Redescription of the deep-sea octopod Benthoctopus normani (Massy 1907) and a description of a new species from the Northeast Atlantic

A long-synonymized species Benthoctopus normani (Massy 1907) (Cephalopoda: Octopodidae) is redescribed from material collected over 30 years by the National Oceanography Centre, Southampton and the National Museums of Scotland. It can be distinguished from other octopodid specimens found in deep waters of the Northeast Atlantic by its biserial suckers, lack of ink sac, and simple ligula, which lacks transverse ridges. Examination of the collections led to the identification of a new species of Benthoctopus from the Northeast Atlantic, which is described herein.

Resolution of a taxonomic conundrum: an ultrastructural and molecular description of the life cycle of Pleistophora mulleri (Pfeiffer 1895; Georgevitch 1929)

The classification of a microsporidian parasite observed in the abdominal muscles of amphipod hosts has been repeatedly revised but still remains inconclusive. This parasite has variable spore numbers within a sporophorous vesicle and has been assigned to the genera Glugea, Pleistophora, Stempellia, and Thelohania. We used electron microscopy and molecular evidence to resolve the previous taxonomic confusion and confirm its identification as Pleistophora mulleri. The life cycle of P. mulleri is described from the freshwater amphipod host Gammarus duebeni celticus. Infection appeared as white tubular masses within the abdominal muscle of the host. Light and transmission electron microscope examination revealed the presence of an active microsporidian infection that was diffuse within the muscle block with no evidence of xenoma formation. Paucinucleate merogonial plasmodia were surrounded by an amorphous coat immediately external to the plasmalemma. The amorphous coat developed into a merontogenetic sporophorous vesicle that was present throughout sporulation. Sporogony was polysporous resulting in uninucleate spores, with a bipartite polaroplast, an anisofilar polar filament and a large posterior vacuole. SSU rDNA analysis supported the ultrastructural evidence clearly placing this parasite within the genus Pleistophora. This paper indicates that Pleistophora species are not restricted to vertebrate hosts.

Lagrangian description of nonlinear dust-ion acoustic waves in dusty plasmas

An analytical model is presented for the description of nonlinear dust-ion-acoustic waves propagating in an unmagnetized, collisionless, three component plasma composed of electrons, ions and inertial dust grains. The formulation relies on a Lagrangian approach of the plasma fluid model. The modulational stability of the wave amplitude is investigated. Different types of localized envelope electrostatic excitations are shown to exist.

A Description of the Stationary Wave Pattern of a Marine Craft in Shallow and Intermediate Water Depths

A stationary phase model is used to study supercritical waves generated by high speed ferries. Some general relationships in terms of wave angle, propagation direction, dispersion relationship and depth wavelength relationship are explored and discussed. In particular, it is shown that the wave pattern generated by high speed craft at supercritical speeds depends mainly on the relationship of water depth and ship speed and that the wave patterns are similar in terms of location of crests and troughs for a given depth Froude number. In addition it is found that the far field wave pattern can be described adequately using a single moving point source. The theoretical model compares well with towing tank measurements and full scale data over a range of parameters and hull shapes. The paper also demonstrates that the far field wave pattern at supercritical speeds should be non-dimensionalised by water depth and not hull length unlike it is usually done for subcritical speeds.

Cohort description: The Northern Ireland Longitudinal Study (NILS)

no abstract available

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.