Outer membrane proteins of Pseudomonas

In this review, we describe the outer membrane proteins of Pseudomonas aeruginosa and related strains from the Pseudomonas fluorescens rRNA homology group of the Pseudomonadaceae, with emphasis on the physiological function and biochemical characteristics of these proteins. The use of opr (for outer membrane protein) is proposed as the genetic designation for the P. aeruginosa outer membrane proteins and letters are assigned, in conjunction with this designation, to known outer membrane proteins. Proteins whose primary functions involve pore formation, transport of specific substrates, cell structure determination and membrane stabilization are discussed. The conservation of selected proteins in the above Pseudomonas species is also examined.     

HYBRIDIZATION OF PANUS TIGRINUS AND LENTODIUM SQUAMULOSUM

Crosses between monokaryotic isolates of two strains of Lentodium squamulosum and one strain of Panus tigrinus showed complete intercompatibility. Hybrid fruits from crosses between monokaryotic isolates of L. squamulosum and P. tigrinus were uniformly like the P. tigrinus parent. Though both isolates of L. squamulosum showed considerable variability in form, merging toward P. tigrinus, L. squamulosum could always be distinguished from P. tigrinus from hand sections of the hymenophore. The significance of these results as they bear on the problem of inter-relationships between hymenomycetes and gasteromycetes is discussed. L. squamulosum is recognized as variety squamulosus of P. tigrinus.     

Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)