Analysis of genetic variability in a sample of the durum wheat (Triticum durum Desf.) Spanish collection based on gliadin markers

In this work gliadin proteins were used to analyse the genetic variability in a sample of the durum wheat Spanish collection conserved at the CRF-INIA. In total 38 different alleles were identified at the loci Gli-A1, Gli-A3, Gli-B5, Gli-B1, Gli-A2 and Gli-B2. All the gliadin loci were polymorphic, possessed large genetic diversity and small and large differentiation within and between varieties, respectively. The Gli-A2 and Gli-B2 loci were the most polymorphic, the most fixed within varieties and the most useful to distinguish among varieties. Alternatively, Gli-B1 locus presented the least genetic variability out of the four main loci Gli-A1, Gli-B1, Gli-A2 and Gli-B2. The Gli-B1 alleles coding for the gliadin γ-45, associated with good quality, had an accumulated frequency of 69.7%, showing that the Spanish germplasm could be a good source for breeding quality. The Spanish landraces studied showed new gliadin alleles not catalogued so far. These new alleles might be associated with specific Spanish environment factors. The large number of new alleles identified also indicates that durum wheat Spanish germplasm is rather unique.

Sample Size vs. Bias in Defect Prediction

Most empirical disciplines promote the reuse and sharing of datasets, as it leads to greater possibility of replication. While this is increasingly the case in Empirical Software Engineering, some of the most popular bug-fix datasets are now known to be biased. This raises two significants concerns: first, that sample bias may lead to underperforming prediction models, and second, that the external validity of the studies based on biased datasets may be suspect. This issue has raised considerable consternation in the ESE literature in recent years. However, there is a confounding factor of these datasets that has not been examined carefully: size. Biased datasets are sampling only some of the data that could be sampled, and doing so in a biased fashion; but biased samples could be smaller, or larger. Smaller data sets in general provide less reliable bases for estimating models, and thus could lead to inferior model performance. In this setting, we ask the question, what affects performance more? bias, or size? We conduct a detailed, large-scale meta-analysis, using simulated datasets sampled with bias from a high-quality dataset which is relatively free of bias. Our results suggest that size always matters just as much bias direction, and in fact much more than bias direction when considering information-retrieval measures such as AUC and F-score. This indicates that at least for prediction models, even when dealing with sampling bias, simply finding larger samples can sometimes be sufficient. Our analysis also exposes the complexity of the bias issue, and raises further issues to be explored in the future.

Ultrastructure of Plant Leaf Cuticles in relation to Sample Preparation as Observed by Transmission Electron Microscopy

The leaf cuticular ultrastructure of some plant species has been examined by transmission electron microscopy (TEM) in only few studies. Attending to the different cuticle layers and inner structure, plant cuticles have been grouped into six general morphological types. With the aim of critically examining the effect of cuticle isolation and preparation for TEM analysis on cuticular ultrastructure, adaxial leaf cuticles of blue-gum eucalypt, grey poplar, and European pear were assessed, following a membrane science approach. The embedding and staining protocols affected the ultrastructure of the cuticles analysed. The solubility parameter, surface tension, and contact angles with water of pure Spurr's and LR-White resins were within a similar range. Differences were however estimated for resin : solvent mixtures, since Spurr’s resin is combined with acetone and LR-White resin is mixed with ethanol. Given the composite hydrophilic and lipophilic nature of plant cuticles, the particular TEM tissue embedding and staining procedures employed may affect sample ultrastructure and the interpretation of the results in physicochemical and biological terms. It is concluded that tissue preparation procedures may be optimised to facilitate the observation of the micro- and nanostructure of cuticular layers and components with different degrees of polarity and hydrophobicity.