Focal adhesion interactions with topographical structures: a novel method for immuno-SEM labelling of focal adhesions in S-phase cells

Current understanding of the mechanisms involved in osseointegration following implantation of a biomaterial has led to adhesion quantification being implemented as an assay of cytocompatibility. Such measurement can be hindered by intra-sample variation owing to morphological changes associated with the cell cycle. Here we report on a new scanning electron microscopical method for the simultaneous immunogold labelling of cellular focal adhesions and S-phase nuclei identified by BrdU incorporation. Prior to labelling, cellular membranes are removed by tritonization and antigens of non-interest blocked by serum incubation. Adhesion plaque–associated vinculin and S-phase nuclei were both separately labelled with a 1.4 nm gold colloid and visualized by subsequent colloid enhancement via silver deposition. This study is specifically concerned with the effects microgroove topographies have on adhesion formation in S-phase osteoblasts. By combining backscattered electron (BSE) imaging with secondary electron (SE) imaging it was possible to visualize S-phase nuclei and the immunogold-labelled adhesion sites in one energy 'plane' and the underlying nanotopography in another. Osteoblast adhesion to these nanotopographies was ascertained by quantification of adhesion complex formation.     

Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images

Global Vegetation Index (GVI) data from the Advanced Very High Resolution Radiometer (AVHRR) was used to identify macro-scale vegetation/ land cover regions in the former Soviet Union (FSU). These regions are a better representation of surface vegetation and land cover than can be obtained from existing thematic maps of the FSU. Image classes were identified through cluster analysis using the ISODATA clustering algorithm and a maximum likelihood classifier. Qualitative analysis of the image variants produced with different input parameters indicated that an image with 42 classes best represented significant details in vegetation and land cover patterns without producing uninterpretable levels of details that represent artefacts of the clustering algorithm. Initial identification of image classes has been made by considering the weight of evidence provided by quantitative and qualitative analysis of existing maps, analytical tools from class statistics, ancillary data from a variety of sources and expert assessment by Russian scientists with extensive field experience in the FSU. Overall, this method of image classification using GVI data appears to describe accurately regions with similar vegetation and hind cover across the FSU. Some questions regarding the identification of wetlands and potential problems with classification in the Russian high arctic are discussed. The products of this research will help improve carbon budget estimates of the FSU by providing accurate delineation and definition of carbon quantifiable regions.     

Digital enhancement as an aid to detecting patterns of vegetation stress using medium-scale aerial photography

The application of image processing algorithms, originally developed for satellite image analysis, has been shown to improve pattern recognition of environmental change and vegetation stress with medium-scale aerial photography. Image enhancement helps to identify subtle features and spatial patterns embedded in a digitized air photo transparency that might otherwise escape visual identification. Our procedure combines visual interpretation with computer-aided image enhancement, including Normalized Difference Vegetation Index (NDVI) and supervised and unsupervised classification. The motiviation for the application of these techniques came from the need to provide a multi-season record of environmental changes in the immediate vicinity of power generation facilities. The procedure involves simple, on-call, acquisition of colour infrared and colour aerial photography that can be electronically enhanced and evaluated, resulting in accurate, timely, and cost-efficient information on vegetation changes related to land management, weather, diseases, insects, or other environmental stressors.