Effects of fungicide dose and mixtures on selection for triazole resistance in Mycosphaerella graminicola under field conditions

The effects of varying doses of fungicides, alone or in mixtures, on selection for triazole resistance were examined under field conditions. Two experiments were conducted using the triazole fungicide fluquinconazole with the strobilurin fungicide azoxystrobin as a mixture partner. Inoculated wheat plots with a known ratio of more sensitive to less sensitive isolates of the leaf blotch fungus Mycosphaerella graminicola were sprayed with fungicide and sampled once symptoms had appeared. Selection for fluquinconazole resistance increased in proportion to the dose, up to one-half of the full dose (the maximum tested) in both experiments. At the higher doses of fluquinconazole, the addition of azoxystrobin was associated with a decrease in selection (nonsignificant in the first experiment) for triazole resistance. Control by low doses of fluquinconazole was increased by mixture with azoxystrobin, but at higher doses mixture with azoxystrobin sometimes decreased control, so that reduced selection was obtained at the cost of some reduction in control. The effects on resistance are not necessarily general consequences of mixing fungicides, and suggest that the properties of any specific mixture may need to be demonstrated experimentally. Selection was inversely related to control in the unmixed treatments in both experiments, but the relationship was weaker in the mixtures with azoxystrobin.

Stress-induced changes in the Schizosaccharomyces pombe proteome using two-dimensional difference gel electrophoresis, mass spectrometry and a novel integrated robotics platform

Robotic and manual methods have been used to obtain identification of significantly changing proteins regulated when Schizosaccharomyces pombe is exposed to oxidative stress. Differently treated S. pombe cells were lysed, labelled with CyDye and analysed by two-dimensional difference gel electrophoresis. Gel images analysed off-line, using the DeCyder image analysis software [GE Healthcare, Amersham, UK] allowed selection of significantly regulated proteins. Proteins displaying differential expression were excised robotically for manual digestion and identified by matrix-assisted laser desorption/ionisation - mass spectrometry (MALDI-MS). Additionally the same set of proteins displaying differential expression were automatically cut and digested using a prototype robotic platform. Automated MALDI-MS, peak label assignment and database searching were utilised to identify as many proteins as possible. The results achieved by the robotic system were compared to manual methods. The identification of all significantly altered proteins provides an annotated peroxide stress-related proteome that can be used as a base resource against which other stress-induced proteomic changes can be compared.

Modelling habitat selection and distribution of the critically endangered Jerdon's courser Rhinoptilus bitorquatus in scrub jungle: an application of a new tracking method

1. Jerdon's courser Rhinoptilus bitorquatus is a nocturnally active cursorial bird that is only known to occur in a small area of scrub jungle in Andhra Pradesh, India, and is listed as critically endangered by the IUCN. Information on its habitat requirements is needed urgently to underpin conservation measures. We quantified the habitat features that correlated with the use of different areas of scrub jungle by Jerdon's coursers, and developed a model to map potentially suitable habitat over large areas from satellite imagery and facilitate the design of surveys of Jerdon's courser distribution. 2. We used 11 arrays of 5-m long tracking strips consisting of smoothed fine soil to detect the footprints of Jerdon's coursers, and measured tracking rates (tracking events per strip night). We counted the number of bushes and trees, and described other attributes of vegetation and substrate in a 10-m square plot centred on each strip. We obtained reflectance data from Landsat 7 satellite imagery for the pixel within which each strip lay. 3. We used logistic regression models to describe the relationship between tracking rate by Jerdon's coursers and characteristics of the habitat around the strips, using ground-based survey data and satellite imagery. 4. Jerdon's coursers were most likely to occur where the density of large (>2 m tall) bushes was in the range 300-700 ha(-1) and where the density of smaller bushes was less than 1000 ha(-1). This habitat was detectable using satellite imagery. 5. Synthesis and applications. The occurrence of Jerdon's courser is strongly correlated with the density of bushes and trees, and is in turn affected by grazing with domestic livestock, woodcutting and mechanical clearance of bushes to create pasture, orchards and farmland. It is likely that there is an optimal level of grazing and woodcutting that would maintain or create suitable conditions for the species. Knowledge of the species' distribution is incomplete and there is considerable pressure from human use of apparently suitable habitats. Hence, distribution mapping is a high conservation priority. A two-step procedure is proposed, involving the use of ground surveys of bush density to calibrate satellite image-based mapping of potential habitat. These maps could then be used to select priority areas for Jerdon's courser surveys. The use of tracking strips to study habitat selection and distribution has potential in studies of other scarce and secretive species.

Stress-induced changes in the Schizosaccharomyces pombe proteome using two-dimensional difference gel electrophoresis, mass spectrometry and a novel integrated robotics platform

Robotic and manual methods have been used to obtain identification of significantly changing proteins regulated when Schizosaccharomyces pombe is exposed to oxidative stress. Differently treated S. pombe cells were lysed, labelled with CyDye (TM) and analysed by two-dimensional difference gel. electrophoresis. Gel images analysed off-line, using the DeCyder (TM) image analysis software [GE Healthcare, Amersham, UK] allowed selection of significantly regulated proteins. Proteins displaying differential expression were excised robotically for manual digestion and identified by matrix-assisted laser desorption/ionisation - mass spectrometry (MALDI-MS). Additionally the same set of proteins displaying differential expression were automatically cut and digested using a prototype robotic platform. Automated MALDI-MS, peak label assignment and database searching were utilised to identify as many proteins as possible. The results achieved by the robotic system were compared to manual methods. The identification of all significantly altered proteins provides an annotated peroxide stress-related proteome that can be used as a base resource against which other stress-induced proteomic changes can be compared.

The production, purification and characterisation of two novel alpha-D-mannosidases from Aspergillus phoenicis

1,6-alpha-D-Mannosidase from Aspergillits phoenicis was purified by anion-exchange chromatography, chromatofocussing and size-exclusion chromatography. The apparent molecular weight was 74 kDa by SDS-PAGE and 81 kDa by native-PAGE. The isoelectric point was 4.6. 1,6-alpha-D-Mannosidase had a temperature optimum of 60 degrees C, a pH optimum of 4.0-4.5. a K-m of 14 mM with alpha-D-Manp-(1 -> 6)-D-Manp as substrate. It was strongly inhibited by Mn2+ and did not need Ca2+ or any other metal cofactor of those tested. The enzyme cleaves specifically (1 -> 6)-linked mannobiose and has no activity towards any other linkages, p-nitrophenyl-alpha-D-mannopyranoside or baker's yeast mannan. 1,3(1,6)-alpha-D-Mannosidase from A. phoenicis was purified by anion-exchange chromatography, chromatofocus sing and size-exclusion chromatography. The apparent molecular weight was 97 kDa by SDS-PAGE and 110 kDa by native-PAGE. The 1,3(1,6)-alpha-D-mannosidase enzyme existed as two charge isomers or isoforms. The isoelectric points of these were 4.3 and 4.8 by isoelectric focussing. It cleaves alpha-D-Manp-(1 -> 3)-D-Manp 10 times faster than alpha-D-Manp-(1 -> 6)-D-Manp, has very low activity towards p-nitrophenyl-alpha-D-mannopyranoside and baker's yeast mannan, and no activity towards alpha-D-Manp-(1 -> 2)-D-Manp. The activity towards (1 -> 3)-linked mannobiose is strongly activated by 1 mM Ca2+ and inhibited by 10 mM EDTA, while (1 -> 6)-activity is unaffected, indicating that the two activities may be associated with different polypeptides. It is also possible that one polypeptide may have two active sites catalysing distinct activities. (c) 2005 Elsevier Ltd. All rights reserved.

A pilot randomised, open, uncontrolled, clinical study of two dosages of St John's wort (Hypericum perforatum) herb extract (LI-160) as an aid to motivational/behavioural support in smoking cessation

There is an association between smoking and depression, yet the herbal antidepressant St John's wort (Hypericum perforatum L.: SJW) herb extract has not previously been investigated as an aid in smoking cessation. In this open, uncontrolled, pilot study, 28 smokers of 10 or more cigarettes per day for at least one year were randomised to receive SJW herb extract (LI-160) 300mg once or twice daily taken for one week before and continued for 3 months after a target quit date. In addition, all participants received motivational/behavioural support from a trained pharmacist. At 3 months, the point prevalence and continuous abstinence rates were both 18%, and at 12 months were 0%. Fifteen participants (54%) reported 23 adverse events up to the end of the 3-month follow-up period. There was no statistically significant difference in the frequency of adverse events for participants taking SJW once or twice daily (p > 0.05). Most adverse events were mild, transient and non-serious. This preliminary study has not provided convincing evidence that a SJW herb extract plus individual motivational/behavioural support is likely to be effective as an aid in smoking cessation. However, it may be premature to rule out a possible effect on the basis of a single, uncontrolled pilot study, and other approaches involving SJW extract may warrant investigation.

The perception of emotion from body movement in point-light displays of interpersonal dialogue

We examined whether it is possible to identify the emotional content of behaviour from point-light displays where pairs of actors are engaged in interpersonal communication. These actors displayed a series of emotions, which included sadness, anger, joy, disgust, fear, and romantic love. In experiment 1, subjects viewed brief clips of these point-light displays presented the right way up and upside down. In experiment 2, the importance of the interaction between the two figures in the recognition of emotion was examined. Subjects were shown upright versions of (i) the original pairs (dyads), (ii) a single actor (monad), and (iii) a dyad comprising a single actor and his/her mirror image (reflected dyad). In each experiment, the subjects rated the emotional content of the displays by moving a slider along a horizontal scale. All of the emotions received a rating for every clip. In experiment 1, when the displays were upright, the correct emotions were identified in each case except disgust; but, when the displays were inverted, performance was significantly diminished for some ernotions. In experiment 2, the recognition of love and joy was impaired by the absence of the acting partner, and the recognition of sadness, joy, and fear was impaired in the non-veridical (mirror image) displays. These findings both support and extend previous research by showing that biological motion is sufficient for the perception of emotion, although inversion affects performance. Moreover, emotion perception from biological motion can be affected by the veridical or non-veridical social context within the displays.

Perceptual cues for orientation in a two finger haptic grasp task

Single point interaction haptic devices do not provide the natural grasp and manipulations found in the real world, as afforded by multi-fingered haptics. The present study investigates a two-fingered grasp manipulation involving rotation with and without force feedback. There were three visual cue conditions: monocular, binocular and projective lighting. Performance metrics of time and positional accuracy were assessed. The results indicate that adding haptics to an object manipulation task increases the positional accuracy but slightly increases the overall time taken.

Construction of tunable radial basis function networks using orthogonal forward selection

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

Ratio selection for classification models

This paper is concerned with the selection of inputs for classification models based on ratios of measured quantities. For this purpose, all possible ratios are built from the quantities involved and variable selection techniques are used to choose a convenient subset of ratios. In this context, two selection techniques are proposed: one based on a pre-selection procedure and another based on a genetic algorithm. In an example involving the financial distress prediction of companies, the models obtained from ratios selected by the proposed techniques compare favorably to a model using ratios usually found in the financial distress literature.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Finding the point on Bezier Curves with the normal vector passing an external point

Two algorithms for finding the point on non-rational/rational Bezier curves of which the normal vector passes through a given external point are presented. The algorithms are based on Bezier curves generation algorithms of de Casteljau's algorithm for non-rational Bezier curve or Farin's recursion for rational Bezier curve, respectively. Orthogonal projections from the external point are used to guide the directional search used in the proposed iterative algorithms. Using Lyapunov's method, it is shown that each algorithm is able to converge to a local minimum for each case of non-rational/rational Bezier curves. It is also shown that on convergence the distance between the point on curves to the external point reaches a local minimum for both approaches. Illustrative examples are included to demonstrate the effectiveness of the proposed approaches.

Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.