86,587 research outputs found
Identification of molecular markers for characterization of IGP tomato using HR-NMR spectroscopy
No full textPhaeodactylum tricornutum as a source of value-added products: A review on recent developments in cultivation and extraction technologies
No full textMicroalgae are photosynthesizing organisms that produce high-value metabolites by using CO2 as a feedstock, potentially promoting sustainable manufacturing solutions. They are indeed rich in polyunsaturated fatty acids, pigments, polysaccharides, and proteins, which are widely employed in the pharmaceutical, nutraceutical, cosmetic, and food sectors. Among marine diatoms, Phaeodactylum tricornutum is one of the most studied species and commercially suitable strain for large-scale cultivation thanks to its capacity to accumulate commercially relevant metabolites such as EPA, chrysolaminarin, and fucoxanthin. It can successfully operate phototrophy, but mixotrophy mode was revealed to enhance the growth rate and biomass concentration and quality. This review collects recent findings in cultivation strategies and extraction technologies tested on this species so far. The culture performances are discussed and a comparison between mixotrophic and phototrophic conditions is provided. Finally, successful biorefinery extraction cascades experimented on this diatom are described to foster research in this direction
Simultaneous classification of multiple classes in NMR metabolomics and vibrational spectroscopy using interval-based classification methods:iECVA vs iPLS-DA
No full textInterval based chemometric algorithms have proven to be very powerful for spectral alignments, spectral regressions and spectral classifications. The interval-based methods may not only improve the performance, but also reduce model complexity and enhance the spectral interpretation. Extended Canonical Variate Analysis (ECVA) is a powerful method for multiple group classifications of multivariate data and can easily be extended to an interval approach, iECVA. This study outlines the iECVA method and compares its performance to interval Partial Least Squares Discriminant Analysis (iPLS-DA) on three spectroscopic datasets from Nuclear Magnetic Resonance (NMR), Near Infrared (NIR) and Infrared (IR) spectroscopy, respectively. The results invariantly show that the interval-based classification methods greatly enhance the interpretability of the models by identifying important spectral regions, which facilitate interpretation and biomarker discovery. Although the results for the two methods are similar regarding the number of misclassifications and identified important regions, the model complexity of the PLS-DA proved to consistently lower than the ECVA. The Matlab source codes for both iECVA and iPLS-DA are made freely available at www.models.life.ku.dk
I problemi applicativi della riforma
No full textThe essay explores the problems, advantages and disadvantages, that occur in the application of a new type of guardianship, “amministrazione di sostegno”, instead of the traditional “interdizione”, to patients in hospitals, and in particular to patients who are mentally impaired, weak or insane
<em><em>i</em></em>coshift:a versatile tool for the rapid alignment of 1D NMR spectra
No full textThe increasing scientific and industrial interest towards metabonomics takes advantage from the high qualitative and quantitative information level of nuclear magnetic resonance (NMR) spectroscopy. However, several chemical and physical factors can affect the absolute and the relative position of an NMR signal and it is not always possible or desirable to eliminate these effects a priori. To remove misalignment of NMR signals a posteriori, several algorithms have been proposed in the literature. The icoshift program presented here is an open source and highly efficient program designed for solving signal alignment problems in metabonomic NMR data analysis. The icoshift algorithm is based on correlation shifting of spectral intervals and employs an FFT engine that aligns all spectra simultaneously. The algorithm is demonstrated to be faster than similar methods found in the literature making full-resolution alignment of large datasets feasible and thus avoiding down-sampling steps such as binning. The algorithm uses missing values as a filling alternative in order to avoid spectral artifacts at the segment boundaries. The algorithm is made open source and the Matlab code including documentation can be downloaded from www.models.life.ku.dk. © 2009 Elsevier Inc. All rights reserved
icoshift: an effective tool for the alignment of chromatographic data
No full textThe interval Correlation Optimised shifting algorithm (icoshift) has recently been introduced for the alignment of nuclear magnetic resonance spectra. The method is based on an insertion/deletion model to shift intervals of spectra/chromatograms and relies on an efficient Fast Fourier Transform based computation core that allows the alignment of large data sets in a few seconds on a standard personal computer. The potential of this programme for the alignment of chromatographic data is outlined with focus on the model used for the correction function. The efficacy of the algorithm is demonstrated on a chromatographic data set with 45 chromatograms of 64000 data points. Computation time is significantly reduced compared to the Correlation Optimised Warping (COW) algorithm, which is widely used for the alignment of chromatographic signals. Moreover, icoshift proved to perform better than COW in terms of quality of the alignment (viz. of simplicity and peak factor), but without the need for computationally expensive optimisations of the warping meta-parameters required by COW. Principal Component Analysis (PCA) is used to show how a significant reduction on data complexity was achieved, improving the ability to highlight chemical differences amongst the samples.JRC.I.6 - Systems toxicolog
Metabolic responses of clams, Ruditapes decussatus and Ruditapes philippinarum, to short-term exposure to lead and zinc
No full textThis study investigated the effects of 48h heavy metal exposure upon the metabolic profiles of Ruditapes decussatus and Ruditapes philippinarum using 1H NMR metabolomics. Both species were exposed to increasing concentrations of lead nitrate (10, 40, 60 and 100μg/L) and zinc chloride (20, 50, 100 and 150μg/L), under laboratory conditions. ICP-OES analysis was further performed on the clams' samples in order to verify the occurrence of heavy metal bioaccumulation. With respect to the controls, the metabolic profiles of treated R. decussatus exhibited higher levels of organic osmolytes and lower contents of free amino acids. An opposite behavior was shown by R. philippinarum. In terms of heavy metal, the exposure effects were more evident in the case of Pb rather than Zn. These findings show that NMR-based metabolomics has the required sensitivity and specificity for the identification of metabolites that can act as sensitive indicators of contaminant-induced stres
Identification of molecular markers for characterization of IGP cherry tomatoes of pachino using HR-NMR spectroscopy
No full textChemometric Exploration of Quantitative NMR Data
No full textThis article outlines the synergistic relationship between NMR and chemometrics. The latent variable approach used in chemometrics has proven very powerful for performing inductive explorations of biological systems and for its usefulness insolving industrial problems effectively. This article reviews some of the commonest latent variable approaches applied to the exploratory and predictive modeling of NMR data. It describes how challenging NMR data can be adapted for multivariate data analysis and how the different chemometric methods manipulate the NMR data. The different results from unsupervised data exploration by principal component analysis and multivariate curve resolution are illustrated. On the other hand, many modern applications of NMR within metabolomics and quality control are based on supervised regression analysis or classification analysis. This article demonstrates how these basic chemometric methods work and gives examples of how such methods can be optimized by variable reduction and orthogonal factor extraction. Validation methods and classification performance by the receiver operating characteristics are illustrated. Finally, the potential for merging advanced multiway chemometric methods such as parallel factor analysis (PARAFAC) with the ability of NMR to record true high-order data is emphasized, and illustrated by the application to 2D diffusion-edited spectra of human plasma samples
Metabolic profiling and aquaculture differentiation of gilthead sea bream by 1H NMR metabonomics
No full textThis work describes a metabolic profiling study of gilthead sea bream, from three different aquaculture systems, using 1H NMR and chemometrics. A total of 54 samples under two different storage regimens were analysed. The assignment of all major NMR signals of the perchloric extracts was performed. A comprehensive multivariate data analysis proved able to distinguish the fish metabolism amongst the different aquaculture systems and to determine whether a fish was stored or not. The state of energy metabolism of inosine proved a robust biomarker for evaluating storage time. A new multivariate classification tool, iECVA, revealed several metabolites which are important biomarkers for characterising the three different aquaculture systems: glycogen (stress indicator), histidine, alanine and especially glycine for long storage times and mainly betaine for fresh samples. The findings represent a step forward in understanding how in vivo and postmortem processes affect the total quality of the final product
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