The secondary structure of V4, the largest variable area of eukaryotic small subunit ribosomal RNA, was re-examined by comparative analysis of 3253 nucleotide sequences distributed over the animal, plant and fungal kingdoms and a diverse set of protist taxa. An extensive search for compensating base pair substitutions and for base covariation revealed that in most eukaryotes the secondary structure of the area consists of 11 helices and includes two pseudoknots. In one of the pseudoknots, exchange of base pairs between the two stems seems to occur, and covariation analysis points to the presence of a base triple. The area also contains three potential insertion points where additional hairpins or branched structures are present in a number of taxa scattered throughout the eukaryotic domain.
Abstract. The least trimmed squares estimator and the minimum covariance determinant estimator [6] are frequently used robust estimators of regression and of location and scatter. Consistency factors can be computed for both methods to make the estimators consistent at the normal model. However, for small data sets these factors do not make the estimator unbiased. Based on simulation studies we therefore construct formulas which allow us to compute small sample correction factors for all sample sizes and dimensions without having to carry out any new simulations. We give some examples to illustrate the e¤ect of the correction factor.
In a bivariate data set it is easy to represent clusters, e.g. by manually circling them or separating them by lines. But many data sets have more than two variables, or they come in the form of inter-object dissimilarities. There exist methods to partition such a data set into clusters, but the resulting partition is not visual by itself. In this paper we construct a new graphical display called CLUSPLOT, in which the objects are represented as points in a bivariate plot and the clusters as ellipses of various sizes and shapes. The algorithm is implemented as an S-PLUS function. Several options are available, e.g. labelling of objects and clusters, drawing lines connecting clusters, and the use of color. We illustrate this new tool with several examples.
This article aims to identify empirically the most important location advantages determining the port of Antwerp's competitive position for containers and conventional cargo as compared to its main rivals in the Hamburg-Le Havre range. A conceptual framework is developed for analysing the competitiveness of seaports, based on an extended version of Porter's ‘diamond’ approach. This framework is tested through a large scale survey with port operators and experts. This article suggests that the port of Antwerp largely benefits from the superstructure used by forwarders, the flexibility and the productivity of its dockworkers and its forwarders. The analysis also identifies three key disadvantages, namely the weak competitiveness of pilotage and inland navigation service providers in the port of Antwerp, and the limited maritime accessibility of the port. The introduction of the extended ‘diamond’ framework allows the identification of a set of strengths and weaknesses characterising a specific port cluster and can thus contribute substantially to the development of port policy and management.International Journal of Maritime Economics (2000) 2, 69–82; doi:10.1057/ijme.2000.8
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