Characterization of White bream virus reveals a novel genetic cluster of nidoviruses.

The order Nidovirales comprises viruses from the families Coronaviridae (genera Coronavirus and Torovirus), Roniviridae (genus Okavirus), and Arteriviridae (genus Arterivirus). In this study, we characterized White bream virus (WBV), a bacilliform plus-strand RNA virus isolated from fish. Analysis of the nucleotide sequence, organization, and expression of the 26.6-kb genome provided conclusive evidence for a phylogenetic relationship between WBV and nidoviruses. The polycistronic genome of WBV contains five open reading frames (ORFs), called ORF1a, -1b, -2, -3, and -4. In WBV-infected cells, three subgenomic RNAs expressing the structural proteins S, M, and N were identified. The subgenomic RNAs were revealed to share a 42-nucleotide, 5' leader sequence that is identical to the 5'-terminal genome sequence. The data suggest that a conserved nonanucleotide sequence, CA(G/A)CACUAC, located downstream of the leader and upstream of the structural protein genes acts as the core transcription-regulating sequence element in WBV. Like other nidoviruses with large genomes (>26 kb), WBV encodes in its ORF1b an extensive set of enzymes, including putative polymerase, helicase, ribose methyltransferase, exoribonuclease, and endoribonuclease activities. ORF1a encodes several membrane domains, a putative ADP-ribose 1"-phosphatase, and a chymotrypsin-like serine protease whose activity was established in this study. Comparative sequence analysis revealed that WBV represents a separate cluster of nidoviruses that significantly diverged from toroviruses and, even more, from coronaviruses, roniviruses, and arteriviruses. The study adds to the amazing diversity of nidoviruses and appeals for a more extensive characterization of nonmammalian nidoviruses to better understand the evolution of these largest known RNA viruses.

Detection of the icaADBC gene cluster and biofilm formation in Staphylococcus epidermidis isolates from catheter-related urinary tract infections.

Cho SH, Naber K, Hacker J, Ziebuhr W. Institut für Molekulare Infektionsbiologie, Röntgenring 11, D-97070 Würzburg, Germany. Biofilm production in Staphylococcus epidermidis is an important virulence factor that is mediated by the expression of the icaADBC operon. In this study 41 S. epidermidis isolates obtained from catheter-related urinary tract infections were analyzed for the presence of the icaADBC operon and biofilm formation. Eighteen of 41 isolates (44%) were shown to carry ica-specific DNA, but only 11 isolates (27%) produced biofilms spontaneously under normal growth conditions. Upon induction by external stress or antibiotics, biofilm formation could be stimulated in five of seven ica-positive, biofilm-negative isolates, indicating that the icaADBC expression was down-regulated in these strains. Genetic analyses of the ica gene clusters of the remaining two ica-positive, biofilm-negative strains revealed a spontaneous ICAC::IS256 insertion in one strain. Insertion of the element caused a target site duplication of seven base pairs and a biofilm-negative phenotype. After repeated passages the insertion mutant was able to revert to a biofilm-forming phenotype which was due to the precise excision of IS256 from the icaC gene. The data show that icaC::IS256 integrations occur during S. epidermidis polymer-related infections and the results highlight the biological relevance of the IS256-mediated phase variation of biofilm production in S. epidermidis during an infection.

Difference equation sensor characterization algorithms for two-thermocouple probes

The characterization of thermocouple sensors for temperature measurement in variable flow environments is a challenging problem. In this paper, novel difference equation-based algorithms are presented that allow in situ characterization of temperature measurement probes consisting of two-thermocouple sensors with differing time constants. Linear and non-linear least squares formulations of the characterization problem are introduced and compared in terms of their computational complexity, robustness to noise and statistical properties. With the aid of this analysis, least squares optimization procedures that yield unbiased estimates are identified. The main contribution of the paper is the development of a linear two-parameter generalized total least squares formulation of the sensor characterization problem. Monte-Carlo simulation results are used to support the analysis.

Computationally efficient sequential learning algorithms for direct link resource-allocating networks

Computionally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). These are achieved by decomposing existing recursive training algorithms on a layer by layer and neuron by neuron basis. This allows network weights to be updated in an efficient parallel manner and facilitates the implementation of minimal update extensions that yield a significant reduction in computation load per iteration compared to existing sequential learning methods employed in resource-allocation network (RAN) and minimal RAN (MRAN) approaches. The new algorithms, which also incorporate a pruning strategy to control network growth, are evaluated on three different system identification benchmark problems and shown to outperform existing methods both in terms of training error convergence and computational efficiency. (c) 2005 Elsevier B.V. All rights reserved.

Genetic algorithms for local controller network construction

Local Controller Networks (LCNs) provide nonlinear control by interpolating between a set of locally valid, subcontrollers covering the operating range of the plant. Constructing such networks typically requires knowledge of valid local models. This paper describes a new genetic learning approach to the construction of LCNs directly from the dynamic equations of the plant, or from modelling data. The advantage is that a priori knowledge about valid local models is not needed. In addition to allowing simultaneous optimisation of both the controller and validation function parameters, the approach aids transparency by ensuring that each local controller acts independently of the rest at its operating point. It thus is valuable for simultaneous design of the LCNs and identification of the operating regimes of an unknown plant. Application results from a highly nonlinear pH neutralisation process and its associated neural network representation are utilised to illustrate these issues.

Comparison of two construction algorithms for local model networks

The divide-and-conquer approach of local model (LM) networks is a common engineering approach to the identification of a complex nonlinear dynamical system. The global representation is obtained from the weighted sum of locally valid, simpler sub-models defined over small regions of the operating space. Constructing such networks requires the determination of appropriate partitioning and the parameters of the LMs. This paper focuses on the structural aspect of LM networks. It compares the computational requirements and performances of the Johansen and Foss (J&F) and LOLIMOT tree-construction algorithms. Several useful and important modifications to each algorithm are proposed. The modelling performances are evaluated using real data from a pilot plant of a pH neutralization process. Results show that while J&F achieves a more accurate nonlinear representation of the pH process, LOLIMOT requires significantly less computational effort.