| NSF Org: |
ITE Innovation and Technology Ecosystems |
| Recipient: |
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| Initial Amendment Date: | September 4, 2020 |
| Latest Amendment Date: | October 14, 2020 |
| Award Number: | 2040688 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Mike Pozmantier
ITE Innovation and Technology Ecosystems TIP Directorate for Technology, Innovation, and Partnerships |
| Start Date: | September 15, 2020 |
| End Date: | May 31, 2022 (Estimated) |
| Total Intended Award Amount: | $923,999.00 |
| Total Awarded Amount to Date: | $923,999.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
110 21ST AVE S NASHVILLE TN US 37203-2416 (615)322-2631 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1025 16th Avenue South Suite 102 Nashville TN US 37212-2328 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Convergence Accelerator Resrch |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.084 |
ABSTRACT
The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. Today there is a huge gap between the global need to manage our ecosystems, protect our societies, and discover new therapeutics ? and the global capacity to deliver the data and models needed to solve the most pressing challenges of our time. This project is intended to bridge that gap by connecting researchers, policy makers, and industries to the scalable biome monitoring networks of the future ? by developing the unified biome datasets, cross-cutting models, and policy paradigms that will empower these disciplines to accelerate, innovate, and converge. If successful, this would lead to a fundamental paradigm shift in how disciplines study and manage the planet. It will contribute to the advent of a new generation of scientists developing predictive AI models of the biome, and to developing science-based methods and tools for shaping policies and delivering policy-aware tools to solve societal-scale challenges. We expect that deep monitoring of biome and the new science and technology ecosystem emerging from it will have wide impact on human health, agriculture, national security, and ecology.
The technical goals of this project have been carefully instantiated so that progress towards convergence makes a lasting impact on a range of scientific problems. First, the life sciences, engineering, and policy domains continually face the challenge of managing and unifying disparate biome and ecological datasets. These issues are addressed head on by bringing together uniquely deep and state-of-the-art biome and ecological data sets, identifying the hard unification problems, and providing a reference solution to unification. Second, there is a focus is on new unified agent-based models for predicting mosquito populations, as mosquito-borne diseases already account for over 600 million cases of human disease per year, with a disproportionately large impact on disadvantaged communities in sub-Saharan-Africa. By accelerating the development of new predictive mosquito models ? especially by generalizing them to additional species ? this project will provide long lasting contributions to human health and pandemic preparedness. Third, as deep biome data exponentially scales, the life sciences will become overwhelmed with genomic information. Convergence must lead to new methods to efficiently harness these data and autonomously derive insights.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Computing the Biome: Phase 1 Research Outcome
COVID-19 acutely and painfully demonstrated the impacts of biological threats. Society needs cost-effective systems to detect emerging threats and predict future outbreaks early. The goals of our research project were twofold: (1) demonstrate an AI platform that continuously monitors and predicts biothreats in a major U.S. city, (2) and create a framework for economic sustainability and global scalability by empowering businesses and advanced science missions to consume predictions and produce valuable consumer apps and breakthroughs.
In the phase 1 effort of the Computing the Biome, our team participated in the curriculum training sessions, conducted user interviews, analyzed lessons learned from the interviews and adjusted phase 2 plans based on the results, and produced preliminary results for phase 2 of the program.
Our phase 1 program began with the premise that more data and better models about the biome were essential to addressing society-scale challenges of pandemic preparedness, food security, and climate change. However, we did not anticipate that phase 1 would occur during the worst pandemic in modern history. Thus, it was essential for us to interview a range of stakeholders, from on-the-ground first responders to drug development researchers. Each stakeholder group was impacted differently and could consider the data and models from different perspectives. The interviews allowed us to develop a disciplined phase 2 program that will result in specific new capabilities ? but more broadly sets the stage for a sustainable expansion that can be harnessed by many stakeholders tackling these society-scale challenges. The societal context and the user interviews reshaped our entire approach into a rigorous phase 2 roadmap backed by extensive prototyping and prototyping interviews. This new phase 2 roadmap defined the following pillars: (1) deliver a set of data and AI-driven tools to public health, policymakers, and clinicians that provide them with fundamentally new capabilities to manage some biothreats (i.e., mosquito-borne diseases), (2) ensure that all deliverables are built on extensible platforms and open data approaches with interfaces for scientists and researchers ? as well as application developers and small businesses, (3) immediately and in parallel with these efforts, create specific and community-led business frameworks so that the full creativity of entrepreneurs, application developers, and researchers will drive a vibrant ecosystem on top of these results ? economically sustaining its scaling and cross-cutting benefits. This was a fundamental redesign of the entire program, which we believe more broadly creates new opportunities for science and businesses to engage and address societal-scale challenges. All our academic, industrial, and government participants recognized how the NSF Convergence Accelerator offers a unique framework for delivering impact through its deep understanding of the challenges of convergence.
The phase 2 effort yielded the following preliminary results for phase 2 activities:
1. Early data collection and validation in replicated Harris County, TX habitats.
Working with Harris County Public Health organization, Microsoft created a Microsoft Premonition Proving Ground that replicated Harris County mosquito habitat. The Proving Ground was used to evaluate efficiency of Microsoft Premonition robotic platforms to lure and detect key wild-type vector species from Harris County in 14 ? 16 hours trials with room-scale multi-camera tracking at 100 Hz producing many TBs of data. This data also captured high-fidelity foraging behaviors consistent with published data collected in real environments.
2. Foundations for sustainability and community governance.
Microsoft and HCPH created a collaborative framework called the ?Harris County Healthy Biome Network? that unanimously passed in Harris County Commissioners Court with the vision of creating ?one of the world?s most advanced biological early warning systems? and to ?accelerate new research and startup ecosystems?. As a result of this success, governance mechanisms are in place to achieve goals in phase 2.
3. Preparing for just-in-time sequencing and metagenomics.
While this project is not a sequencing project, sequencing is a fundamental technology for the detection of biothreats and decoding the biome at microbial and viral levels. This project considers how data and AI can guide the deployment of sequencing in a just-in-time fashion so each application of the technology maximizes the information gained and ROI in an actual public health setting. Working collaboratively with the Microsoft, JHU, Pitt, UW, and VU teams, HCPH has developed initial cost models using detailed protocols for detecting cryptic and invasive species, recovering insecticide resistance markers and mosquito host signals, and performing sequencing for total DNA or RNA metagenomics. This effort sets the stage for a complete team that can go from autonomously collecting high-priority samples to sequenced data, to AI-based analysis, within a single day within the 1,800 mi2 of Harris County.
The phase 1 preliminary results are extensively used in phase 2 research activities of the Computing the Biome team.
Last Modified: 09/28/2022
Modified by: Janos Sztipanovits
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