Molecular documentation of polyembryony and the micro-spatial dispersion of clonal sibships in the nine-banded armadillo, Dasypus novemcinctus.

A battery of allelic markers at highly polymorphic microsatellite loci was developed and employed to confirm genetically the clonal nature of sibships in nine-banded armadillos. This phenomenon of consistent polyembryony, otherwise nearly unknown among the vertebrates, then was capitalized upon to describe the micro-spatial distributions of numerous clonal sibships in a natural population of armadillos. Adult clonemates were significantly more dispersed than were juvenile sibs, suggesting limited opportunities for altruistic behavioral interactions among mature individuals. These results, and considerations of armadillo natural history, suggest that evolutionary explanations for polyembryony in this species may not reside in the kinds of ecological and kin selection theories relevant to some of the polyembryonic invertebrates. Rather, polyembryony in armadillos may be associated evolutionarily with other reproductive peculiarities of the species, including delayed uterine implantation of a single egg.

Low mating frequency of queens in the stingless bee Scaptotrigona postica and worker maternity of males.

Kin selection models of intracolonial conflict over the maternity of males predict that social hymenopteran workers should favour the production of sons and nephews over brothers when the effective mating frequency (me) of the queen is low (me2. Stingless bees have been used to support these models in that me within the group is considered low and workers are thought often to monopolise the parentage of males. We genetically analysed 20 worker and 20 male pupae from each of 10 colonies of the stingless bee Scaptotrigona postica (= Scaptotrigona aff. depilis) using six microsatellite loci and demonstrate queen monandry in eight nests and apparent low me in the other two. However, four colonies contained an additional matriline, possibly due to queen supersedure (serial polygyny), which complicated their genetic structure. Across colonies, workers were responsible for the maternity of 13% of all males. These data are broadly in agreement with predictions from kin selection theory, though the question remains open as to why workers do not secure a greater share of male maternity in this and other stingless bee species in which workers are more closely related to nephews than brothers.

Queen-worker conflict over male production and sex ratio in a facultatively polyandrous bumble bee, Bombus hypnorum: the consequences of nest usurpation.

Evolutionary conflicts among social hymenopteran nestmates are theoretically likely to arise over the production of males and the sex ratio. Analysis of these conflicts has become an important focus of research into the role of kin selection in shaping social traits of hymenopteran colonies. We employ microsatellite analysis of nestmates of one social hymenopteran, the primitively eusocial and monogynous bumblebee Bombus hypnorum, to evaluate these conflicts. In our 14 study colonies, B. hypnorum queens mated between one and six times (arithmetic mean 2.5). One male generally predominated, fathering most of the offspring, thus the effective number of matings was substantially lower (1–3.13; harmonic mean 1.26). In addition, microsatellite analysis allowed the detection of alien workers, those who could not have been the offspring of the queen, in approximately half the colonies. Alien workers within the same colony were probably sisters. Polyandry and alien workers resulted in high variation among colonies in their sociogenetic organization. Genetic data were consistent with the view that all males (n = 233 examined) were produced by a colony’s queen. Male parentage was therefore independent of the sociogenetic organization of the colony, suggesting that the queen, and not the workers, was in control of the laying of male-destined eggs. The population-wide sex ratio (fresh weight investment ratio) was weakly female biased. No evidence for colony-level adaptive sex ratio biasing could be detected.

Selection Power and Selection Labor for Information Retrieval

This study examines the relation between selection power and selection labor for information retrieval (IR). It is the first part of the development of a labor theoretic approach to IR. Existing models for evaluation of IR systems are reviewed and the distinction of operational from experimental systems partly dissolved. The often covert, but powerful, influence from technology on practice and theory is rendered explicit. Selection power is understood as the human ability to make informed choices between objects or representations of objects and is adopted as the primary value for IR. Selection power is conceived as a property of human consciousness, which can be assisted or frustrated by system design. The concept of selection power is further elucidated, and its value supported, by an example of the discrimination enabled by index descriptions, the discovery of analogous concepts in partly independent scholarly and wider public discourses, and its embodiment in the design and use of systems. Selection power is regarded as produced by selection labor, with the nature of that labor changing with different historical conditions and concurrent information technologies. Selection labor can itself be decomposed into description and search labor. Selection labor and its decomposition into description and search labor will be treated in a subsequent article, in a further development of a labor theoretic approach to information retrieval.

Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.

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.

Gene selection by cooperative competition clustering

Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.