Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 148.01K | Year: 2012
DESCRIPTION (provided by applicant): Research at the intersection of limnology and public health is showing that toxins produced from cyanobacteria act as an environmental trigger for Amyotrophic Lateral Sclerosis (ALS) commonly known as Lou Gehrig's Disease. Additional linkages are being made between cyanobacteria and Parkinson's and several acute illnesses and neurological disorders. A primary obstacle in advancing our understanding of linkages between cyanobacteria blooms, toxicity, and human health is water quality information on the presence, extent, magnitude, and intensity of these harmful algal blooms in freshwater bodies. Little to no exposure data is available to the public health community which has created an opportunity and innovation gap. Remote sensing science has now advanced to the point where operational assessment of cyanobacteria and water quality is feasible. The innovation of this NIH SBIR Phase 1 is the development of an operational cyanobacteria indicator tool (Cyano-Map) that utilizesNASA satellite remote sensing platforms and state-of-the-art geosciences methods. Cyano-Map will be capable mapping and monitoring phycocyanin (PC) abundance and cyanobacteria (i.e., microcystis, anabaena, and planktothrix) over space and time for inlandwaters. Cyano-Map will utilize multiple NASA platforms to extract the strengths of multiple sensors for the optimal spatial, temporal, spectral and radiometric resolutions. PUBLIC HEALTH RELEVANCE: Indicators and risk maps for cyanobacteria blooms in water systems and mapping environmental triggers for ALS and Parkinson's Disease.
Agency: Department of Agriculture | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 600.00K | Year: 2016
Demand for mapping forest disturbance and structural characteristics exists from USDA and Federal programs, industry, and international programs such as REDD. Applications range from operational forestry, assessing carbon sequestration, monitoring wildlife habitat, weather and disaster response, infestations, gauging forest productivity, and evaluating programs such as Environmental Quality Incentives Program (EQIP), Easements, FIA, and state management plans. There is an opportunity to operationalize multiscale Synthetic Aperture Radar (SAR) data to generate metrics of stand structure and spatiotemporal changes. Specifically, this SBIR Phase II is to prototype an automated system and scale products for above ground biomass, forest stand height, crown canopy cover, and disturbance detection using multiscale SAR. During Phase II we build on previous work and leverage ongoing partnerships with USDA USFS and space agencies (NASA, USGS, JAXA, ISRO). Our focused Phase II builds on Phase I case studies by conducting a coordinated campaign of near-simultaneous collection of field data, SAR, and Lidar in partnership with USFS; adds the recently launched operational Sentinel C-band satellites; and scales products to larger areas in a robust and automated approach. We considered recommendations from Phase 1 reviews, Science Advisory Panel feedback, and the most promising outcomes from Phase 1. The long-term (Phase III) vision is to 1.) provide disturbance mapping services, 2.) build and offer robust Monitoring, Reporting, and Verification (MRV) forest metrics derived from multiscale SAR, and 3.) develop Public Private Partnerships (PPP) to support regional, Federal (USFS, NASA, EPA), and international programs centered on monitoring forest disturbances and characterizing ecosystems services.
Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 149.98K | Year: 2014
There is broad scientific consensus that some diseases are influenced by the environment. Recent work has suggested that harmful algae in lakes are linked to some illnesses and diseases. This project will study relationships among lake water quality, harmful algae, and Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrigs disease. An interdisciplinary team of geographers, neurologists, epidemiologists, and statisticians will collaborate to assess how and under what conditions algal levels and water quality impact ALS incidence, a very poorly understood disease. This work will contribute to research on disease ecology as well as inform a wide audience concerned with health outcomes including public health and environmental agencies, medical centers, those potentially exposed to the risk factors, and property owners.
Amyotrophic lateral sclerosis (ALS) is a progressive, fatal disease with an average life expectancy of two to five years from time of diagnosis. Approximately 90% of ALS cases have no known genetic cause and are commonly known as sporadic ALS (sALS). Despite many recent discoveries about the genetics of ALS, the etiology or causal origins of sALS remain largely unknown. It is most likely that sALS results from a combination of underlying genetic susceptibility coupled with environmental exposure to one or more toxins. Recent work has shown linkages between lake water quality and high ALS incidence, with the [algal] cyanotoxin beta methyl-amino-alanine (BMAA) as a potential trigger. The overarching goal of this study is to characterize the relationship between sALS incidence and lake water quality parameters that favor cyanobacteria growth. Multi-scale satellite remote sensing imagery will be used to map lake water quality attributes including cyanobacteria, phycocyanin, chlorophyll-a, and total nitrogen in freshwater lakes in northern New England. An ALS database with completed questionnaire surveys, including information on residential history and related risk factors, will be integrated into a spatial analysis framework that will be generalizable to other similar freshwater / residential systems. Eco-epidemiological modeling will be conducted to test the relationships among lake conditions, risk factors, and sALS. This work will develop tools for assessing stressor response relationships improve understanding of sALS. The results will assist in identifying potential causal factors as well as and means by which they may be remotely monitored, thereby contributing to the quantification of the role of freshwater aquatic ecosystems in human health risk.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.92K | Year: 2013
Rice is an important crop globally that influences food security and the Earth system. Rice is the predominant food staple in many regions with approximately 700 million tons of production annually across the globe. Rice is sensitive to a variety of drivers that can adversely impact production and efficiency including weather variability and inter-seasonal volatility, water resources, and risk management decisions (e.g., pests). Futures are a tool used to manage or hedge risk, reduce volatility, improve food security, and maximize efficiency and profit on the open market. Currently, the rice futures market has little high quality and timely information available to make strategic or application specific decisions to reduce risk and maximize profit. The global rice futures market is thinly traded causing extreme price fluctuation orders of magnitude. This innovation gap has created an opportunity to build an operational Rice Decision Support System to support the rice futures market. The overarching goal of this NASA Phase I SBIR is to evaluate the feasibility of a Rice Decision Support System (RiceDSS) to Support Global Food Security and Commodity Markets. RiceDSS brings together operational remotely sensed mapping of rice, crop production modeling, and weather forecasts to seamlessly generate near real time information on rice extent, growth stages, yield forecasts and statistical uncertainty. RiceDSS uses state-of-the-art web-GIS and mobile technologies to support visualization and delivery of information to futures markets and food security programs.
Agency: Department of Agriculture | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 99.78K | Year: 2014
The goal of the SBIR is to "Operationalize multiscale Synthetic Aperture Radar (SAR) forest structure and disturbance metrics". The objectives are to develop 1.) operational forest structure and disturbance metrics derived from Synthetic Aperture Radar and 2.) mapping information services for rapid forest disturbance assessment. A set of practical questions to be answered at three demonstration case study sites will evaluate the technical, scientific, and commercial feasibility. A direct outcome is the creation of rapid, automated forest structure metrics, disturbance assessment, and decision support tools to improve our understanding of forest health and sustainability; quantify the impacts of disturbance; and help characterize the impacts of climate change and invasive species on forest resources. This SBIR also addresses a NIFA Societal Challenge Area, "Climate Change", by developing standardized assessment metrics that will contribute to carbon science and reducing greenhouse gas emissions through programs such as Forest Inventory and Analysis (FIA) National Program and international programs such as Reducing Emissions from Deforestation and Degradation (REDD).
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.10M | Year: 2014
Agriculture faces major challenges in the decades to come due to increasing resource pressures, severe weather and climate change, population growth and shifting diets, and economic development. Rice is one of the most important crops globally considering its role in the Earth system, food security, and providing livelihoods with more than 1 billion people depending on rice. Tools and systems that can help monitor production and support risk management are needed for decision making by many end users and governments. Futures are a tool used to manage or hedge risk, reduce volatility, improve food security, and maximize efficiency and profit on the open market. Currently, the rice futures market has little high quality and timely information available to make strategic or application specific decisions to reduce risk and maximize profit. The global rice futures market is thinly traded causing extreme price fluctuation orders of magnitude. The innovation of Rice Decision Support System (RiceDSS) is the seamless fusion of operational satellite remote sensing monitoring metrics of rice agriculture, rice yield modeling, and weather forecasts to generate near real time information on rice extent, growth stages, production forecasts and statistical uncertainty. RiceDSS uses a state-of-the-art open source framework with advanced automation routines, web-GIS, and mobile technologies to support visualization and delivery of information to support global food security programs and commodity markets.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 748.97K | Year: 2015
We propose to build and commercialize a working prototype Geospatial Decision Support Toolkit (GeoKit). GeoKit will enable scientists, agencies, and stakeholders to create and deploy their own web based applications containing maps, forms, algorithms, and a rich set of functionality related to visualization, analysis, reporting, querying, and publication of geospatial data and information. GeoKit is intended for customers who are experts in a particular area or problem; have in mind a set of users who will use their site to address a specific problem; have in mind a particular workflow that they want the users to perform and datasets they want to utilize; do not necessarily know or want to know about geospatial data types, formats, operations, and structure; and do not necessarily know or want to know how to construct a web-based application. The mission of GeoKit is to reduce and eventually remove technical barriers that limit direct stakeholder control over the creation and management of geospatially enabled web applications. AGS has worked on numerous geospatial web based applications and services, and continues to have active projects in this area. Our Phase II GeoKit proposal is to create the technological foundation for the distributed "open source DST development framework" that NASA envisions as described in the 2015 Subtopic S5.02 SBIR solicitation.
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 123.58K | Year: 2014
We propose to design a working prototype Geospatial Decision Support Toolkit (GeoKit) that will enable scientists, agencies, and stakeholders to configure and deploy their own web based applications containing maps, forms, algorithms, and a rich set of functionality related to visualization, analysis, querying, and publication of geospatial data and information. GeoKit will focus on development of a suite of tools that will operate on data, to create rule-based applications for risk analysis, risk mitigation, operations management, and science research support. GeoKit will enhance the the use of data from NASA and other sources, provide a tool for non-software developers to create a website with custom functions and tools that operate on geospatial data, and provide a framework for development of new tools to support risk assessment, risk management, and operational analysis of spatially- explicit data from NASA platforms, climate reanalyses, and user-defined sources, as well as allow real-time publication of results in standard geobrowser compatible formats.
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase I | Award Amount: 150.74K | Year: 2016
DESCRIPTION provided by applicant Concern over toxins and public health threats resulting from Cyanobacterial Harmful Algal Blooms CHABs have gained attention as reoccurring and seasonal blooms persist in many waters It has also been suggested that climate change is increasing the frequency intensity and duration of CHABs Broadly cyanotoxins can be described as having negative health impacts and can be grouped into neurotoxic lipopolysaccharides or hepatotoxic such as microcystins which tend to be the most frequently reported The neurotoxin N methylamino L alanine BMAA can be produced by cyanobacteria and have been associated with CHABs and Amyotrophic lateral sclerosis ALS clusters across northern New England Caller et al Torbick et al Banack The magnitude and complexity of CHABs in our freshwater lakes requires innovative technologies and multiscale analysis for detection understanding forecasting and mitigating public health threats Specifically during this SBIR we partner with Cleveland Clinic and the ALS Research Center to evaluate linkages between Lake Erie CHABs and ALS cases ALS is a progressive fatal neurodegenerative disease with a lifetime risk of in The pathologic hallmark of ALS is the selective death of motor neurons in the brain and spinal cord producing debilitating symptoms of progressive weakness muscle wasting and spasticity Mutations in genes underlying familial ALS fALS have been discovered in only of the total population of ALS patients Approximately of ALS cases have no known genetic cause this group is commonly called sporadic ALS sALS There is a broad scientific consensus that ALS is caused by gene environment interactions Evidence has shown potential linkages between water quality cyanobacteria and high ALS incidence Decision Support Tools DSTs that integrate satellite remote sensing web and cloud services and mobile devices e g phones tablets offer the capability to monitor CHABs at spatial and temporal scales not achievable by discrete point observations or traditional techniques For CHAB detection bio optical algorithms use color remote sensing data to convert observed spectral light information into geophysical products such as chlorophyll a and phycocyanin concentration maps Remote sensing can detail attributes over space time and characterize location duration intensity and frequency The amount of historical satellite imagery is now thousands of terabytes presenting data handling challenges for all but the most technically capable end users Real time image processing flows integration of mobile devices and crowd sourcing and public health warning and forecasting tools are unobtainable for most applications due to technical challenges Proposed Innovation We propose to design build and operate a andapos Cloud based Lake Cyanobacterial Harmful Algal Bloom Mapping and Analysis Platform CHAB MAP for supporting public health risk assessmentandapos The tool will automate the mapping and analysis of relevant satellite remote sensing data and real time imagery updates for mapping and analyzing CHAB metrics derived from MODIS MERIS Sentiel and Landsat Mobile apps and crowd sourcing tools will be designed in partnership with NOAA GLERL and EPA to improve access to information decision making and data gathering In this SBIR we work closely with Cleveland Clinic to address the role of cyanotoxins and ALS in Ohio Specific Aim Design and apply BigData approaches and generate historical and real time MODIS present MERIS and Landsat present Lake Erie CHABs metrics including chl a phycocyanin and water temperature working with NOAA GLERL NASA and EPA partners Specific Aim Design test and identify optimal web and cloud framework for managing visualization plotting tools managing tabular data and accessing products using web and mobile packages Specific Aim Work in partnership with Cleveland Clinic and Dr Erik Pioro Barry Winovich Chair for ALS Research in the Lerner Research Institute and director of the section of ALS in the Dept of Neurology to assess the role of CHABs and cyanotoxins in ALS in northern and central Ohio Grow other applications investigating neurodegenerative diseases and disorders potentially linked to CHABs PUBLIC HEALTH RELEVANCE Design build and operate a andapos Cloud based Lake Cyanobacterial Harmful Algal Bloom Mapping and Analysis Platform Lake CHAB MAP for investigating impact of cyanotoxins on ALS and developing public health applicationsandapos Help address cyanobacteria ALS and toxicity public health threats
Agency: Department of Agriculture | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 460.00K | Year: 2011
Agricultural row crops occupy hundreds of million acres of land in the United States. Decisions regarding the implementation of tillage practices in these agricultural areas have a significant effect on other environmental outcomes including soil erosion, water quality, and carbon sequestration. In addition, the effects of tillage practices can vary due to soil type and topographic conditions. There is currently no systematic and cost-effective method for documenting tillage practices, or the resulting effects, over a large region. Currently, agricultural tillage practices are typically mapped at the regional level using a drive-by survey method, which is time consuming, expensive, and limited in spatial extent and temporal sampling. The high cost, in time and resources, of identifying tillage practices across a large region using this traditional drive-by survey is prohibitive. The use of remote sensing data for mapping tillage practices across large regions represents a cost efficient solution. With funding from the USDA, we will build a prototype operational tillage practice monitoring platform (OpTIS) that will systematically provide information about the spatial and temporal dynamics of tillage practices through a web-GIS environment.