BackgroundChagas disease is caused by the protozoan Trypanosoma cruzi and is characterized by heart failure and sudden death. Identifying which factors are involved in evolution and treatment response is actually challenging.Thus, the aim of this work was to determine the Th1/Th17 (IL-6, IL-2, TNF, IL-17 and IFN-γ) and Th2 (IL-4 and IL-10) serum profile in Venezuelan Chagasic patients stratified according amiodarone treatment, hypertension and arrhythmias.MethodsSera from 38 chagasic patients were analyzed to determine the level of cytokines by Multiplexed Bead-Based Immunoassays. ANOVA test was applied to determine differences for each group. Additionally, a Linear Discriminant Analysis (LDA) was applied to observe the accuracy of different cytokines to discriminate between the groups.ResultsThe levels of several cytokines were significantly higher in the high-risk of sudden death and untreated group. LDA showed that IL-2, IFN-γ and IL-10 were the best cytokines for discriminating between high-risk of sudden death and untreated patients versus low-risk of sudden death, treated and control groups.ConclusionsHigh IL-2 levels seem to identify patients with high-risk of sudden death and seems adequate as treatment efficacy marker. To our knowledge, this is the first report about the anti-inflammatory role of the amiodarone in Chagas disease, suggesting an inmunomodulatory effect that may be exploited as coadjutant therapy in chronic Chagas disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-017-2324-x) contains supplementary material, which is available to authorized users.
Time series analysis has remained as an extremely active research area for decades, receiving a great deal of attention from very different domains like econometrics, statistics, engineering, mathematics, medicine and social sciences. To say nothing about its importance in real-world applications in a wide variety of industrial and business scenarios. However, as hardware becomes ubiquitous, the amounts of data collected is more and more overwhelming, bringing us all the so-called big data era. It is in this context where automatic time series analysis deserves especial attention as a mean of making sense of such enormous databases. Nevertheless, the automatic identification of the appropriate data modelling techniques stands in the middle as a compulsory stage of any big data implementation. Research on model selection and combination points out the benefits of such techniques in terms of forecast accuracy and reliability. This study proposes a novel ensemble approach for automatic time series forecasting as a part of a big data implementation. Given a set of alternative models, a Support Vector Machine (SVM) is trained at each forecasting origin to select the best model, according to the computed features and the past performance. The feature space embeds information of the time series itself as well as responses and parameters of the alternative models. This approach will help to reduce the risk of misusing modelling techniques when dealing with big datasets, and at the same time will provide a mechanism to assert the appropriateness of the underlying models used to analyse such data. The effects of the proposed approach are explored empirically using a set of representative forecasting methods and a dataset of 229 weekly demand series from a leading household and personal care UK manufacturer. Findings suggest that the proposed approach results in more robust predictions with lower mean forecasting error and biases than base forecasts.
In this paper an approach for hierarchical time series forecasting based on State Space modelling is proposed. Previous developments provide solutions to the hierarchical forecasting problem by algebra manipulations based on forecasts produced by independent models for each time series involved in the hierarchy. The solutions produce optimal reconciled forecasts for each individual forecast horizon, but the link along time that is implied by the dynamics of the models is completely ignored. Therefore, the novel approach in this paper improves upon past research at least in two key points. Firstly, the algebra is already encoded in the State Space system and the Kalman Filter algorithm, giving an elegant and clean solution to the problem. Secondly, the State Space approach is optimal both across the hierarchy, as expected, but also along time, something missing in past developments. In addition, the present approach provides an unified treatment of top-down, bottom-up, middle-out and reconciled approaches reported in the literature; it generalizes the optimization of hierarchies by proposing combined hierarchies which integrate the previous categories at different segments of the hierarchy; and it allows for multiple hierarchies to be simultaneously adjusted. The approach is assessed by comparing its forecasting performance to the existing methods, through simulations and using real data of a Spanish grocery retailer.
SSpace is a MATLAB toolbox for state space modeling. State space modeling is in itself a powerful and flexible framework for dynamic system modeling, and SSpace is conceived in a way that tries to maximize this flexibility. One of the most salient features is that users implement their models by coding a MATLAB function. In this way, users have complete flexibility when specifying the systems, have absolute control on parameterizations, constraints among parameters, etc. Besides, the toolbox allows for some ways to implement either non-standard models or standard models with non-standard extensions, like heteroskedasticity, time-varying parameters, arbitrary nonlinear relations with inputs, transfer functions without the need of using explicitly the state space form, etc. The toolbox may be used on the basis of scratch state space systems, but is supplied with a number of templates for standard widespread models. A full help system and documentation are provided. The way the toolbox is built allows for extensions in many ways. In order to fuel such extensions and discussions an online forum has been launched.
Condition Monitoring (CM) is the process of determining the state of a system according to a certain number of parameters. This ‘condition' is tracked over time to detect any developing fault or non desired behaviour. As the Information and Communication Technologies (ICT) continue expanding the range of possible applications and gaining industrial maturity, the appearing of new sensor technologies such as Macro Fiber Composites (MFC) has opened a new range of possibilities for addressing a CM in industrial scenarios. The huge amount of data collected by MFC could overflow most conventional monitoring systems, requiring new approaches to take true advantage of the data. Big Data approach makes it possible to take profit of tons of data, integrating in the appropriate algorithms and technologies in a unified platform. This chapter proposes a real time condition monitoring approach, in which the system is continuously monitored allowing an online analysis.
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