This study was conducted to test, in mountain running route conditions, the accuracy of the Polar V800™ monitor as a suitable device for monitoring the heart rate variability (HRV) of runners.

Epigenetic changes through altered DNA methylation have been implicated in critical aspects of tumor progression, and have been extensively studied in a variety of cancer types. In contrast, our current knowledge of the aberrant genomic DNA methylation in tumor-associated fibroblasts (TAFs) or other stromal cells that act as critical coconspirators of tumor progression is very scarce. To address this gap of knowledge, we conducted genome-wide DNA methylation profiling on lung TAFs and paired control fibroblasts (CFs) from non-small cell lung cancer patients using the HumanMethylation450 microarray. We found widespread DNA hypomethylation concomitant with focal gain of DNA methylation in TAFs compared to CFs. The aberrant DNA methylation landscape of TAFs had a global impact on gene expression and a selective impact on the TGF-β pathway. The latter included promoter hypermethylation-associated SMAD3 silencing, which was associated with hyperresponsiveness to exogenous TGF-β1 in terms of contractility and extracellular matrix deposition. In turn, activation of CFs with exogenous TGF-β1 partially mimicked the epigenetic alterations observed in TAFs, suggesting that TGF-β1 may be necessary but not sufficient to elicit such alterations. Moreover, integrated pathway-enrichment analyses of the DNA methylation alterations revealed that a fraction of TAFs may be bone marrow-derived fibrocytes. Finally, survival analyses using DNA methylation and gene expression datasets identified aberrant DNA methylation on the EDARADD promoter sequence as a prognostic factor in non-small cell lung cancer patients. Our findings shed light on the unique origin and molecular alterations underlying the aberrant phenotype of lung TAFs, and identify a stromal biomarker with potential clinical relevance.

Liquid chromatography coupled to mass spectrometry (LC/MS) has become widely used in Metabolomics. Several artefacts have been identified during the acquisition step in large LC/MS metabolomics experiments, including ion suppression, carryover or changes in the sensitivity and intensity. Several sources have been pointed out as responsible for these effects. In this context, the drift effects of the peak intensity is one of the most frequent and may even constitute the main source of variance in the data, resulting in misleading statistical results when the samples are analysed. In this article, we propose the introduction of a methodology based on a common variance analysis before the data normalization to address this issue. This methodology was tested and compared with four other methods by calculating the Dunn and Silhouette indices of the quality control classes. The results showed that our proposed methodology performed better than any of the other four methods. As far as we know, this is the first time that this kind of approach has been applied in the metabolomics context. Availability and implementation: The source code of the methods is available as the R package intCor at http://b2slab.upc.edu/software-and-downloads/intensity-drift-correction/ . Supplementary information: Supplementary data are available at Bioinformatics online.