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.

Identification of sample-specific sequences in mammalian cDNA and genomic DNA by the novel ligation-mediated subtraction (Limes)

The representational difference analysis (RDA) and other subtraction techniques are used to enrich sample-specific sequences by elimination of ubiquitous sequences existing in both the sample of interest (tester) and the subtraction partner (driver). While applying the RDA to genomic DNA of cutaneous lymphoma cells in order to identify tumor relevant alterations, we predominantly isolated repetitive sequences and artificial repeat-mediated fusion products of otherwise independent PCR fragments (PCR hybrids). Since these products severely interfered with the isolation of tester-specific fragments, we developed a considerably more robust and efficient approach, termed ligation-mediated subtraction (Limes). In first applications of Limes, genomic sequences and/or transcripts of genes involved in the regulation of transcription, such as transforming growth factor β stimulated clone 22 related gene (TSC-22R), cell death and cytokine production (caspase-1) or antigen presentation (HLA class II sequences), were found to be completely absent in a cutaneous lymphoma line. On the assumption that mutations in tumor-relevant genes can affect their transcription pattern, a protocol was developed and successfully applied that allows the identification of such sequences. Due to these results, Limes may substitute/supplement other subtraction/comparison techniques such as RDA or DNA microarray techniques in a variety of different research fields.

Sample size determination in combinatorial chemistry.

Combinatorial chemistry is gaining wide appeal as a technique for generating molecular diversity. Among the many combinatorial protocols, the split/recombine method is quite popular and particularly efficient at generating large libraries of compounds. In this process, polymer beads are equally divided into a series of pools and each pool is treated with a unique fragment; then the beads are recombined, mixed to uniformity, and redivided equally into a new series of pools for the subsequent couplings. The deviation from the ideal equimolar distribution of the final products is assessed by a special overall relative error, which is shown to be related to the Pearson statistic. Although the split/recombine sampling scheme is quite different from those used in analysis of categorical data, the Pearson statistic is shown to still follow a chi2 distribution. This result allows us to derive the required number of beads such that, with 99% confidence, the overall relative error is controlled to be less than a pregiven tolerable limit L1. In this paper, we also discuss another criterion, which determines the required number of beads so that, with 99% confidence, all individual relative errors are controlled to be less than a pregiven tolerable limit L2 (0 < L2 < 1).

Sample-to-sample fluctuations of the overlap distributions in the three-dimensional Edwards-Anderson spin glass

We study the sample-to-sample fluctuations of the overlap probability densities from large-scale equilibrium simulations of the three-dimensional Edwards-Anderson spin glass below the critical temperature. Ultrametricity, stochastic stability, and overlap equivalence impose constraints on the moments of the overlap probability densities that can be tested against numerical data. We found small deviations from the Ghirlanda Guerra predictions, which get smaller as system size increases. We also focus on the shape of the overlap distribution, comparing the numerical data to a mean-field-like prediction in which finite-size effects are taken into account by substituting delta functions with broad peaks.

The Use of the Rey 15-Item Test and Recognition Trial to Evaluate Suboptimal Effort in Neuropsychological Assessment with a Pediatric Mild TBI Sample

The utilization of symptom validity tests (SVTs) in pediatric assessment is receiving increasing empirical support. The Rey 15-Item Test (FIT) is an SVT commonly used in adult assessment, with limited research in pediatric populations. Given that FIT classification statistics across studies to date have been quite variable, Boone, Salazar, Lu, Warner-Chacon, and Razani (2002) developed a recognition trial to use with the original measure to enhance accuracy. The current study aims to assess the utility of the FIT and recognition trial in a pediatric mild traumatic brain injury (TBI) sample (N = 112; M = 14.6 years), in which a suboptimal effort base rate of 17% has been previously established (Kirkwood & Kirk, 2010). All participants were administered the FIT as part of an abbreviated neuropsychological evaluation; failure on the Medical Symptom Validity Test (MSVT) was used as the criterion for suspect effort. The traditional adult cut-off score of(99%), but poor sensitivity (6%). When the recognition trial was also utilized, a combination score of(sensitivity = 64%, specificity = 93%). Results indicate that the FIT with recognition trial may be useful in the assessment of pediatric suboptimal effort, at least among relatively high functioning children following mild TBI.