Currently, nothing is known about the molecular biology of the interaction between this generalist pathogen and its brown algal host. We developed a Real-Time PCR assay that reliably quantifies Eurychasma infection in brown algae and found that various clonal Ectocarpus strains show differential susceptibility towards the oomycete pathogen, with resistance towards Eurychasma being the “exception to the rule”. Established on laboratory cultures, this assay is also applicable for the detection of the pathogen in natural brown algal populations. Furthermore we have established proteomic tools on the compatible interaction between host and pathogen which allow the investigation of protein pattern variations during the course of infection. The results obtained represent first insights into the algal response upon biotic stress. We also contributed to the manual annotation of the Ectocarpus siliculosus genome (first ever sequenced seaweed), uncovering parallels and differences in the repertoire of putative defence-related genes as compared to terrestrial higher plants. During a two-month field work, new records of the pathogen Eurychasma dicksonii have been made around the coast of Lesbos; Greece (in collaboration with the University of the Aegean, Mytilini). This work also resulted in the identification of additional two intracellular pathogens of brown algae which have hardly been described in the literature so far.
Applications of neural network technology and remote sensing in Oceanography.
, González Vilas L, Spyrakos E, Darriba Estevez M, Yarovenko N.
Applied Physic Department, University of Vigo, Spain.
Remote sensing is a geographic analysis tool capable of producing large quantities of data in the spectral, temporal and spatial domains. Techniques for automating the image analysis process would be advanced by the inclusion of artificial intelligence (A.I.) techniques in the design of image processing systems. The systems described in this work involve the follow applications:
Detection, monitoring and forecasting of hydrocarbons spills in the ocean. In this study it is described an operational system for the detection, monitoring and forecasting of oil spills on the Galician coast. Remote sensing and A.I. techniques are applied in order to detect the possible oil slicks, using mainly ENVISAT ASAR radar images, although it was also studied the possible application of ultraviolet and visible sensors. Using GIS and Neural networks to forecast the distribution of commercial fisheries efforts for the Galician fleet