Abstract
A new, fiber-based, cavity ring-down topology is presented which enables the application of the cavity ring-down technique to remote sensing, by the use of a large cavity ring and an optical circulator. For a proof of concept a 1.5 km ring is assembled and a taper is used as a sensing head for measuring displacement. The cavity ring-down technique is seen to hold some potential for remote sensing through its implementation on optical fibers.

Abstract
The present study had the following objectives: i) to examine the inter-scale correlations between the three dimensions of responses (intensity, direction and frequency) of the CSAI-2R and its relationship with competitive experience, and ii) evaluate the effect of competitive experience anxiety (cognitive and somatic) and self-confidence in the total sample and for different types of modalities (individual vs. team). The sample consisted of 267 athletes (196 male and 71 female), of different sports, aged be-tween 18 and 40 years (M = 24.30, SD = 5.62). Athletes completed the Brazilian version of the CSAI-2, which included the addition of the dimensions of direction and frequency response. Spearman test and Manova were used for the data analysis. Overall, it was found that the competitive experience has a high multivariate and significant effect on the dimensions of competitive anxiety. Both individual and team athletes with low competitive experience showed a trend to report lower levels of self-confidence intensity, compared to counterparts with high competitive experience. These results were discussed in view of the theoretic framework and practical implications planning Sport Psychology intervention programs in local athletes with different backgrounds. Copyright © 2014 Fundação Técnica e Científica do Desporto.

Abstract
Kolmogorov complexity (K) is an incomputable function. It can be approximated from above but not to arbitrary given precision and it cannot be approximated from below. By restricting the source of the data to a specific model class, we can construct a computable function (kappa) over bar to approximate K in a probabilistic sense: the probability that the error is greater than kappa decays exponentially with kappa. We apply the same method to the normalized information distance (NID) and discuss conditions that affect the safety of the approximation.

Abstract
The Wind Scanner e-Science platform architecture and the underlying premises are discussed. It is a collaborative platform that will provide a repository for experimental data and metadata. Additional data processing capabilities will be incorporated thus enabling in-situ data processing. Every resource in the platform is identified by a Uniform Resource Identifier (URI), enabling an unequivocally identification of the field(s) campaign(s) data sets and metadata associated with the data set or experience. This feature will allow the validation of field experiment results and conclusions as all managed resources will be linked. A centralised node (Hub) will aggregate the contributions of 6 to 8 local nodes from EC countries and will manage the access of 3 types of users: data-curator, data provider and researcher. This architecture was designed to ensure consistent and efficient research data access and preservation, and exploitation of new research opportunities provided by having this "Collaborative Data Infrastructure". The prototype platform-WindS@UP-enables the usage of the platform by humans via a Web interface or by machines using an internal API (Application Programming Interface). Future work will improve the vocabulary ("application profile") used to describe the resources managed by the platform.

Abstract

Abstract
Accurately quantifying gene expression levels is a key goal of experiments using RNA-sequencing to assay the transcriptome. This typically requires aligning the short reads generated to the genome or transcriptome before quantifying expression of pre-defined sets of genes. Differences in the alignment/quantification tools can have a major effect upon the expression levels found with important consequences for biological interpretation. Here we address two main issues: do different analysis pipelines affect the gene expression levels inferred from RNA-seq data? And, how close are the expression levels inferred to the "true" expression levels? We evaluate fifty gene profiling pipelines in experimental and simulated data sets with different characteristics (e. g, read length and sequencing depth). In the absence of knowledge of the 'ground truth' in real RNAseq data sets, we used simulated data to assess the differences between the "true" expression and those reconstructed by the analysis pipelines. Even though this approach does not take into account all known biases present in RNAseq data, it still allows to estimate the accuracy of the gene expression values inferred by different analysis pipelines. The results show that i) overall there is a high correlation between the expression levels inferred by the best pipelines and the true quantification values; ii) the error in the estimated gene expression values can vary considerably across genes; and iii) a small set of genes have expression estimates with consistently high error (across data sets and methods). Finally, although the mapping software is important, the quantification method makes a greater difference to the results.