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Toward Power Analysis for Partial Least Squares‐Based Methods
Full text linkIn recent years, power analysis has become widely used in applied sciences, with the increasing importance of the replicability issue. When distribution-free methods, such as partial least squares (PLS)-based approaches, are considered, formulating power analysis is challenging. In this study, we introduce the methodological framework of a new procedure for performing power analysis when PLS-based methods are used. Data are simulated by the Monte Carlo method, assuming the null hypothesis of no effect is false and exploiting the latent structure estimated by PLS in the pilot data. In this way, the complex correlation data structure is explicitly considered in power analysis and sample size estimation. The paper offers insights into selecting test statistics for the power analysis procedure, comparing accuracy-based tests and those based on continuous parameters estimated by PLS. Simulated and real data sets are investigated to show how the method works in practice
NMR assessment of European acacia honey origin and composition of EU-blend based on geographical floral markers
No full textLongitudinal Assessment of Lung Function in BPD Survivors from Birth to Adulthood: The Padova BPD Study
No full textNovel aspects of grape berry ripening and post-harvest withering revealed by untargeted LC-ESI-MS metabolomics analysis
No full textWe established a step-by-step, experimentguidedmetabolomics procedure, based on LC-ESI-MSanalysis, to generate a detailed picture of the changingmetabolic profiles during late berry development in theimportant Italian grapevine cultivar Corvina. We sampledberries from four developmental time points and three postharvesttime points during the withering process, and usedchromatograms of methanolic extracts to test the performanceof the MetAlign and MZmine data mining programs.MZmine achieved a better resolution and thereforegenerated a more useful data matrix. Then both the quantitativeperformance of the analytical platform and thematrix effect were assessed, and the final dataset wasinvestigated by multivariate data analysis. Our analysisconfirmed the results of previous studies but also revealedsome novel findings, including the prevalence of two specificflavonoids in unripe berries and important differencesbetween the developmental profiles of flavones and flavanones,suggesting that specific individual metabolites couldhave different functions, and that flavones and flavanonesprobably play quite distinct biological roles. Moreover, thehypothesis-free multivariate analysis of subsets of the widedata matrix evidentiated the relationships between thevarious classes of metabolites, such as those betweenanthocyanins and hydroxycinnamic acids and betweenflavan-3-ols and anthocyanins
Modification of urinary metabolic profile after oral administration of curcuma extract in rats
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
New findings on the in vivo antioxidant activity of Curcuma longa extract by an integrated 1H NMR and HPLC-MS metabolomic approach
Full text linkCurcuminoids possess powerful antioxidant activity as demonstrated in many chemical in vitro tests and in several in vivo trials. Nevertheless, the mechanism of this activity is not completely elucidated and studies on the in vivo antioxidant effects are still needed. Metabolomics may be used as an attractive approach for such studies and in this paper, we describe the effects of oral administration of a Curcuma longa L. extract (150 mg/kg of total curcuminoids) to 12 healthy rats with particular attention to urinary markers of oxidative stress. The experiment was carried out over 33 days and changes in the 24-h urine samples metabolome were evaluated by H-1 NMR and HPLC-MS. Both techniques produced similar representations for the collected samples confirming our previous study. Modifications of the urinary metabolome lead to the observation of different variables proving the complementarity of 1H NMR and HPLC-MS for metabolomic purposes. The urinary levels of allantoin, m-tyrosine, 8-hydroxy-2'-deoxyguanosine, and nitrotyrosine were decreased in the treated group thus supporting an in vivo antioxidant effect of the oral administration of Curcuma extract to healthy rats. On the other hand, urinary TMAO levels were higher in the treated compared to the control group suggesting a role of curcumin supplementation on microbiota or on TMAO urinary excretion. Furthermore, the urinary levels of the sulphur containing compounds taurine and cystine were also changed suggesting a role for such constituents in the biochemical pathways involved in Curcuma extract bioactivity and indicating the need for further investigation on the complex role of antioxidant curcumin effects
PLS2 in metabolomics
Full text linkMetabolomics is the systematic study of the small-molecule profiles of biological samples produced by specific cellular processes. The high-throughput technologies used in metabolomic investigations generate datasets where variables are strongly correlated and redundancy is present in the data. Discovering the hidden information is a challenge, and suitable approaches for data analysis must be employed. Projection to latent structures regression (PLS) has successfully solved a large number of problems, from multivariate calibration to classification, becoming a basic tool of metabolomics. PLS2 is the most used implementation of PLS. Despite its success, PLS2 showed some limitations when the so called 'structured noise' affects the data. Suitable methods have been recently introduced to patch up these limitations. In this study, a comprehensive and up-to-date presentation of PLS2 focused on metabolomics is provided. After a brief discussion of the mathematical framework of PLS2, the post-transformation procedure is introduced as a basic tool for model interpretation. Orthogonally-constrained PLS2 is presented as strategy to include constraints in the model according to the experimental design. Two experimental datasets are investigated to show how PLS2 and its improvements work in practic
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