Variations in the Phenolic Contents of Cocoyam Clones in Correlation to Resistance to Pythium Myriotylum
Phenolic compounds from leaves and roots of infected and healthy cocoyam clones resistant (RO1075), tolerant (RO1043), and susceptible (RO1157) to Pythium myriotylum were quantified and tested for their in vitro fungitoxicity on the causal agent of the cocoyam root rot disease. All clones, infected or not, have phenolic compounds showing fungitoxic activity. The phenolic content of the tolerant and susceptible clones is less than that observed in the resistant one meanwhile in the resistant clone RO1075, a large increase in phenolic content is observed particularly in the roots during attack by pathogen.
Changes in phenolic acid levels in wheat leaves expressing resistance to Puccinia recondita f. sp. tritici
Abstract Levels of individual phenolic acids were examined in primary leaves of wheat ( Triticum aestivum ) after inoculation with avirulent and virulent strains of the leaf rust fungus ( Puccinia recondita f. sp. tritici ) at stages when previous work had shown fungal and host cells to be affected by expression of the Lr 20 or Lr 28 alleles for resistance. The predominant phenolic acid, ferulic acid, as well as p -coumaric and syringic acids were detected in primary leaves in both unbound and bound forms. They were not detected in germinating urediniospores of either rust strain. Levels of unbound phenolic acids changed little in response to infection. In Lr28 -bearing leaves inoculated with an avirulent strain, increased concentrations of bound phenolic acids and three other unidentified compounds were observed about 4 h after many single or small groups of cells had undergone hypersensitive collapse. In an Lr20 -bearing cultivar, levels of bound phenolic acids fell in leaves inoculated with either a virulent or avirulent rust strain. Coniferyl aldehyde and coniferyl alcohol were not detected in healthy or inoculated leaves of either wheat cultivar. Attempts to affect expression of resistance by application of inhibitors of phenylalanine ammonia-lyase were not successful and both wheat cultivars remained resistant to avirulent rust strains. The bound phenolic acids which accumulate in cells undergoing a hypersensitive response may play a role in resistance of Lr28 -bearing wheat to the leaf rust fungus.
Application of SVM on satellite images to detect hotspots in Jharia coal field region of India
The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.