Abstract
Metabolomics aims at gathering the maximum amount of metabolic information for a total interpretation of biological systems. A process analytical technology pipeline, combining gas chromatography-mass spectrometry data preprocessing with multivariate analysis, was applied to a Port wine "forced ageing" process under different oxygen saturation regimes at 60 degrees C. It was found that extreme "forced ageing" conditions promote the occurrence of undesirable chemical reactions by production of dioxane and dioxolane isomers, furfural and 5-hydroxymethylfurfural, which affect the quality of the final product through the degradation of the wine aromatic profile, colour and taste. Also, were found high kinetical correlations between these key metabolites with benzaldehyde, sotolon, and many other metabolites that contribute for the final aromatic profile of the Port wine. The use of the kinetical correlations in time-dependent processes as wine ageing can further contribute to biological or chemical systems monitoring, new biomarkers discovery and metabolic network investigations.

Abstract
Port wine is a flagship fortified wine of Portugal, which undergoes a particularly long aging period, developing a dynamic sensory profile over time, responsible for several wine categories, which is dependent upon the type of aging (bottle or barrel). Therefore, the quality of the product is dependent upon the chemical mechanisms occurring during the aging process, such as oxidation or Maillard reactions. To attain the desired quality management, it si necessary to understand how technological parameters, such as temperatures or oxygen exposure, affect the kinetics of the formation of key odorants, such as sotolon. There is a lack of information about hte impact of the storage conditions (oxygen and temperature) on Port wine quality. In this study, the effect of these two parameters were investigated to increase the knowledge database concerning aging management of Port wines. It was found that sotolon formation is highly dependent upon oxygen and temperature. There is however a synergistic effect between these two parameters that could significantly increase the concentration. The kinetic parameters of oxygen, sotolon, and other compounds related to Port aging (cis- and trans-5-hydroxy-2-methyl-1,3-dioxan, 2-furfural, 5-hydroy-methyl-furfural, and 5-methyl-furfural) are also reported. Kinetic models with Monte Carlo simulations, where the oxygen permeability dispersion and temperature are the parameters under evaluation, were applied. On the basis of the modeling predictions, it would seem that the temperature of a cellar would have a more significant impact on the Port wines stored in containers where the oxygen intake is higher (barrels) when compared to containers with low oxygen permeability (bottles using cork stoppers).

Abstract
OpenCL is an open standard for parallel programming of heterogeneous compute devices, such as GPUs, CPUs, DSPs or FPGAs. However, the verbosity of its C host API can hinder application development. In this paper we present cf4ocl, a software library for rapid development of OpenCL programs in pure C. It aims to reduce the verbosity of the OpenCL API, offering straightforward memory management, integrated profiling of events (e.g., kernel execution and data transfers), simple but extensible device selection mechanism and user-friendly error management. We compare two versions of a conceptual application example, one based on cf4ocl, the other developed directly with the OpenCL host API. Results show that the former is simpler to implement and offers more features, at the cost of an effectively negligible computational overhead. Additionally, the tools provided with cf4ocl allowed for a quick analysis on how to optimize the application.

Abstract
The original version of this article unfortunately contained amistake. The presentation of Table 3 was incorrect. The corrected (Table 3) is given below. © 2017, Springer Science+Business Media New York.

Abstract
The R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar high-dimension multivariate observations.

Abstract
Computational models of complex systems are usually elaborate and sensitive to implementation details, characteristics which often affect their verification and validation. Model replication is a possible solution to this issue. It avoids biases associated with the language or toolkit used to develop the original model, not only promoting its verification and validation, but also fostering the credibility of the underlying conceptual model. However, different model implementations must be compared to assess their equivalence. The problem is, given two or more implementations of a stochastic model, how to prove that they display similar behavior? In this paper, we present a model comparison technique, which uses principal component analysis to convert simulation output into a set of linearly uncorrelated statistical measures, analyzable in a consistent, model-independent fashion. It is appropriate for ascertaining distributional equivalence of a model replication with its original implementation. Besides model-independence, this technique has three other desirable properties: a) it automatically selects output features that best explain implementation differences; b) it does not depend on the distributional properties of simulation output; and, c) it simplifies the modelers' work, as it can be used directly on simulation outputs. The proposed technique is shown to produce similar results to the manual or empirical selection of output features when applied to a well-studied reference model.