The nucleon intensity in the atmosphere and the Pt distribution

The diffusion equation for cosmic ray nucleons in the atmosphere has been solved analytically, taking into account the transverse momentum distribution of nucleons produced in nucleon-air nucleus collisions. The effect of the transverse momentum distribution increases the nucleon intensity at large zenith angles and low energies

Estimation of composition of quinoa (Chenopodium quinoa Willd.) grains by Near-Infrared Transmission spectroscopy

The aim of this study was to develop robust chemometric models for the routine determination of dietary constituents of quinoa (Chenopodium quinoa Willd.) using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa grains of 77 cultivars were acquired while dietary constituents were determined by reference methods. Spectra were subjected to multiplicative scatter correction (MSC) or extended multiplicative signal correction (EMSC), and were (or not) treated by Savitzky-Golay (SG) filters. Latent variables were extracted by partial least squares regression (PLSR) or canonical powered partial least squares (CPPLS) algorithms, and the accuracy and predictability of all modelling strategies were compared. Smoothing the spectra improved the accuracy of the models for fat (root mean square error of cross-validation, RMSECV: 0.3190.327%), ashes (RMSECV: 0.2240.230%), and particularly for protein (RMSECV: 0.5180.564%) and carbohydrates (RMSECV: 0.5420.559%), while enhancing the prediction performance, particularly, for fat (root mean square error of prediction, RMSEP: 0.2480.335%) and ashes (RMSEP: 0.1370.191%). Although the highest predictability was achieved for ashes (SG-filtered EMSC/PLSR: bootstrapped 90% confidence interval for RMSEP: [0.3760.512]) and carbohydrates (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6510.901]), precision was acceptable for protein (SG-filtered MSC/CPPLS: 90% CI RMSEP: [0.6500.852]), fat (SG-filtered EMSC/CPPLS: 90% CI RMSEP: [0.4780.654]) and moisture (non-filtered EMSC/PLSR: 90% CI RMSEP: [0.6580.833]).Mr. Encina-Zelada acknowledges the financial aid provided by the Peruvian National Programme of Scholarships and Student Loans (PRONABEC) in the mode of PhD grants (Presidente de La República Grant Number 183308). Dr. Gonzales-Barron wishes to acknowledge the financial support provided by the Portuguese Foundation for Science and Technology (FCT) through the award of a five-year Investigator Fellowship (IF) in the mode of Development Grants (IF/00570)

Estimation of Proximate Composition of Quinoa (Chenopodium quinoa, Willd.) Flour by Near-Infrared Transmission Spectroscopy

The aim of this study was to develop chemometric models for protein, fat, ashes and carbohydrates contents of quinoa flour using Near-Infrared Transmission (NIT) spectroscopy. Spectra of quinoa flour obtained from grains of 70 different cultivars were scanned while dietary constituents were determined by reference AOAC methods. As a pre-treatment, spectra were subjected to extended multiplicative signal correction (EMSC) with polynomial degree 0, 1 or 2. Next, the Canonical Powered Partial Least Squares (CPPLS) algorithm was applied, and models were compared in terms of accuracy and predictability. For all models, root mean square errors of cross-validation (RMSECV), root meat square errors of prediction (RMSEP) and coefficient of correlation of cross-validation (RCV) were computed. Robust models were obtained when quinoa spectra were pre-processed using EMSC of polynomial degree 2 for both fat (RMSECV: 0.268% and RMSEP: 0.256%) and carbohydrates (RMSECV: 0.641% and RMSEP: 0.643%) following extraction of five CPPLS latent variables. Good coefficients of correlation of prediction (RP: 0.6900.821) were found for all constituents when models were validated on a test data set consisting of 13 quinoa flour spectra. Thus, good predictions of the dietary constituents of quinoa flour could be achieved by using NIT technology, as implied by the low coefficient of variation of prediction (CVP): 5.64% for protein, 3.88% for fat 7.32% for ashes and 0.80% for carbohydrates contents.info:eu-repo/semantics/publishedVersio

Arbuscular Mycorrhizal Fungi and Plant Chemical Defence : Effects of Colonisation on Aboveground and Belowground Metabolomes

Arbuscular mycorrhizal fungal (AMF) colonisation of plant roots is one of the most ancient and widespread interactions in ecology, yet the systemic consequences for plant secondary chemistry remain unclear. We performed the first metabolomic investigation into the impact of AMF colonisation by Rhizophagus irregularis on the chemical defences, spanning above- and below-ground tissues, in its host-plant ragwort (Senecio jacobaea). We used a non-targeted metabolomics approach to profile, and where possible identify, compounds induced by AMF colonisation in both roots and shoots. Metabolomics analyses revealed that 33 compounds were significantly increased in the root tissue of AMF colonised plants, including seven blumenols, plant-derived compounds known to be associated with AMF colonisation. One of these was a novel structure conjugated with a malonyl-sugar and uronic acid moiety, hitherto an unreported combination. Such structural modifications of blumenols could be significant for their previously reported functional roles associated with the establishment and maintenance of AM colonisation. Pyrrolizidine alkaloids (PAs), key anti-herbivore defence compounds in ragwort, dominated the metabolomic profiles of root and shoot extracts. Analyses of the metabolomic profiles revealed an increase in four PAs in roots (but not shoots) of AMF colonised plants, with the potential to protect colonised plants from below-ground organisms

Mining for genotype-phenotype relations in Saccharomyces using partial least squares

<p>Abstract</p> <p>Background</p> <p>Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations.</p> <p>Results</p> <p>Applying this methodology to an extensive data set for the model yeast <it>Saccharomyces cerevisiae</it>, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on <it>Saccharomyces </it>yeasts recent adaptation to environmental changes in its ecological niche.</p> <p>Conclusions</p> <p>BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.</p