O3M_Marmot_DATA

This Rdata file provides the data used int the real data application on fitness variation with age in the Alpine marmot as presented in the publication "Testing determinants of the annual individual fitness: an overall mean mixture model for de-lifing data" by Pierre Dupont, Dominique Allainé, Aurélie Cohas & Roger Pradel

Partitioning of the observed microsatellite variation (AMOVA) based on F_ST of <i>Rhododendron ferrugineum</i> populations.

<p>Partitioning of the observed microsatellite variation (AMOVA) based on F_ST of <i>Rhododendron ferrugineum</i> populations.</p

F_ST values among genetic clusters (p-values for all pairs<0.001).

<p>F_ST values among genetic clusters (p-values for all pairs<0.001).</p

Genetic structure of the 33 populations.

<p>a. Plot of Delta K according to K. b. Structure clustering results obtained at K = 2 and K = 6. Each individual is represented by a thin bar corresponding to the sum of assignment probabilities to the K cluster. Black bars separate populations.</p

Geographical information and genetic diversity of <i>Rhododendron ferrugineum</i> populations.

<p>Altitudinal classes are given in the code population (L: low, I: intermediate, H: high altitude).</p><p>n  =  sample size, Ar  =  allelic richness, H_O  =  observed heterozigosity, H_E  =  expected heterozygosity, F_IS  =  within population coefficient of inbreeding (non-significant values are in bold, P<0.01).</p

Microsatellite Marker Analysis Reveals the Complex Phylogeographic History of <i>Rhododendron ferrugineum</i> (Ericaceae) in the Pyrenees

<div><p>Genetic variation within plant species is determined by a number of factors such as reproductive mode, breeding system, life history traits and climatic events. In alpine regions, plants experience heterogenic abiotic conditions that influence the population's genetic structure. The aim of this study was to investigate the genetic structure and phylogeographic history of the subalpine shrub Rhododendron ferrugineum across the Pyrenees and the links between the populations in the Pyrenees, the Alps and Jura Mountains. We used 27 microsatellite markers to genotype 645 samples from 29 Pyrenean populations, three from the Alps and one from the Jura Mountains. These data were used to estimate population genetics statistics such as allelic richness, observed heterozygosity, expected heterozygosity, fixation index, inbreeding coefficient and number of migrants. Genetic diversity was found to be higher in the Alps than in the Pyrenees suggesting colonization waves from the Alps to the Pyrenees. Two separate genetic lineages were found in both the Alps and Pyrenees, with a substructure of five genetic clusters in the Pyrenees where a loss of genetic diversity was noted. The strong differentiation among clusters is maintained by low gene flow across populations. Moreover, some populations showed higher genetic diversity than others and presented rare alleles that may indicate the presence of alpine refugia. Two lineages of R. ferrugineum have colonized the Pyrenees from the Alps. Then, during glaciation events R. ferrugineum survived in the Pyrenees in different refugia such as lowland refugia at the eastern part of the chain and nunataks at high elevations leading to a clustered genetic pattern.</p></div

Location of the sampled populations.

<p>a. Location of the sampled <i>Rhododendron ferrugineum</i> populations in the Alps, Jura Mountains and Pyrenees, b. detailed map of the Pyrenean populations (the colors correspond to the five genetic clusters).</p

Genetic diversity of <i>Rhododendron ferrugineum</i> per genetic cluster.

<p>Ar  =  allelic richness, H_O  =  observed heterozigosity, H_E  =  expected heterozygosity, F_IS  =  within population coefficient of inbreeding.</p

STAMINA: a competition to encourage the development and assessment of software model inference techniques

Models play a crucial role in the development and maintenance of software systems, but are often neglected during the development process due to the considerable manual effort required to produce them. In response to this problem, numerous techniques have been developed that seek to automate the model generation task with the aid of increasingly accurate algorithms from the domain of Machine Learning. From an empirical perspective, these are extremely challenging to compare; there are many factors that are difficult to control (e.g. the richness of the input and the complexity of subject systems), and numerous practical issues that are just as troublesome (e.g. tool availability). This paper describes the StaMinA (State Machine Inference Approaches) competiton, that was designed to address these problems. The competition attracted numerous submissions, many of which were improved or adapted versions of techniques that had not been subjected to extensive empirical evaluations, and had not been evaluated with respect to their ability to infer models of software systems. This paper shows how many of these techniques substantially improve on the state of the art, providing insights into some of the factors that could underpin the success of the best techniques. In a more general sense it demonstrates the potential for competitions to act as a useful basis for empirical software engineering by (a) spurring the development of new techniques and (b) facilitating their comparative evaluation to an extent that would usually be prohibitively challenging without the active participation of the developers

Definitions of allergy used for the classification of children.

<p>Definitions of allergy used for the classification of children.</p