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.

Textural fractography of fatigue failures under variable cycle loading

A new concept of counting time at fatigue processes is proposed, aimed to reach fractographic compatibility in cases of different loading sequences. Values of cycle effectivity are summarized to give the new reference time. The improvement is shown in application - textural fractography of three specimens loaded by constant cycle, constant cycle with periodic overloading, and a random block, respectively. In contrast to the conventional crack growth rate, the reference crack growth rate is related to common morphologic features of all fracture surfaces.

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.

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.

Variable incidence angle localized surface plasmon resonance graphene biosensor

This paper investigates the enhancement of sensitivity of variable incidence angle LSPR biosensor by monitoring biomolecular interactions of biotin-streptavidin with gold thin film. The investigation is carried out by means of introducing an additional layer of graphene sheet on top of gold layer (graphene biosensor) and using different coupling configuration of laser beam. The sensitivity, which is indicated by the shift of plasmon resonance angle, increases with graphene deposited onto the gold layers and is linearly related with the number of graphene layers. In addition, an investigation of the shift of plasmon dip is carried out for two different analyte interfaces: air and water. It is found that graphene biosensor has better sensitivity for triangular prism, higher prism angle, and water interface. The evaluation approach involves a plot of a reflectivity curve as a function of the angle of incidence while the operating wavelength is kept fixed.