Discrete Conditional Phase-type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.

Mating behavior and chemical communication in the order hymenoptera

Insects of the order Hymenoptera are biologically and economically important members of natural and agro ecosystems and exhibit diverse biologies, mating systems, and sex pheromones. We review what is known of their sex pheromone chemistry and function, paying particular emphasis to the Hymenoptera Aculeata (primarily ants, bees, and sphecid and vespid wasps), and provide a framework for the functional classification of their sex pheromones. Sex pheromones often comprise multicomponent blends derived from numerous exocrine tissues, including the cuticle. However, very few sex pheromones have been definitively characterized using bioassays, in part because of the behavioral sophistication of many Aculeata. The relative importance of species isolation versus sexual selection in shaping sex pheromone evolution is still unclear. Many species appear to discriminate among mates at the level of individual or kin/colony, and they use antiaphrodisiacs. Some orchids use hymenopteran sex pheromones to dupe males into performing pseudocopulation, with extreme species specificity.

Elucidating the Regulon of the Multidrug Resistance Regulator RarA in Klebsiella pneumoniae

RarA is an AraC-type regulator in Klebsiella pneumoniae, which, when overexpressed, confers a low-level multidrug-resistant (MDR) phenotype linked to the upregulation of both the acrAB and oqxAB efflux genes. Increased rarA expression has also been shown to be integral in the development of tigecycline resistance in the absence of ramA in K. pneumoniae. Given its phenotypic role in MDR, microarray analyses were performed to determine the RarA regulon. Transcriptome analysis was undertaken using strains Ecl8?rarA/pACrarA-2 (rarA-expressing construct) and Ecl8?rarA/pACYC184 (vector-only control) using bespoke microarray slides consisting of probes derived from the genomic sequences of K. pneumoniae MGH 78578 (NC_009648.1) and Kp342 (NC_011283.1). Our results show that rarA overexpression resulted in the differential expression of 66 genes (42 upregulated and 24 downregulated). Under the COG (clusters of orthologous groups) functional classification, the majority of affected genes belonged to the category of cell envelope biogenesis and posttranslational modification, along with genes encoding the previously uncharacterized transport proteins (e.g., KPN_03141, sdaCB, and leuE) and the porin OmpF. However, genes associated with energy production and conversion and amino acid transport/metabolism (e.g., nuoA, narJ, and proWX) were found to be downregulated. Biolog phenotype analyses demonstrated that rarA overexpression confers enhanced growth of the overexpresser in the presence of several antibiotic classes (i.e., beta-lactams and fluoroquinolones), the antifungal/antiprotozoal compound clioquinol, disinfectants (8-hydroxyquinoline), protein synthesis inhibitors (i.e., minocycline and puromycin), membrane biogenesis agents (polymyxin B and amitriptyline), DNA synthesis (furaltadone), and the cytokinesis inhibitor (sanguinarine). Both our transcriptome and phenotypic microarray data support and extend the role of RarA in the MDR phenotype of K. pneumoniae.

Topological mappings between graphs, trees and generalized trees

We present novel topological mappings between graphs, trees and generalized trees that means between structured objects with different properties. The two major contributions of this paper are, first, to clarify the relation between graphs, trees and generalized trees, a graph class recently introduced. Second, these transformations provide a unique opportunity to transform structured objects into a representation that might be beneficial for a processing, e.g., by machine learning techniques for graph classification. (c) 2006 Elsevier Inc. All rights reserved.

Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.