Variation of fibre diameter coefficient of variation and fibre curvature across mohair fleeces: Implications for animal selection, genetic selection and fleece evaluation

The presentwork aimed to determine howthe average fibre diameter coefficient of variation (CVD) and fibre curvature (FC) differences between nine sampling sites vary between sex and flock, to identify differences in variability between sampling sites as a result of between animal and between sire variability and to determine correlations between sampling sites in between animal and between sire variability. Australian Angoras (n = 313) from two farms in southern Australia were sampled at 12 and 18 months of age at nine sites (mid side, belly, brisket, hind flank, hip, hock, mid back, neck, shoulder). Staples were taken prior to shearing at skin level and CVD and FC determined. For each shearing, differences in CVD and FC between sampling sites, how these differences were affected by farm, sex, and sire, and the covariance between sites for sire and individual animal effects were investigated by restricted maximum likelihood (REML) analyses. The median mid side CVD at 12 and 18 months of age ranged from 23.6 to 25.1% but the actual range was 16.8–34.2%. The median mid side FC at 12 and 18 months of age ranged from 14.4 to 18.6◦/mm but the actual range was 10.5–26.3◦/mm. The general pattern for CVDwas for the mid back, hip and neck sites to have similar CVD, the brisket, hind flank and hock sites to have larger CVD and the belly to have smaller CVD than the mid side site. The between animal variation for CVD was lowest at the mid back site. This implies that the mid back would be the most effective site for between animal selection for CVD. Heritabilities for CVD (range at 18 months 0.18–0.30) were only about half the heritabilities for mean fibre diameter in the same study. There was a marked anterior–posterior increase in FC at both farms and with both ages. The results give no clear indication of the best site for between animal selection for FC, other than that the hock should be avoided. Heritabilities for FC are moderate to high (range at 18 months 0.44–0.77) and the genetic correlations are high except for the hock. Thus genetic selection for FC at any site, other than the hock, should be effective for changing FC over the entire fleece. There was more variability between animals than between sites and sires. These results are put into context with associated research on variation in mean fibre diameter and staple length.

How does ecological disturbance influence genetic diversity?

Environmental disturbance underpins the dynamics and diversity of many of the ecosystems of the world, yet its influence on the patterns and distribution of genetic diversity is poorly appreciated. We argue here that disturbance history may be the major driver that shapes patterns of genetic diversity in many natural populations. We outline how disturbance influences genetic diversity through changes in both selective processes and demographically driven, selectively neutral processes. Our review highlights the opportunities and challenges presented by genetic approaches, such as landscape genomics, for better understanding and predicting the demographic and evolutionary responses of natural populations to disturbance. Developing this understanding is now critical because disturbance regimes are changing rapidly in a human-modified world.

The rough sets feature selection for trees recognition in color aerial images using genetic algorithms

Selecting a set of features which is optimal for a given task is the problem which plays an important role in a wide variety of contexts including pattern recognition, images understanding and machine learning. The concept of reduction of the decision table based on the rough set is very useful for feature selection. In this paper, a genetic algorithm based approach is presented to search the relative reduct decision table of the rough set. This approach has the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and finds the effective feature subset for texture classification . On the basis of the effective feature subset selected, this paper presents a method to extract the objects which are higher than their surroundings, such as trees or forest, in the color aerial images. The experiments results show that the feature subset selected and the method of the object extraction 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.

Sexual selection and individual genetic diversity in a songbird

Here, we report for the first time, to our knowledge, a strong correlation between a measure of individual genetic diversity and song complexity, a sexually selected male trait in sedge warblers, Acrocephalus schoenobaenus. We also find that females prefer to mate with males who will maximize this diversity in individual progeny. The genetic diversity of each offspring is further increased by means of nonrandom fertilization, as we also show that the fertilizing sperm contains a haplotype more genetically distant to that of the egg than expected by chance. These findings suggest that species' mating preferences may be subject to fine tuning aimed at increasing offspring viability through increased genetic diversity. This includes external and internal mechanisms of selection, even within the ejaculate of a single male.

A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data

Background: Feature selection techniques are critical to the analysis of high dimensional datasets. This is especially true in gene selection from microarray data which are commonly with extremely high feature-to-sample ratio. In addition to the essential objectives such as to reduce data noise, to reduce data redundancy, to improve sample classification accuracy, and to improve model generalization property, feature selection also helps biologists to focus on the selected genes to further validate their biological hypotheses.
Results: In this paper we describe an improved hybrid system for gene selection. It is based on a recently proposed genetic ensemble (GE) system. To enhance the generalization property of the selected genes or gene subsets and to overcome the overfitting problem of the GE system, we devised a mapping strategy to fuse the goodness information of each gene provided by multiple filtering algorithms. This information is then used for initialization and mutation operation of the genetic ensemble system.
Conclusion: We used four benchmark microarray datasets (including both binary-class and multi-class classification problems) for concept proving and model evaluation. The experimental results indicate that the proposed multi-filter enhanced genetic ensemble (MF-GE) system is able to improve sample classification accuracy, generate more compact gene subset, and converge to the selection results more quickly. The MF-GE system is very flexible as various combinations of multiple filters and classifiers can be incorporated based on the data characteristics and the user preferences.

Sex-dependent selection differentially shapes genetic variation on and off the guppy Y chromosome

Because selection is often sex-dependent, alleles can have positive effects on fitness in one sex and negative effects in the other, resulting in intralocus sexual conflict. Evolutionary theory predicts that intralocus sexual conflict can drive the evolution of sex limitation, sex-linkage, and sex chromosome differentiation. However, evidence that sex-dependent selection results in sex-linkage is limited. Here, we formally partition the contribution of Y-linked and non-Y-linked quantitative genetic variation in coloration, tail, and body size of male guppies (Poecilia reticulata)—traits previously implicated as sexually antagonistic. We show that these traits are strongly genetically correlated, both on and off the Y chromosome, but that these correlations differ in sign and magnitude between both parts of the genome. As predicted, variation in attractiveness was found to be associated with the Y-linked, rather than with the non-Y-linked component of genetic variation in male ornamentation. These findings show how the evolution of Y-linkage may be able to resolve sexual conflict. More generally, they provide unique insight into how sex-specific selection has the potential to differentially shape the genetic architecture of fitness traits across different parts of the genome.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.