Triticum monococcum lines with distinct metabolic phenotypes and phloem-based partial resistance to the bird cherry-oat aphid Rhopalosiphum padi.

Crop protection is an integral part of establishing food security, by protecting the yield potential of crops. Cereal aphids cause yield losses by direct damage and transmission of viruses. Some wild relatives of wheat show resistance to aphids but the mechanisms remain unresolved. In order to elucidate the location of the partial resistance to the bird cherry-oat aphid, Rhopalosiphum padi, in diploid wheat lines of Triticum monococcum, we conducted aphid performance studies using developmental bioassays and electrical penetration graphs, as well as metabolic profiling of partially resistant and susceptible lines. This demonstrated that the partial resistance is related to a delayed effect on the reproduction and development of R. padi. The observed partial resistance is phloem based and is shown by an increase in number of probes before the first phloem ingestion, a higher number and duration of salivation events without subsequent phloem feeding and a shorter time spent phloem feeding on plants with reduced susceptibility. Clear metabolic phenotypes separate partially resistant and susceptible lines, with the former having lower levels of the majority of primary metabolites, including total carbohydrates. A number of compounds were identified as being at different levels in the susceptible and partially resistant lines, with asparagine, octopamine and glycine betaine elevated in less susceptible lines without aphid infestation. In addition, two of those, asparagine and octopamine, as well as threonine, glutamine, succinate, trehalose, glycerol, guanosine and choline increased in response to infestation, accumulating in plant tissue localised close to aphid feeding after 24 h. There was no clear evidence of systemic plant response to aphid infestation.

Identification of neuroactive steroids and their precursors and metabolites in adult male rat brain.

Steroids in the brain arise both from local synthesis and from peripheral sources and have a variety of effects on neuronal function. However, there is little direct chemical evidence for the range of steroids present in brain or of the pathways for their synthesis and inactivation. This information is a prerequisite for understanding the regulation and function of brain steroids. After extraction from adult male rat brain, we have fractionated free steroids and their sulfate esters and then converted them to heptafluorobutyrate or methyloxime-trimethylsilyl ether derivatives for unequivocal identification and assay by gas chromatography analysis and selected ion monitoring mass spectrometry. In the free steroid fraction, corticosterone, 3alpha,5alpha-tetrahydrodeoxycorticosterone, testosterone, and dehydroepiandrosterone were found in the absence of detectable precursors usually found in endocrine glands, indicating peripheral sources and/or alternative synthetic pathways in brain. Conversely, the potent neuroactive steroid 3alpha,5alpha-tetrahydroprogesterone (allopregnanolone) was found in the presence of its precursors pregnenolone, progesterone, and 5alpha-dihydroprogesterone. Furthermore, the presence of 3beta-, 11beta-, 17alpha-, and 20alpha-hydroxylated metabolites of 3alpha,5alpha-tetrahydroprogesterone implicated possible inactivation pathways for this steroid. The 20alpha-reduced metabolites could also be found for pregnenolone, progesterone, and 5alpha-dihydroprogesterone, introducing a possible regulatory diversion from the production of 3alpha,5alpha-tetrahydroprogesterone. In the steroid sulfate fraction, dehydroepiandrostrone sulfate was identified but not pregnenolone sulfate. Although pharmacologically active, identification of the latter appears to be an earlier methodological artifact, and the compound is thus of doubtful physiological significance in the adult brain. Our results provide a basis for elucidating the origins and regulation of brain steroids.

Merits of random forests emerge in evaluation of chemometric classifiers by external validation.

Real-world applications will inevitably entail divergence between samples on which chemometric classifiers are trained and the unknowns requiring classification. This has long been recognized, but there is a shortage of empirical studies on which classifiers perform best in 'external validation' (EV), where the unknown samples are subject to sources of variation relative to the population used to train the classifier. Survey of 286 classification studies in analytical chemistry found only 6.6% that stated elements of variance between training and test samples. Instead, most tested classifiers using hold-outs or resampling (usually cross-validation) from the same population used in training. The present study evaluated a wide range of classifiers on NMR and mass spectra of plant and food materials, from four projects with different data properties (e.g., different numbers and prevalence of classes) and classification objectives. Use of cross-validation was found to be optimistic relative to EV on samples of different provenance to the training set (e.g., different genotypes, different growth conditions, different seasons of crop harvest). For classifier evaluations across the diverse tasks, we used ranks-based non-parametric comparisons, and permutation-based significance tests. Although latent variable methods (e.g., PLSDA) were used in 64% of the surveyed papers, they were among the less successful classifiers in EV, and orthogonal signal correction was counterproductive. Instead, the best EV performances were obtained with machine learning schemes that coped with the high dimensionality (914-1898 features). Random forests confirmed their resilience to high dimensionality, as best overall performers on the full data, despite being used in only 4.5% of the surveyed papers. Most other machine learning classifiers were improved by a feature selection filter (ReliefF), but still did not out-perform random forests.