Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.

An Electron-Microscope X-Ray-Diffraction And Amino-Acid-Analysis Study Of The Opercular Filament Cuticle Calcareous Opercular Plate And Habitation Tube Of Pomatoceros Lamarckii Quatrefages (Polychaeta Serpulidae)

The structure, X-ray diffraction and amino acid compositions of the opercular filament cuticle, calcareous opercular plate and habitation tube of the polychaete serpulid, Pomatoceros lamarckii quatrefages, are reported. The opercular filament cuticle is made up of protein and chitin. The chitin is probably in the crystallographic α form. The structure and amino acid composition of the organic components of the opercular filament cuticle and calcareous opercular plate have similarities but are distinctly different from those of the calcareous habitation tube. The opercular plate and habitation tube are composed of different polymorphs of calcium carbonate, aragonite and calcite respectively. Comparisons are made with other chitin-protein systems, structural and calcified proteins.

A neural network based tool for semi-automatic code transformation

A neural network based tool has been developed to assist in the process of code transformation. The tool offers advice on appropriate transformations within a knowledge-driven, semi-automatic parallelisation environment. We have identified the essential characteristics of codes relevant to loop transformations. A Kohonen network is used to discover structure in the characterised codes thus revealing new knowledge that may be brought to bear on the mapping between codes and transformations or transformation sequences. A transform selector based on this process has been developed and successfully applied to the parallelisation of sequential codes.