A stream-sediment survey was carried out for the island of Aruba. Concentrations in stream sediments represent the abundance of chemical elements in the drainage basins. A geochemical atlas was created from the collected data and natural background values were established. Cluster analysis and pattern recognition techniques were used to gain a better understanding of the data set.

Two cluster models were selected to study the various geochemical controls on the sediments and to establish a spatial basis of environmental-quality settings for the development of future environmental policies. The first cluster model was suitable to recognise in some detail the reflection of the geology on the geochemistry of the stream sediments. The second, coarser cluster model stressed the importance to distinguish between the two main lithological units of the island when instituting natural background values. Not one uniform value per element is valid, but the value depends on the lithology.

We present a technique to find threshold values that allows the user to separate signal from noise in fluorescence grey-level images. It can be classified as a purely comparative method based upon the amount of “Mutual Information” between two or more florescence images, and we apply it to stacks of such images produced using the newly-developed MELK technology. Our results are compared to results obtained by another research group using a quite different (completely independent and more technology-based) approach; and also to results obtained using Otsu's Thresholding Method, yet another completely distinct approach invented to separate foreground and background in a grey-level image, based on minimising “intra-class variance” [9,10]. The remarkably good agreement found suggests that our proposed comparative information based method not only accounts for the biological mechanisms governing cellular protein networks very well, but also (and probably much more importantly) shows that cells actually organise the spatial structure of their protein networks in a highly non-random fashion as might be expected – and thereby try to optimise their “mutual information content”, and thus most probably their efficiency.