Arcanobacterium hippocoleae sp. nov., from the vagina of a horse

A polyphasic taxonomic study was performed on a previously unidentified gram-positive, facultatively anaerobic, diphtheroid-shaped organism isolated from a vaginal discharge of a horse. Comparative 16S rRNA gene sequencing demonstrated that the strain was a member of the genus Arcanobacterium, but sequence divergence values of >4% with described species of this genus (viz: Arcanobacterium haemolyticum, Arcanobacterium bernardiae, Arcanobacterium phocae, Arcanobacterium pluranimalium and Arcanobacterium pyogenes) demonstrated that the isolate represented a novel species. The unknown bacterium was readily distinguished from other Arcanobacterium species by biochemical tests. Based on phylogenetic and phenotypic evidence, it is proposed that the unknown bacterium be classified as Arcanobacterium hippocoleae sp. nov. The type strain of A. hippocoleae is CCUG 44697T (= CIP 106850T).

A stable one-step-ahead predictive control of non-linear systems

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input–output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model.

Stable receding horizon control based on recurrent networks

The last decade has seen the re-emergence of artificial neural networks as an alternative to traditional modelling techniques for the control of nonlinear systems. Numerous control schemes have been proposed and have been shown to work in simulations. However, very few analyses have been made of the working of these networks. The authors show that a receding horizon control strategy based on a class of recurrent networks can stabilise nonlinear systems.

Equine anthelmintics: survey of the patterns of use, beliefs and attitudes among horse owners in the UK

An online survey was conducted to establish horse owners' beliefs, attitudes and practices relating to the use of anthelmintic drugs. Out of a total of 574 respondents, 89 per cent described themselves as ‘leisure riders’, most of whom took part in a variety of activities including eventing, show jumping, dressage, hunter trials, hunting, driving, endurance and showing. Overall, respondents were generally aware and concerned about the issue of anthelmintic resistance. Less than 60 per cent of all respondents were comfortable with their existing anthelmintic programme, and 25 per cent would like to reduce the use of anthelmintics in their horses. Of all the respondents, 47 per cent used livery, and 49 per cent of those reported that the livery imposed a common anthelmintic programme for horses kept on the premises; 45 per cent of these respondents were not entirely happy with the livery yard's programme. Less than 50 per cent of all respondents included ‘veterinary surgeon’ among their sources of advice on worming.

Stable linearization using multilayer neural networks

The main limitation of linearization theory that prevents its application in practical problems is the need for an exact knowledge of the plant. This requirement is eliminated and it is shown that a multilayer network can synthesise the state feedback coefficients that linearize a nonlinear control affine plant. The stability of the linearizing closed loop can be guaranteed if the autonomous plant is asymptotically stable and the state feedback is bounded.

Stable reduced-order models for discrete-time systems

The Routh-stability method is employed to reduce the order of discrete-time system transfer functions. It is shown that the Routh approximant is well suited to reduce both the denominator and the numerator polynomials, although alternative methods, such as PadÃ�Â(c)-Markov approximation, are also used to fit the model numerator coefficients.

Fully automated software solution for protein quantitation by global metabolic labeling with stable isotopes

Metabolic stable isotope labeling is increasingly employed for accurate protein (and metabolite) quantitation using mass spectrometry (MS). It provides sample-specific isotopologues that can be used to facilitate comparative analysis of two or more samples. Stable Isotope Labeling by Amino acids in Cell culture (SILAC) has been used for almost a decade in proteomic research and analytical software solutions have been established that provide an easy and integrated workflow for elucidating sample abundance ratios for most MS data formats. While SILAC is a discrete labeling method using specific amino acids, global metabolic stable isotope labeling using isotopes such as (15)N labels the entire element content of the sample, i.e. for (15)N the entire peptide backbone in addition to all nitrogen-containing side chains. Although global metabolic labeling can deliver advantages with regard to isotope incorporation and costs, the requirements for data analysis are more demanding because, for instance for polypeptides, the mass difference introduced by the label depends on the amino acid composition. Consequently, there has been less progress on the automation of the data processing and mining steps for this type of protein quantitation. Here, we present a new integrated software solution for the quantitative analysis of protein expression in differential samples and show the benefits of high-resolution MS data in quantitative proteomic analyses.