Investigation into Cloud Computing for More Robust Automated Bulk Image Geoprocessing

Geospatial resource assessments frequently require timely geospatial data processing that involves large multivariate remote sensing data sets. In particular, for disasters, response requires rapid access to large data volumes, substantial storage space and high performance processing capability. The processing and distribution of this data into usable information products requires a processing pipeline that can efficiently manage the required storage, computing utilities, and data handling requirements. In recent years, with the availability of cloud computing technology, cloud processing platforms have made available a powerful new computing infrastructure resource that can meet this need. To assess the utility of this resource, this project investigates cloud computing platforms for bulk, automated geoprocessing capabilities with respect to data handling and application development requirements. This presentation is of work being conducted by Applied Sciences Program Office at NASA-Stennis Space Center. A prototypical set of image manipulation and transformation processes that incorporate sample Unmanned Airborne System data were developed to create value-added products and tested for implementation on the "cloud". This project outlines the steps involved in creating and testing of open source software developed process code on a local prototype platform, and then transitioning this code with associated environment requirements into an analogous, but memory and processor enhanced cloud platform. A data processing cloud was used to store both standard digital camera panchromatic and multi-band image data, which were subsequently subjected to standard image processing functions such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), band stacking, reprojection, and other similar type data processes. Cloud infrastructure service providers were evaluated by taking these locally tested processing functions, and then applying them to a given cloud-enabled infrastructure to assesses and compare environment setup options and enabled technologies. This project reviews findings that were observed when cloud platforms were evaluated for bulk geoprocessing capabilities based on data handling and application development requirements

Technology Development to Explore the Relationship Between Oral Health and the Oral Microbial Community

The human oral cavity contains a complex microbial community that, until recently, has not been well characterized. Studies using molecular tools have begun to enumerate and quantify the species residing in various niches of the oral cavity; yet, virtually every study has revealed additional new species, and little is known about the structural dynamics of the oral microbial community or how it changes with disease. Current estimates of bacterial diversity in the oral cavity range up to 700 species, although in any single individual this number is much lower. Oral microbes are responsible for common chronic diseases and are suggested to be sentinels of systemic human diseases. Microarrays are now being used to study oral microbiota in a systematic and robust manner. Although this technology is still relatively young, improvements have been made in all aspects of the technology, including advances that provide better discrimination between perfect-match hybridizations from non-specific (and closely-related) hybridizations. This review addresses a core technology using gel-based microarrays and the initial integration of this technology into a single device needed for system-wide studies of complex microbial community structure and for the development of oral diagnostic devices

Phenological Parameters Estimation Tool

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGIN

Assessing MODIS-based Products and Techniques for Detecting Gypsy Moth Defoliation

The project showed potential of MODIS and VIIRS time series data for contributing defoliation detection products to the USFS forest threat early warning system. This study yielded the first satellite-based wall-to-wall 2001 gypsy moth defoliation map for the study area. Initial results led to follow-on work to map 2007 gypsy moth defoliation over the eastern United States (in progress). MODIS-based defoliation maps offer promise for aiding aerial sketch maps either in planning surveys and/or adjusting acreage estimates of annual defoliation. More work still needs to be done to assess potential of technology for "now casts"of defoliation

Potential of VIIRS Data for Regional Monitoring of Gypsy Moth Defoliation: Implications for Forest Threat Early Warning System

A NASA RPC (Rapid Prototyping Capability) experiment was conducted to assess the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) data for monitoring non-native gypsy moth (Lymantria dispar) defoliation of forests. This experiment compares defoliation detection products computed from simulated VIIRS and from MODIS (Moderate Resolution Imaging Spectroradiometer) time series products as potential inputs to a forest threat EWS (Early Warning System) being developed for the USFS (USDA Forest Service). Gypsy moth causes extensive defoliation of broadleaved forests in the United States and is specifically identified in the Healthy Forest Restoration Act (HFRA) of 2003. The HFRA mandates development of a national forest threat EWS. This system is being built by the USFS and NASA is aiding integration of needed satellite data products into this system, including MODIS products. This RPC experiment enabled the MODIS follow-on, VIIRS, to be evaluated as a data source for EWS forest monitoring products. The experiment included 1) assessment of MODIS-simulated VIIRS NDVI products, and 2) evaluation of gypsy moth defoliation mapping products from MODIS-simulated VIIRS and from MODIS NDVI time series data. This experiment employed MODIS data collected over the approximately 15 million acre mid-Appalachian Highlands during the annual peak defoliation time frame (approximately June 10 through July 27) during 2000-2006. NASA Stennis Application Research Toolbox software was used to produce MODIS-simulated VIIRS data and NASA Stennis Time Series Product Tool software was employed to process MODIS and MODIS-simulated VIIRS time series data scaled to planetary reflectance. MODIS-simulated VIIRS data was assessed through comparison to Hyperion-simulated VIIRS data using data collected during gypsy moth defoliation. Hyperion-simulated MODIS data showed a high correlation with actual MODIS data (NDVI R2 of 0.877 and RMSE of 0.023). MODIS-simulated VIIRS data for the same date showed moderately high correlation with Hyperion-simulated VIIRS data (NDVI R2 of 0.62 and RMSE of 0.035), even though the datasets were collected about a half an hour apart during changing weather conditions. MODIS products (MOD02, MOD09, and MOD13) and MOD02-simulated VIIRS time series data were used to generate defoliation mapping products based on image classification and image differencing change detection techniques. Accuracy of final defoliation mapping products was assessed by image interpreting over 170 randomly sampled locations found on Landsat and ASTER data in conjunction with defoliation map data from the USFS. The MOD02-simulated VIIRS 400-meter NDVI classification produced a similar overall accuracy (87.28 percent with 0.72 Kappa) to the MOD02 250-meter NDVI classification (86.71 percent with 0.71 Kappa). In addition, the VIIRS 400-meter NDVI, MOD02 250-meter NDVI, and MOD02 500-meter NDVI showed good user and producer accuracies for the defoliated forest class (70 percent) and acceptable Kappa values (0.66). MOD02 and MOD02-simulated VIIRS data both showed promise as data sources for regional monitoring of forest disturbance due to insect defoliation

Reconstructing the Inflaton Potential---in Principle and in Practice

Generalizing the original work by Hodges and Blumenthal, we outline a formalism which allows one, in principle, to reconstruct the potential of the inflaton field from knowledge of the tensor gravitational wave spectrum or the scalar density fluctuation spectrum, with special emphasis on the importance of the tensor spectrum. We provide some illustrative examples of such reconstruction. We then discuss in some detail the question of whether one can use real observations to carry out this procedure. We conclude that in practice, a full reconstruction of the functional form of the potential will not be possible within the foreseeable future. However, with a knowledge of the dark matter components, it should soon be possible to combine intermediate-scale data with measurements of large-scale cosmic microwave background anisotropies to yield useful information regarding the potential.Comment: 39 pages plus 2 figures (upon request:[email protected]), LaTeX, FNAL--PUB--93/029-A; SUSSEX-AST 93/3-

NOD2 Mutations and Anti-Saccharomyces cerevisiae Antibodies Are Risk Factors for Crohn's Disease in African Americans

NOD2 mutations and anti-Saccharomyces cerevisiae antibodies (ASCA) are associated with Crohn’s disease (CD), ileal involvement and complicated disease behavior in whites. ASCA and the three common NOD2 mutations have not been assessed in African American (AA) adults with CD

Remodeling of the Streptococcus agalactiae Transcriptome in Response to Growth Temperature

BACKGROUND: To act as a commensal bacterium and a pathogen in humans and animals, Streptococcus agalactiae (group B streptococcus, GBS) must be able to monitor and adapt to different environmental conditions. Temperature variation is a one of the most commonly encountered variables. METHODOLOGY/PRINCIPAL FINDINGS: To understand the extent to which GBS modify gene expression in response to temperatures encountered in the various hosts, we conducted a whole genome transcriptome analysis of organisms grown at 30 degrees C and 40 degrees C. We identified extensive transcriptome remodeling at various stages of growth, especially in the stationary phase (significant transcript changes occurred for 25% of the genes). A large proportion of genes involved in metabolism was up-regulated at 30 degrees C in stationary phase. Conversely, genes up-regulated at 40 degrees C relative to 30 degrees C include those encoding virulence factors such as hemolysins and extracellular secreted proteins with LPXTG motifs. Over-expression of hemolysins was linked to larger zones of hemolysis and enhanced hemolytic activity at 40 degrees C. A key theme identified by our study was that genes involved in purine metabolism and iron acquisition were significantly up-regulated at 40 degrees C. CONCLUSION/SIGNIFICANCE: Growth of GBS in vitro at different temperatures resulted in extensive remodeling of the transcriptome, including genes encoding proven and putative virulence genes. The data provide extensive new leads for molecular pathogenesis research