. Social insects are an especially powerful system for examining the questions in the proposal because of the profound fitness consequences of interactions among individuals for the group. Therefore, the proposed work will use foraging and food transmission of Argentine ants (Linepithema humile) as a model system to examine the internal and external causes and consequences of higher order interactions. Project outcomes will enable innovative approaches to fundamental and generalizable questions which are currently beyond our reach.<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": "15117", "attributes": { "award_id": "2418011", "title": "SBIR Phase I:Combinatorial Platform for the Discovery of Improved Molecular Recognition Components for Use in Therapeutic and Diagnostic Antibodies", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Technology, Innovation and Partnerships (TIP)", "SBIR Phase I" ], "program_reference_codes": [], "program_officials": [ { "id": 773, "first_name": "Erik", "last_name": "Pierstorff", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 273550, "principal_investigator": { "id": 31668, "first_name": "Christopher", "last_name": "Szent-Gyorgyi", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [], "awardee_organization": { "id": 2510, "ror": "", "name": "BIOCOGNON LLC", "address": "", "city": "", "state": "PA", "zip": "", "country": "United States", "approved": true }, "abstract": "The broader impact of this Small Business Innovation Research (SBIR) Phase I project is the fundamental improvement of crucial antibody components that recognize and bind therapeutic or diagnostic targets. Modern antibodies are usually engineered as protein chimeras comprised of different parts, including one to several molecular recognition domains that mediate binding. The proposed research will integrate breakthroughs in next generation DNA sequencing and synthetic and computational biology to create a combinatorial high throughput platform for generating better recognition domains. The core aim is to creatively and efficiently use genetic information from patients, pathogens and antibodies for the advancement of therapeutics and diagnostics across a spectrum of diseases. The platform could expedite the design and discovery of current antibody-based therapeutics to reduce the enormous costs and time required to bring these drugs to market. The platform is ideally suited for the development of new classes of therapeutics where very rapid, adaptable and inexpensive response is required, such as in truly personalized treatments of continuously changing tumors or in rapidly evolving viral pandemics where passive vaccines need to be generated at scale.<br/><br/>The proposed project will demonstrate that a novel yeast-based high throughput screening platform is able to efficiently generate molecular recognition domains that specifically recognize clinically important targets. The proof-of-concept target antigens are a human receptor/ligand pair important for the immunosuppression of certain cancers and a coronavirus surface protein that mediates infection by binding a human receptor. In these screens, the use of yeast cells that surface display antibody recognition domains, and secrete these target antigens from the same cell, enables next generation sequencing to identify the genetic information encoding both the domain and the target. This dual detection capability is made possible by innovative fluorescent biosensors and is unique to this screening platform. The project will utilize synthetic biology to construct a library with a rich variety of recognition domains that will be screened simultaneously against several target antigens of varying design. Next generation sequencing analysis will show that it is practical to implement combinatorial screens using engineered recognition domains and antigens to identify recognition domains with desired binding specificity and affinity.<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": "15118", "attributes": { "award_id": "2405483", "title": "Interactive, Individualized Professional Learning for Elementary School Teachers: Enhancing Content and Pedagogical Content Knowledge as a Basis for Improving Practice", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)", "Discovery Research K-12" ], "program_reference_codes": [], "program_officials": [ { "id": 27714, "first_name": "Jennifer", "last_name": "Noll", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 4639444, "principal_investigator": { "id": 31670, "first_name": "Yasemin", "last_name": "Copur-Gencturk", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 2765, "first_name": "Ken", "last_name": "Frank", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31669, "first_name": "Jiliang", "last_name": "Tang", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 152, "ror": "https://ror.org/03taz7m60", "name": "University of Southern California", "address": "", "city": "", "state": "CA", "zip": "", "country": "United States", "approved": true }, "abstract": "Mathematics plays a critical role in students’ career choices as well as their success in STEM fields. Unfortunately, math performance often declines as students’ progress through the grade levels and the aftermath of the COVID19 pandemic has worsened the situation. Research has shown that when teachers have strong content and pedagogical content knowledge that they can provide better quality mathematics instruction to their students and improve student outcomes. The goal of this project is to enhance elementary school teachers’ capacity to improve students’ mathematics learning through a scaled professional development program that uses artificial intelligence (AI) to create a personalized, active learning environment for teachers. The professional development program focuses on key elements of content-specific expertise needed for teaching, such as enhancing teachers’ understanding of the foundational ideas behind numbers and operations concepts that are developed across grade levels, as well as teachers’ understanding of how students learn these concepts and how various instructional tools and practices can improve students’ learning. By creating a professional development that is adaptive to the individualized needs of teachers and is accessible to teachers anywhere and at any time, this work has the potential to change student outcomes at scale. The professional development program utilizes advances in AI to create an inquiry-based learning environment for teachers to enhance their understanding through solving problems of practice. Equipping teachers with the knowledge and skills crucial for quality teaching has the potential to improve mathematics teaching and learning at scale, which has the potential to reduce the opportunity gaps to quality teaching faced by underserved students.<br/><br/>The specific focus of this project is to enhance elementary school teachers’ content and pedagogical content knowledge of numbers and operations using a multiple-AI-agent to guide teachers’ development of a conceptual understanding of the content as well as ways to make the content more accessible to their students. Rather than AI delivering the information, the AI tool will serve as a facilitator to create a learning environment in which teachers meaningfully engage with purposefully developed activities and learn through the process. The research questions the work addresses are: (1) In what ways can advances in AI be incorporated into the AI-based interactive professional development program? (2) How well does the AI-based professional development program enhance teachers’ content and pedagogical content knowledge of numbers and operations? and (3) How well does the AI-based professional development program enhance the quality of mathematics teaching and students learning of numbers and operation? The professional development program and AI tool will be developed through multiple iterations and inputs from several key stakeholders, such as teachers, teacher educators, and content experts. The study will use a mixed methods approach. The effectiveness of the fully developed program on instruction and student learning will be explored through a randomized controlled trial with 200 elementary school teachers. The final version of the program will be made available online. <br/><br/>The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects.<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": "15119", "attributes": { "award_id": "2409068", "title": "DISES-EX: Simulating social-ecological cascades during the second plague pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)", "DYN COUPLED NATURAL-HUMAN" ], "program_reference_codes": [], "program_officials": [ { "id": 6330, "first_name": "Paco", "last_name": "Moore", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2024-09-01", "end_date": null, "award_amount": 749786, "principal_investigator": { "id": 31674, "first_name": "Nicolas", "last_name": "Gauthier", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, "other_investigators": [ { "id": 2480, "first_name": "Amanda", "last_name": "Wissler", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 500, "ror": "", "name": "Wissler, Amanda", "address": "", "city": "", "state": "AZ", "zip": "", "country": "United States", "approved": true } ] }, { "id": 31671, "first_name": "Sofia", "last_name": "Pacheco-Fores", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31672, "first_name": "Timothy", "last_name": "Newfield", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] }, { "id": 31673, "first_name": "Gabriela", "last_name": "Hamerlinck", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 158, "ror": "https://ror.org/02y3ad647", "name": "University of Florida", "address": "", "city": "", "state": "FL", "zip": "", "country": "United States", "approved": true }, "abstract": "Throughout history, infectious diseases have shaped human societies in profound ways. The Black Death pandemic of the 14th century was one of the deadliest in human history, killing an estimated 30 to 60 percent of Europe’s population. This project aims to unravel the complex relationships between climate, agriculture, human behavior, and disease outbreaks during the Black Death and subsequent centuries-long plague pandemic. By integrating mathematical models with archaeological and historical data, the researchers will reconstruct how environmental and social factors combined to create conditions ripe for catastrophic pandemics. Broader impacts will arise from the integrated datasets and modeling tools that will be made freely available and support infectious disease research. The crucial insights will aid management of modern outbreaks and may improve public health in the future.<br/><br/>The project will develop computer simulations that integrate models of disease transmission, human demographics, land use, and climate. These models will be combined with diverse sources including human skeletal remains, tree-ring data, pollen records, and historical documents using a technique called data assimilation. This approach allows researchers to fill in gaps in the fragmentary historical record and test hypotheses about how factors like climate-driven food shortages, urbanization, and trade routes affected plague outbreaks. The researchers will collect new bio-archaeological data on age, health status, and migration patterns from skeletal remains at plague burial sites across Europe. By reconstructing the environmental and social conditions surrounding major outbreaks over several centuries, the project aims to identify recurring patterns that cannot be revealed from contemporary pandemics alone.<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": 1384, "pages": 1392, "count": 13920 } } }