Effect of triclosan or a phenolic farm disinfectant on the selection of antibiotic-resistant Salmonella enterica

Objective: To determine the effect of growth of five strains of Salmonella enterica and their isogenic multiply antibiotic-resistant (MAR) derivatives with a phenolic farm disinfectant or triclosan (biocides) upon the frequency of mutation to resistance to antibiotics or cyclohexane. Methods: Strains were grown in broth with or without the biocides and then spread on to agar containing ampicillin, ciprofloxacin or tetracycline each at 4x MIC or agar overlaid with cyclohexane. Incubation was for 24 and 48 h and the frequency of mutation to resistance was calculated for strains with and without prior growth with the biocides. MICs were determined and the presence of mutations in the acrR and marR regions was determined by sequencing and the presence of mutations in gyrA by light-cycler analysis, for a selection of the mutants that arose. Results: The mean frequency of mutation to antibiotic or cyclohexane resistance was increased similar to10- to 100-fold by prior growth with the phenolic disinfectant or triclosan. The increases were statistically significant for all antibiotics and cyclohexane following exposure to the phenolic disinfectant (P less than or equal to 0.013), and for ampicillin and cyclohexane following exposure to triclosan (P less than or equal to 0.009). Mutants inhibited by >1 mg/L ciprofloxacin arose only from strains that were MAR. Reduced susceptibility to ciprofloxacin (at 4x MIC for parent strains) alone was associated with mutations in gyrA. MAR mutants did not contain mutations in the acrR or marR region. Conclusions: These data renew fears that the use of biocides may lead to an increased selective pressure towards antibiotic resistance.

An effective approach for selection of terrain modelling method

This letter presents an effective approach for selection of appropriate terrain modeling methods in forming a digital elevation model (DEM). This approach achieves a balance between modeling accuracy and modeling speed. A terrain complexity index is defined to represent a terrain's complexity. A support vector machine (SVM) classifies terrain surfaces into either complex or moderate based on this index associated with the terrain elevation range. The classification result recommends a terrain modeling method for a given data set in accordance with its required modeling accuracy. Sample terrain data from the lunar surface are used in constructing an experimental data set. The results have shown that the terrain complexity index properly reflects the terrain complexity, and the SVM classifier derived from both the terrain complexity index and the terrain elevation range is more effective and generic than that designed from either the terrain complexity index or the terrain elevation range only. The statistical results have shown that the average classification accuracy of SVMs is about 84.3% ± 0.9% for terrain types (complex or moderate). For various ratios of complex and moderate terrain types in a selected data set, the DEM modeling speed increases up to 19.5% with given DEM accuracy.

Using genomic DNA-based probe-selection to improve the sensitivity of high-density oligonucleotide arrays when applied to heterologous species

High-density oligonucleotide (oligo) arrays are a powerful tool for transcript profiling. Arrays based on GeneChip® technology are amongst the most widely used, although GeneChip® arrays are currently available for only a small number of plant and animal species. Thus, we have developed a method to improve the sensitivity of high-density oligonucleotide arrays when applied to heterologous species and tested the method by analysing the transcriptome of Brassica oleracea L., a species for which no GeneChip® array is available, using a GeneChip® array designed for Arabidopsis thaliana (L.) Heynh. Genomic DNA from B. oleracea was labelled and hybridised to the ATH1-121501 GeneChip® array. Arabidopsis thaliana probe-pairs that hybridised to the B. oleracea genomic DNA on the basis of the perfect-match (PM) probe signal were then selected for subsequent B. oleracea transcriptome analysis using a .cel file parser script to generate probe mask files. The transcriptional response of B. oleracea to a mineral nutrient (phosphorus; P) stress was quantified using probe mask files generated for a wide range of gDNA hybridisation intensity thresholds. An example probe mask file generated with a gDNA hybridisation intensity threshold of 400 removed > 68 % of the available PM probes from the analysis but retained >96 % of available A. thaliana probe-sets. Ninety-nine of these genes were then identified as significantly regulated under P stress in B. oleracea, including the homologues of P stress responsive genes in A. thaliana. Increasing the gDNA hybridisation intensity thresholds up to 500 for probe-selection increased the sensitivity of the GeneChip® array to detect regulation of gene expression in B. oleracea under P stress by up to 13-fold. Our open-source software to create probe mask files is freely available http://affymetrix.arabidopsis.info/xspecies/ webcite and may be used to facilitate transcriptomic analyses of a wide range of plant and animal species in the absence of custom arrays.

Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data

Seamless phase II/III clinical trials are conducted in two stages with treatment selection at the first stage. In the first stage, patients are randomized to a control or one of k > 1 experimental treatments. At the end of this stage, interim data are analysed, and a decision is made concerning which experimental treatment should continue to the second stage. If the primary endpoint is observable only after some period of follow-up, at the interim analysis data may be available on some early outcome on a larger number of patients than those for whom the primary endpoint is available. These early endpoint data can thus be used for treatment selection. For two previously proposed approaches, the power has been shown to be greater for one or other method depending on the true treatment effects and correlations. We propose a new approach that builds on the previously proposed approaches and uses data available at the interim analysis to estimate these parameters and then, on the basis of these estimates, chooses the treatment selection method with the highest probability of correctly selecting the most effective treatment. This method is shown to perform well compared with the two previously described methods for a wide range of true parameter values. In most cases, the performance of the new method is either similar to or, in some cases, better than either of the two previously proposed methods.

Olfactory selection of Plantago lanceolata by snails declines with seedling age

Background and Aims Despite recent recognition that (1) plant–herbivore interactions during the establishment phase, (2) ontogenetic shifts in resource allocation and (3) herbivore response to plant volatile release are each pivotal to a comprehensive understanding of plant defence, no study has examined how herbivore olfactory response varies during seedling ontogeny. Methods Using a Y-tube olfactometer we examined snail (Helix aspersa) olfactory response to pellets derived from macerated Plantago lanceolata plants harvested at 1, 2, 3, 4, 5, 6 and 8 weeks of age to test the hypothesis that olfactory selection of plants by a generalist herbivore varies with plant age. Plant volatiles were collected for 10 min using solid-phase microextraction technique on 1- and 8-week-old P. lanceolata pellets and analysed by gas chromatography coupled with a mass spectrometer. Key Results Selection of P. lanceolata was strongly negatively correlated with increasing age; pellets derived from 1-week-old seedlings were three times more likely to be selected as those from 8-week-old plants. Comparison of plant selection experiments with plant volatile profiles from GC/MS suggests that patterns of olfactory selection may be linked to ontogenetic shifts in concentrations of green leaf volatiles and ethanol (and its hydrolysis derivatives). Conclusions Although confirmatory of predictions made by contemporary plant defence theory, this is the first study to elucidate a link between seedling age and olfactory selection by herbivores. As a consequence, this study provides a new perspective on the ontogenetic expression of seedling defence, and the role of seedling herbivores, particularly terrestrial molluscs, as selective agents in temperate plant communities.

Flexible selection of a single treatment incorporating short-term endpoint information in a phase II/III clinical trial

Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim analysis have been the focus of much recent research interest. Many of the methods proposed are based on the group sequential approach. This paper considers designs of this type in which the treatment selection can be based on short-term endpoint information for more patients than have primary endpoint data available. We show that in such a case, the familywise type I error rate may be inflated if previously proposed group sequential methods are used and the treatment selection rule is not specified in advance. A method is proposed to avoid this inflation by considering the treatment selection that maximises the conditional error given the data available at the interim analysis. A simulation study is reported that illustrates the type I error rate inflation and compares the power of the new approach with two other methods: a combination testing approach and a group sequential method that does not use the short-term endpoint data, both of which also strongly control the type I error rate. The new method is also illustrated through application to a study in Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

An enhanced content selection mechanism for personalization of video news programmes

In this paper, we propose a content selection framework that improves the users` experience when they are enriching or authoring pieces of news. This framework combines a variety of techniques to retrieve semantically related videos, based on a set of criteria which are specified automatically depending on the media`s constraints. The combination of different content selection mechanisms can improve the quality of the retrieved scenes, because each technique`s limitations are minimized by other techniques` strengths. We present an evaluation based on a number of experiments, which show that the retrieved results are better when all criteria are used at time.

International conference site selection criteria : And a case study of Stockholm as an international conference city

The purpose of this thesis is to identify the destination site selection criteria for internationalconferences from the perspectives of the three main players of the conference industry,conference buyers (organizers and delegates) and suppliers. Additionally, the researchidentifies the strengths and weaknesses of the congress cities of Stockholm and Vienna.Through a comparison with Vienna, the top city for hosting international conferences, a roadmap for Stockholm has been designed, to strengthen its congress tourism opportunities, thus,obtaining a higher status as an international congress city. This qualitative research hascombined both primary and secondary data methods, through semi-standardized expertinterviews and secondary studies respectively, to fulfil the study’s aim. The data have beenanalysed by applying the techniques of qualitative content analysis; the secondary dataadopting an inductive approach according to Mayring (2003) while the expert interviewsusing a deductive approach according to Meuser & Nagel (2009). The conclusions of thesecondary data have been further compared and contrasted with the outcomes of the primarydata, to propose fresh discoveries, clarifications, and concepts related to the site selectioncriteria for international conferences, and for the congress tourism industry of Stockholm. Theresearch discusses the discoveries of the site selection criteria, the implications of thestrengths and weaknesses of Stockholm in comparison to Vienna, recommendations forStockholm via a road map, and future research areas in detail. The findings andrecommendation, not only provide specific steps and inceptions that Stockholm as aninternational conference city can apply, but also propose findings, which can aid conferencebuyers and suppliers to cooperate, to strengthen their marketing strategies and developsuccessful international conferences and destinations to help achieve a greater competitiveadvantage.

Novel Statistical Methods in Quantitative Genetics : Modeling Genetic Variance for Quantitative Trait Loci Mapping and Genomic Evaluation

This thesis develops and evaluates statistical methods for different types of genetic analyses, including quantitative trait loci (QTL) analysis, genome-wide association study (GWAS), and genomic evaluation. The main contribution of the thesis is to provide novel insights in modeling genetic variance, especially via random effects models. In variance component QTL analysis, a full likelihood model accounting for uncertainty in the identity-by-descent (IBD) matrix was developed. It was found to be able to correctly adjust the bias in genetic variance component estimation and gain power in QTL mapping in terms of precision.  Double hierarchical generalized linear models, and a non-iterative simplified version, were implemented and applied to fit data of an entire genome. These whole genome models were shown to have good performance in both QTL mapping and genomic prediction. A re-analysis of a publicly available GWAS data set identified significant loci in Arabidopsis that control phenotypic variance instead of mean, which validated the idea of variance-controlling genes.  The works in the thesis are accompanied by R packages available online, including a general statistical tool for fitting random effects models (hglm), an efficient generalized ridge regression for high-dimensional data (bigRR), a double-layer mixed model for genomic data analysis (iQTL), a stochastic IBD matrix calculator (MCIBD), a computational interface for QTL mapping (qtl.outbred), and a GWAS analysis tool for mapping variance-controlling loci (vGWAS).

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.

System boundary selection in life-cycle inventories using hybrid approaches

Life-cycle assessment (LCA) is a method for evaluating the environmental impacts of products holistically, including direct and supply chain impacts. The current LCA methodologies and the standards by the International Organization for Standardization (ISO) impose practical difficulties for drawing system boundaries; decisions on inclusion or exclusion of processes in an analysis (the cutoff criteria) are typically not made on a scientific basis. In particular, the requirement of deciding which processes could be excluded from the inventory can be rather difficult to meet because many excluded processes have often never been assessed by the practitioner, and therefore, their negligibility cannot be guaranteed. LCA studies utilizing economic input−output analysis have shown that, in practice, excluded processes can contribute as much to the product system under study as included processes; thus, the subjective determination of the system boundary may lead to invalid results. System boundaries in LCA are discussed herein with particular attention to outlining hybrid approaches as methods for resolving the boundary selection problem in LCA. An input−output model can be used to describe at least a part of a product system, and an ISO-compatible system boundary selection procedure can be designed by applying hybrid input−output-assisted approaches. There are several hybrid input−output analysis-based LCA methods that can be implemented in practice for broadening system boundary and also for ISO compliance.

A meta-learning approach to automatic kernel selection for support vector machines

Appropriate choice of a kernel is the most important ingredient of the kernel-based learning methods such as support vector machine (SVM). Automatic kernel selection is a key issue given the number of kernels available, and the current trial-and-error nature of selecting the best kernel for a given problem. This paper introduces a new method for automatic kernel selection, with empirical results based on classification. The empirical study has been conducted among five kernels with 112 different classification problems, using the popular kernel based statistical learning algorithm SVM. We evaluate the kernels’ performance in terms of accuracy measures. We then focus on answering the question: which kernel is best suited to which type of classification problem? Our meta-learning methodology involves measuring the problem characteristics using classical, distance and distribution-based statistical information. We then combine these measures with the empirical results to present a rule-based method to select the most appropriate kernel for a classification problem. The rules are generated by the decision tree algorithm C5.0 and are evaluated with 10 fold cross validation. All generated rules offer high accuracy ratings.

The application of rough set and Kohonen network to feature selection for object extraction

Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The paper describes an application of rough sets method to feature selection and reduction in texture images recognition. The proposed methods include continuous data discretization based on Kohonen neural network and maximum covariance, and rough set algorithms for feature selection and reduction. The experiments on trees extraction from aerial images show that the methods presented in this paper are practical and effective.

An ensemble of classifiers with genetic algorithmBased Feature Selection

Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.

Towards insightful algorithm selection for optimisation using meta-learning concepts

In this paper we propose a meta-learning inspired framework for analysing the performance of meta-heuristics for optimization problems, and developing insights into the relationships between search space characteristics of the problem instances and algorithm performance. Preliminary results based on several meta-heuristics for well-known instances of the Quadratic Assignment Problem are presented to illustrate the approach using both supervised and unsupervised learning methods.