Factors Influencing Sudden Death Syndrome and Root Health in Soybean

Each year, soybean growers lose at least 14 percent (approximately $175,000,000 in Indiana) of their crop to disease. Root rots disease caused by particular species of Fusarium, Phytophthora, Rhizoctonia, Phialophora, and Macrophomina account for a significant portion of this annual yield loss and substantially increase yield losses above 14 percent in specific production areas in the Midwest. Yield losses due to diseases like Fusarium root rot, Phytophthora root rot, Rhizoctonia root rot, Brown stem rot, and Charcoal root rot caused by species of the fungi mentioned above have been recognized for a number of years and most soybean producers are somewhat familiar with disease symptoms associated with each disease. However, root rot damage and losses due to Sudden Death Syndrome, a relatively new soybean disease are not as well documented

The Uniform Soybean Tests: Northern States 2007

United States Department of Agriculture Agricultural Research Service, West Lafayette, Indiana, Cooperating with State Agricultural Experiment Stations, Northern States

The Uniform Soybean Tests: Northern States 2009

United States Department of Agriculture Agricultural Research Service, West Lafayette, Indiana, Cooperating with State Agricultural Experiment Stations, Northern States

The Uniform Soybean Tests: Northern Region 2005

United States Department of Agriculture Agricultural Research Service, West Lafayette, Indiana, Cooperating with State Agricultural Experiment Stations, Northern States

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

<p>Abstract</p> <p>Background</p> <p>Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability.</p> <p>Methods</p> <p>Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications <it>in silico </it>using simulated datasets.</p> <p>Results</p> <p>We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage.</p> <p>Conclusions</p> <p>We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait.</p

The Science Performance of JWST as Characterized in Commissioning

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies