Identification of bacteria associated with Dinoflagellates (Dinophyceae) Alexandrium spp. using tyramide signal amplification fluorescent in situ hybridization and confocal microscopy

In the marine environment, phytoplankton and bacterioplankton can be physically associated. Such association has recently been hypothesized to be involved in the toxicity of the dinoflagellate genus Alexandrium. However, the methods, which have been used so far to identify, localize, and quantify bacteria associated with phytoplankton, are either destructive, time consuming, or lack precision. In the present study we combined tyramide signal amplification–fluorescent in situ hybridization (TSA-FISH) with confocal microscopy to determine the physical association of dinoflagellate cells with bacteria. Dinoflagellate attached microflora was successfully identified with TSA-FISH, whereas FISH using monolabeled probes failed to detect bacteria, because of the dinoflagellate autofluorescence. Bacteria attached to entire dinoflagellates were further localized and distinguished from those attached to empty theca, by using calcofluor and DAPI, two fluorochromes that stain dinoflagellate theca and DNA, respectively. The contribution of specific bacterial taxa of attached microflora was assessed by double hybridization. Endocytoplasmic and endonuclear bacteria were successfully identified in the nonthecate dinoflagellate Gyrodinium instriatum. In contrast, intracellular bacteria were not observed in either toxic or nontoxic strains of Alexandrium spp. Finally, the method was successfully tested on natural phytoplankton assemblages, suggesting that this combination of techniques could prove a useful tool for the simultaneous identification, localization, and quantification of bacteria physically associated with dinoflagellates and more generally with phytoplankton.

3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

Destination images of non-visitors

This article provides much needed understanding of destination images held by non-visitors. Recognizing the characteristics of non-visitor images and their formation is important in order to understand images more widely. This qualitative study assesses images of London. The views of three hundred people in the Czech Republic who have never visited London were obtained via an innovative open-ended research instrument. The study showed that non-visitors imagine destinations through comparisons with their own experiences of places. Findings indicate that images can be very persistent and that the first images formed of a destination endure over time. Although the research is based on people with no direct experience of London, the research highlights that a range of secondary ‘experiences’ influence image formation.