Stable isotope ratios of H, C, N and O in Italian citrus juices.

Stable isotope ratios (SIRs) of C, N, H and O have been exensively used in fruit juices quality control (ENV and AOAC methods) to detect added sugar and the watering down of concentrated juice, practices prohibited by European legislation (EU Directive 2012/12). The European Fruit Juice Association (AIJN) set some reference guidelines in order to allow the judging of the genuiness of a juice. Moreover, various studies have been carried out to determine the natural variability of SIRs in fruit juices, but none of these has investigated SIRs extensively in authentic citrus juices from Italy. In this work, about 500 citrus juice samples were officially collected in Italy by the Italian Ministry of Agricultural and Forestry Policies from 1998 onwards. (D/H)(I) and (D/H)(II) in ethanol and δ(13) C(ethanol), δ(13) C(pulp), δ(13) C(sugars), δ(18) O(vegetalwater), δ(15) N(pulp), and δ(18) O(pulp) were determined using Site-Specific Natural Isotope Fractionation-Nuclear Magnetic Resonance and Isotope Ratio Mass Spectrometry, respectively. The characteristic ranges of variability in SIRs in genuine Italian citrus juice samples are here presented as well as their relationships and compliance with the limits indicated by the AIJN and others proposed in the literature. In particular, the Italian range of values was found to be not completely in agreement with AIJN guidelines, with the risk that genuine juices could be judged as not genuine. Variety seems not to influence SIRs, whereas harvest year and region of origin have some influence on the different ratios, although their data distribution shows overlapping when principal component analysis is applied.

Beyond two communities - from research utilization and knowledge translation to co-production?

In recent public health discourse, the relations between researchers, policy makers, and professionals are often described as 'gaps', illustrating the (cultural) differences between these domains. Such descriptions seem to be part of a 'two communities'-logic: a perception that researchers and policy makers or professionals stem from strictly separated worlds, with distinctive logics, rationales and incentives. In this paper, the author will argue that this prevalent conceptual framework of 'two communities', whilst having been extremely helpful in theorizing the difficulties of connecting policy needs with research findings, bears several important limitations when analysing structural collaboratives between researchers and policy makers or professionals. The paper outlines several problematic assumptions and neglected elements that are generally perceivable within this tradition, such as the analytical a priori separation of research, policy and practice domains and the overemphasis on both the heterogeneity between domains and the homogeneity within these domains. Inductively developed insights from the field of science and technology studies (STS) show that the boundaries between research and policy are often much more fluid (and largely rhetorical) than the two communities tradition seems to acknowledge. What needs to be analysed is not only how the boundaries can be bridged, but how - and at what moments - these boundaries are maintained, redrawn or re-established - and for what purposes. This paper focuses on these questions by utilizing the alternative conceptual framework of coproduction. It draws on long term qualitative research to structural collaborations (the Dutch Academic Collaborative Centres for Public Health) to illustrate these points.

Carbon, hydrogen and oxygen stable isotope ratios of whole wood, cellulose and lignin methoxyl groups of Picea abies as climate proxies.

RATIONALE: Carbon, hydrogen and oxygen (C, H and O) stable isotope ratios of whole wood and components are commonly used as paleoclimate proxies. In this work we consider eight different proxies in order to discover the most suitable wood component and stable isotope ratio to provide the strongest climate signal in Picea abies in a southeastern Alpine region (Trentino, Italy). METHODS: δ(13)C, δ(18)O and δ(2)H values in whole wood and cellulose, and δ(13)C and δ(2)H values in lignin methoxyl groups were measured. Analysis was performed using an Isotopic Ratio Mass Spectrometer coupled with an Elemental Analyser for measuring (13)C/(12)C and a Pyrolyser for measuring (2)H/(1)H and (18)O/(16)O. The data were evaluated by Principal Component Analysis, and a simple Pearson's correlation between isotope chronologies and climatic features, and multiple linear regression were performed to evaluate the data. RESULTS: Each stable isotope ratio in cellulose and lignin methoxyl differs significantly from the same stable isotope ratio in whole wood, the values begin higher in cellulose and lignin except for the lignin δ(2)H values. Significant correlations were found between the whole wood and the cellulose fractions for each isotope ratio. Overall, the highest correlations with temperature were found with the δ(18)O and δ(2)H values in whole wood, whereas no significant correlations were found between isotope proxies and precipitation. CONCLUSIONS: δ(18)O and δ(2)H values in whole wood provide the best temperature signals in Picea abies in the northern Italian study area. Extraction of cellulose and lignin and analysis of other isotopic ratios do not seem to be necessary.

Statistical methods for improving verification of claims of origin for Italian wines based on stable isotope ratios.

Wine derives its economic value to a large extent from geographical origin, which has a significant impact on the quality of the wine. According to the food legislation, wines can be without geographical origin (table wine) and wines with origin. Wines with origin must have characteristics which are essential due to its region of production and must be produced, processed and prepared, exclusively within that region. The development of fast and reliable analytical methods for the assessment of claims of origin is very important. The current official method is based on the measurement of stable isotope ratios of water and alcohol in wine, which are influenced by climatic factors. The results in this paper are based on 5220 Italian wine samples collected in the period 2000-2010. We evaluate the univariate approach underlying the official method to assess claims of origin and propose several new methods to get better geographical discrimination between samples. It is shown that multivariate methods are superior to univariate approaches in that they show increased sensitivity and specificity. In cases where data are non-normally distributed, an approach based on mixture modelling provides additional improvements.

Interpretation of ANOVA models for microarray data using PCA.

MOTIVATION: ANOVA is a technique, which is frequently used in the analysis of microarray data, e.g. to assess the significance of treatment effects, and to select interesting genes based on P-values. However, it does not give information about what exactly is causing the effect. Our purpose is to improve the interpretation of the results from ANOVA on large microarray datasets, by applying PCA on the individual variance components. Interaction effects can be visualized by biplots, showing genes and variables in one plot, providing insight in the effect of e.g. treatment or time on gene expression. Because ANOVA has removed uninteresting sources of variance, the results are much more interpretable than without ANOVA. Moreover, the combination of ANOVA and PCA provides a simple way to select genes, based on the interactions of interest. RESULTS: It is shown that the components from an ANOVA model can be summarized and visualized with PCA, which improves the interpretability of the models. The method is applied to a real time-course gene expression dataset of mesenchymal stem cells. The dataset was designed to investigate the effect of different treatments on osteogenesis. The biplots generated with the algorithm give specific information about the effects of specific treatments on genes over time. These results are in agreement with the literature. The biological validation with GO annotation from the genes present in the selections shows that biologically relevant groups of genes are selected. AVAILABILITY: R code with the implementation of the method for this dataset is available from http://www.cac.science.ru.nl under the heading "Software".

On the use of 1H and 13C 1D NMR spectra as QSPR descriptors.

Recently, 1D NMR and IR spectra have been proposed as descriptors containing 3D information. And, as such, said to be suitable for making QSAR and QSPR models where 3D molecular geometries matter, for example, in binding affinities. This paper presents a study on the predictive power of 1D NMR spectra-based QSPR models using simulated proton and carbon 1D NMR spectra. It shows that the spectra-based models are outperformed by models based on theoretical molecular descriptors and that spectra-based models are not easy to interpret. We therefore conclude that the use of such NMR spectra offers no added value.

Real-life applications of the MULVADO software package for processing DOSY NMR data.

MULVADO is a newly developed software package for DOSY NMR data processing, based on multivariate curve resolution (MCR), one of the principal multivariate methods for processing DOSY data. This paper will evaluate this software package by using real-life data of materials used in the printing industry: two data sets from the same ink sample but of different quality. Also a sample of an organic photoconductor and a toner sample are analysed. Compared with the routine DOSY output from monoexponential fitting, one of the single channel algorithms in the commercial Bruker software, MULVADO provides several advantages. The key advantage of MCR is that it overcomes the fluctuation problem (non-consistent diffusion coefficient of the same component). The combination of non-linear regression (NLR) and MCR can yield more accurate resolution of a complex mixture. In addition, the data pre-processing techniques in MULVADO minimise the negative effects of experimental artefacts on the results of the data. In this paper, the challenges for analysing polymer samples and other more complex samples will also be discussed.

Method for the computational comparison of crystal structures.

A new method for assessing the similarity of crystal structures is described. A similarity measure is important in classification and clustering problems in which the crystal structures are the source of information. Classification is particularly important for the understanding of properties of crystals, while clustering can be used as a data reduction step in polymorph prediction. The method described uses a radial distribution function that combines atomic coordinates with partial atomic charges. The descriptor is validated using experimental data from a classification study of clathrate structures of cephalosporins and data from a polymorph prediction run. In both cases, excellent results were obtained.

Diagnostic analysis of experimental artefacts in DOSY NMR data by covariance matrix of the residuals.

Multivariate curve resolution (MCR) has been applied to separate pure spectra and pure decay profiles of DOSY NMR data. Given good initial guesses of the pure decay profiles, and combined with the nonlinear least square regression (NLR), MCR can result in good separation of the pure components. Nevertheless, due to the presence of artefacts in experimental data, validation of a MCR model is still necessary. In this paper, the covariance matrix of the residuals (CMR), obtained by postmultiplying the residual matrix with its transpose, is proposed to evaluate the quality of the results of an experimental data set. Plots of the rows of this matrix give a general impression of the covariance in the frequency domain of the residual matrix. Different patterns in the plot indicate possible causes of experimental imperfections. This new criterion can be used as diagnosis in order to improve experimental settings as well as suggest appropriate preprocessing of DOSY NMR data.

Improved DOSY NMR data processing by data enhancement and combination of multivariate curve resolution with non-linear least square fitting.

The quality of DOSY NMR data can be improved by careful pre-processing techniques. Baseline drift, peak shift, and phase shift commonly exist in real-world DOSY NMR data. These phenomena seriously hinder the data analysis and should be removed as much as possible. In this paper, a series of preprocessing operations are proposed so that the subsequent multivariate curve resolution can yield optimal results. First, the baseline is corrected according to a method by Golotvin and Williams. Next, frequency and phase shift are removed by a new combination of reference deconvolution (FIDDLE), and a method presented by Witjes et al. that can correct several spectra simultaneously. The corrected data are analysed by the combination of multivariate curve resolution with non-linear least square regression (MCR-NLR). The MCR-NLR method turns out to be more robust and leads to better resolution of the pure components than classic MCR.

Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains.

This study investigated the relation between vegetation reflectance and elevated concentrations of the metals Ni, Cd, Cu, Zn and Pb in river floodplain soils. High-resolution vegetation reflectance spectra in the visible to near-infrared (400-1350 nm) were obtained using a field radiometer. The relations were evaluated using simple linear regression in combination with two spectral vegetation indices: the Difference Vegetation Index (DVI) and the Red-Edge Position (REP). In addition, a multivariate regression approach using partial least squares (PLS) regression was adopted. The three methods achieved comparable results. The best R(2) values for the relation between metals concentrations and vegetation reflectance were obtained for grass vegetation and ranged from 0.50 to 0.73. Herbaceous species displayed a larger deviation from the established relationships, resulting in lower R(2) values and larger cross-validation errors. The results corroborate the potential of hyperspectral remote sensing to contribute to the survey of elevated metal concentrations in floodplain soils under grassland using the spectral response of the vegetation as an indicator. Additional constraints will, however, have to be taken into account, as results are resolution- and location-dependent.

Powder pattern indexing using the weighted crosscorrelation and genetic algorithms.

X-ray diffraction is a powerful technique for investigating the structure of crystals and crystalline powders. Unfortunately, for powders, the first step in the structure elucidation process, retrieving the unit cell parameters (indexing), is still very critical. In the present article, an improved approach to powder pattern indexing is presented. The proposed method matches peak positions from experimental X-ray powder patterns with peak positions from trial cells using a recently published method for pattern comparison (weighted crosscorrelation). Trial cells are optimized with Genetic Algorithms. Patterns are not pretreated to remove any existing zero point shift, as this is determined during optimization. Another improvement is the peak assignment procedure. This assignment is needed for determining the similarity between lines from trial cells and experiment. It no longer allows calculated peaks to be assigned twice to different experimental peaks, which is beneficial for the indexing process. The procedure proves to be robust with respect to false peaks and accidental or systematic absensences of reflections, and is successfully applied to powder patterns originating from orthorhombic, monoclinic, and triclinic compounds measured with synchrotron as well as with conventional laboratory X-ray diffractometers.

The influence of different structure representations on the clustering of an RNA nucleotides data set.

The last couple of years an overwhelming amount of data has emerged in the field of biomolecular structure determination. To explore information hidden in these structure databases, clustering techniques can be used. The outcome of the clustering experiments largely depends, among others, on the way the data is represented; therefore, the choice how to represent the molecular structure information is extremely important. This article describes what the influence of the different representations on the clustering is and how it can be analyzed by means of a dendrogram comparison method. All experiments are performed using a data set consisting of RNA trinucleotides. Besides the most basic structure representation, the Cartesian coordinates representation, several other structure representations are used.

A procedure for incorporating spatial variability in ecological risk assessment of Dutch river floodplains.

Floodplain soils along the river Rhine in the Netherlands show a large spatial variability in pollutant concentrations. For an accurate ecological risk characterization of the river floodplains, this heterogeneity has to be included into the ecological risk assessment. In this paper a procedure is presented that incorporates spatial components of exposure into the risk assessment by linking geographical information systems (GIS) with models that estimate exposure for the most sensitive species of a floodplain. The procedure uses readily available site-specific data and is applicable to a wide range of locations and floodplain management scenarios. The procedure is applied to estimate exposure risks to metals for a typical foodweb in the Afferdensche and Deestsche Waarden floodplain along the river Waal, the main branch of the Rhine in the Netherands. Spatial variability of pollutants is quantified by overlaying appropriate topographic and soil maps resulting in the definition of homogeneous pollution units. Next to that, GIS is used to include foraging behavior of the exposed terrestrial organisms. Risk estimates from a probabilistic exposure model were used to construct site-specific risk maps for the floodplain. Based on these maps, recommendations for future management of the floodplain can be made that aim at both ecological rehabilitation and an optimal flood defense.

Using genetic algorithms for the construction of phylogenetic trees: application to G-protein coupled receptor sequences.

Many different phylogenetic clustering techniques are used currently. One approach is to first determine the topology with a common clustering method and then calculate the branch lengths of the tree. If the resulting tree is not optimal exchanging tree branches can make some local changes in the tree topology. The whole process can be iterated until a satisfactory result has been obtained. The efficiency of this method fully depends on the initially generated tree. Although local changes are made, the optimal tree will never be found if the initial tree is poorly chosen. In this article, genetic algorithms are applied such that the optimal tree can be found even with a bad initial tree topology. This tree generating method is tested by comparing its results with the results of the FITCH program in the PHYLIP software package. Two simulated data sets and a real data set are used.

Sequential assignment of 2D-NMR spectra of proteins using genetic algorithms.

The application of genetic algorithms to the problem of the sequential assignment of two-dimensional protein NMR spectra is discussed. The problem is heavily underconstrained since in most cases more patterns are available than amino acid positions, and uncertainties may exist in the preliminary assignments. The results indicate that relatively large amounts of errors may be present in the input data for the genetic algorithm while useful results may still be obtained.