Cryptic plasticity underlies a major evolutionary transition

The origin of eusociality is often regarded as a change of macroevolutionary proportions [1, 2]. Its hallmark is a reproductive division of labor between the members of a society: some individuals ("helpers" or "workers") forfeit their own reproduction to rear offspring of others ("queens"). In the Hymenoptera (ants, bees, wasps), there have been many transitions in both directions between solitary nesting and sociality [2-5]. How have such transitions occurred? One possibility is that multiple transitions represent repeated evolutionary gains and losses of the traits underpinning sociality. A second possibility, however, is that once sociality has evolved, subsequent transitions represent selection at just one or a small number of loci controlling developmental switches between preexisting alternative phenotypes [2, 6]. We might then expect transitional populations that can express either sociality or solitary nesting, depending on environmental conditions. Here, we use field transplants to directly induce transitions in British and Irish populations of the sweat bee Halictus rubicundus. Individual variation in social phenotype was linked to time available for offspring production, and to the genetic benefits of sociality, suggesting that helping was not simply misplaced parental care [7]. We thereby demonstrate that sociality itself can be truly plastic in a hymenopteran.

Locally regularised two-stage learning algorithm for RBF network centre selection

Nonlinear models constructed from radial basis function (RBF) networks can easily be over-fitted due to the noise on the data. While information criteria, such as the final prediction error (FPE), can provide a trade-off between training error and network complexity, the tunable parameters that penalise a large size of network model are hard to determine and are usually network dependent. This article introduces a new locally regularised, two-stage stepwise construction algorithm for RBF networks. The main objective is to produce a parsomous network that generalises well over unseen data. This is achieved by utilising Bayesian learning within a two-stage stepwise construction procedure to penalise centres that are mainly interpreted by the noise.

Safe Therapeutic Economic Pharmaceutical Selection (STEPS): development, introduction and use in Northern Ireland

A number of medicine selection methods have been used worldwide for formulary purposes. In Northern Ireland, integrated medicines management is being developed, and related projects have been carried out. This paper deals with the description of the STEPS (Safe Therapeutic Economic Pharmaceutical Selection) programme. The paper outlines the development of STEPS and its application as an element of a cost-effective medicines-management process in Northern Ireland.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.