IMMUNOCHEMICAL AND CHROMATOGRAPHIC ANALYSES OF A NEUROPEPTIDE FROM THE MONOGENEAN PARASITE, DICLIDOPHORA-MERLANGI - EVOLUTIONARY ASPECTS OF THE NEUROPEPTIDE-Y SUPERFAMILY

1. A neuropeptide exhibiting pancreatic polypeptide-immunoreactivity (PP-IR) has been isolated and characterised from the parasitic platyhelminth, Diclidophora merlangi.

Applied aspects of Rhodococcus genetics

Eubacteria of the genus Rhodococcus are a diverse group of microorganisms commonly found in many environmental niches from soils to seawaters and as plant and animal pathogens. They exhibit a remarkable ability to degrade many organic compounds and their economic importance is becoming increasingly apparent. Although their genetic organisation is still far from understood, there have been many advances in recent years. Reviewed here is the current knowledge of rhodococci relating to gene transfer, recombination, plasmid replication and functions, cloning vectors and reporter genes, gene expression and its control, bacteriophages, insertion sequences and genomic rearrangements. Further fundamental studies of Rhodococcus genetics and the application of genetic techniques to the these bacteria will be needed for their continued biotechnological exploitation.

Exploring statistical and population aspects of network complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples.