Species overlap, speciation and the evolution of aggregation pheromones in bark beetles

Theoretical predictions suggest that species-specific signals used in the attraction of mates should evolve to reduce the risk of mismating and hybridization. These predictions lead to the hypothesis that the signals of spatially overlapping (i.e. sympatric or syntopic) species should differ more substantially than those of non-overlapping species. Earlier studies have tested this prediction for auditory and visual signals. Here we test the hypothesis using olfactory signals, specifically the aggregation pheromones of species from two genera of bark beetles, Dendroctonus and Ips. We found no direct evidence from within these genera regarding the fact that the chemical blends that make up these pheromones differ more substantially in species that overlap in their geographical ranges and/or host-tree use than in allopatric taxa. However, when comparing between genera, the pheromones of overlapping species appear to be more similar than non-overlapping species. We hypothesize that the species of host tree utilized by the beetles may have some influence on their pheromone blends. Additionally, within genera, species that overlap in host use tend to be more closely related than species that use different hosts. These results may provide indirect evidence for an effect of species overlap on the evolution of bark beetle pheromones.

The mode of evolution of aggregation pheromones in Drosophila species

Aggregation pheromones are used by fruit flies of the genus Drosophila to assemble on breeding substrates, where they feed, mate and oviposit communally. These pheromones consist of species-specific blends of chemicals. Here, using a phylogenetic framework, we examine how differences among species in these pheromone blends have evolved. Theoretical predictions, genetic evidence, and previous empirical analysis of bark beetle species, suggest that aggregation pheromones do not evolve gradually, but via major, saltational shifts in chemical composition. Using pheromone data for 28 species of Drosophila we show that, unlike with bark beetles, the distribution of chemical components among species is highly congruent with their phylogeny, with closely related species being more similar in their pheromone blends than are distantly related species. This pattern is also strong within the melanogaster species group, but less so within the virilis species group. Our analysis strongly suggests that the aggregation pheromones of Drosophila exhibit a gradual, not saltational, mode of evolution. We propose that these findings reflect the function of the pheromones in the ecology of Drosophila, which does not hinge on species specificity of aggregation pheromones as signals.

Series feature aggregation for content-based image retrieval

Feature aggregation is a critical technique in content-based image retrieval systems that employ multiple visual features to characterize image content. One problem in feature aggregation is that image similarity in different feature spaces can not be directly comparable with each other. To address this problem, a new feature aggregation approach, series feature aggregation (SFA), is proposed in this paper. In contrast to merging incomparable feature distances in different feature spaces to get aggregated image similarity in the conventional feature aggregation approach, the series feature aggregation directly deal with images in each feature space to avoid comparing different feature distances. SFA is effectively filtering out irrelevant images using individual features in each stage and the remaining images are images that collectively described by all features. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform the parallel feature aggregation and linear distance combination schemes. Furthermore, SFA is able to retrieve more relevant images in top ranked outputs that brings better user experience in finding more relevant images quickly.

An unified framework based on p-norm for feature aggregation in content- based image retrieval

Feature aggregation is a critical technique in content- based image retrieval systems that employ multiple visual features to characterize image content. In this paper, the p-norm is introduced to feature aggregation that provides a framework to unify various previous feature aggregation schemes such as linear combination, Euclidean distance, Boolean logic and decision fusion schemes in which previous schemes are instances. Some insights of the mechanism of how various aggregation schemes work are discussed through the effects of model parameters in the unified framework. Experiments show that performances vary over feature aggregation schemes that necessitates an unified framework in order to optimize the retrieval performance according to individual queries and user query concept. Revealing experimental results conducted with IAPR TC-12 ImageCLEF2006 benchmark collection that contains over 20,000 photographic images are presented and discussed.

Properties of series feature aggregation schemes

Feature aggregation is a critical technique in content-based image retrieval (CBIR) that combines multiple feature distances to obtain image dissimilarity. Conventional parallel feature aggregation (PFA) schemes failed to effectively filter out the irrelevant images using individual visual features before ranking images in collection. Series feature aggregation (SFA) is a new scheme that aims to address this problem. This paper investigates three important properties of SFA that are significant for design of systems. They reveal the irrelevance of feature order and the convertibility of SFA and PFA as well as the superior performance of SFA. Furthermore, based on Gaussian kernel density estimator, the authors propose a new method to estimate the visual threshold, which is the key parameter of SFA. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform conventional PFA schemes.

QDFA : query-dependent feature aggregation for medical image retrieval

We propose a novel query-dependent feature aggregation (QDFA) method for medical image retrieval. The QDFA method can learn an optimal feature aggregation function for a multi-example query, which takes into account multiple features and multiple examples with different importance. The experiments demonstrate that the QDFA method outperforms three other feature aggregation methods.

Properties of series feature aggregation schemes

Feature aggregation is a critical technique in content-based image retrieval (CBIR) that combines multiple feature distances to obtain image dissimilarity. Conventional parallel feature aggregation (PFA) schemes failed to effectively filter out the irrelevant images using individual visual features before ranking images in collection. Series feature aggregation (SFA) is a new scheme that aims to address this problem. This paper investigates three important properties of SFA that are significant for design of systems. They reveal the irrelevance of feature order and the convertibility of SFA and PFA as well as the superior performance of SFA. Furthermore, based on Gaussian kernel density estimator, the authors propose a new method to estimate the visual threshold, which is the key parameter of SFA. Experiments, conducted with IAPR TC-12 benchmark image collection (ImageCLEF2006) that contains over 20,000 photographic images and defined queries, have shown that SFA can outperform conventional PFA schemes.

Transcriptional regulation of the yghJ-pppA-yghG-gspCDEFGHIJKLM cluster, encoding the type II secretion pathway in Enterotoxigenic Escherichia coli

The gene cluster gspCDEFGHIJKLM codes for various structural components of the type II secretion pathway which is responsible for the secretion of heat-labile enterotoxin by enterotoxigenic Escherichia coli (ETEC). In this work, we used a variety of molecular approaches to elucidate the transcriptional organization of the ETEC type II secretion system and to unravel the mechanisms by which the expression of these genes is controlled. We showed that the gspCDEFGHIJKLM cluster and three other upstream genes, yghJ, pppA, and yghG, are cotranscribed and that a promoter located in the upstream region of yghJ plays a major role in the expression of this 14-gene transcriptional unit. Transcription of the yghJ promoter was repressed 168-fold upon a temperature downshift from 37°C to 22°C. This temperature-induced repression was mediated by the global regulatory proteins H-NS and StpA. Deletion mutagenesis showed that the promoter region encompassing positions −321 to +301 relative to the start site of transcription of yghJ was required for full repression. The yghJ promoter region is predicted to be highly curved and bound H-NS or StpA directly. The binding of H-NS or StpA blocked transcription initiation by inhibiting promoter open complex formation. Unraveling the mechanisms of regulation of type II secretion by ETEC enhances our understanding of the pathogenesis of ETEC and other pathogenic varieties of E. coli.

Efficacy of a referral and physical activity program for survivors of prostate cancer [ENGAGE] : rationale and design for a cluster randomised controlled trial

Background: Despite evidence that physical activity improves the health and well-being of prostate cancer survivors, many men do not engage in sufficient levels of activity. The primary aim of this study (ENGAGE) is to determine the efficacy of a referral and physical activity program among survivors of prostate cancer, in terms of increasing participation in physical activity. Secondary aims are to determine the effects of the physical activity program on psychological well-being, quality of life and objective physical functioning. The influence of individual and environmental mediators on participation in physical activity will also be determined.
Methods/Design: This study is a cluster randomised controlled trial. Clinicians of prostate cancer survivors will be randomised into either the intervention or control condition. Clinicians in the intervention condition will refer eligible patients (n = 110) to participate in an exercise program, comprising 12 weeks of supervised exercise sessions and unsupervised physical activity. Clinicians allocated to the control condition will provide usual care to eligible patients (n = 110), which does not involve the recommendation of the physical activity program. Participants will be assessed at baseline, 12 weeks, 6 months, and 12 months on physical activity, quality of life, anxiety, depression, self-efficacy, outcome expectations, goals, and socio-structural factors.
Discussion: The findings of this study have implications for clinicians and patients with different cancer types or other chronic health conditions. It will contribute to our understanding on the potential impact of clinicians promoting physical activity to patients and the long term health benefits of participating in physical activity programs.

Non-monotone averaging aggregation

We advance the theory of aggregation operators and introduce non-monotone aggregation methods based on minimization of a penalty for inputs disagreements. The application in mind is processing data sets which may contain noisy values. Our aim is to filter out noise while at the same time preserve signs of unusual values. We review various methods of robust estimators of location, and then introduce a new estimator based on penalty minimisation.