Can transcranial direct current stimulation improve the resistance strength and decrease the rating perceived scale in recreational weight-training experience?

Lattari, E, Andrade, ML, Filho, AS, Moura, AM, Neto, GM, Silva, JG, Rocha, NB, Yuan, T-F, Arias-Carrión, O, and Machado, S. Can transcranial direct current stimulation improve the resistance strength and decrease the rating perceived scale in recreational weight-training experience? J Strength Cond Res 30(12): 3381-3387, 2016-The goal of this study was to evaluate the acute efficacy of anodic transcranial direct current stimulation on the total volume of repetitions and perceived exertion in recreationally trained individuals in strength. The sample consisted of 10 participants trained in exercise against resistance for at least 3 months. Participants underwent elbow flexion exercise at barbell with a specific load of 10 repetition maximum (10RM), responded immediately after the OMNI-RES scale, and were stimulated for 20 minutes with a tDSC protocol (2 mA), depending on randomization. After applying the tDSC, subjects were again subjected to perform elbow flexion with 10RM load and, soon after, again responded to OMNI-RES scale. All subjects underwent the 3 experimental conditions of the study, c-tDSC, a-tDSC, and sham-tDSC, which were randomized. A range of 48-72 hours was allowed between each assessment visit. An interaction to condition and time (F = 52.395; p ≤ 0.001) has shown that repetitions completed after anodic condition were higher compared with the other conditions in the postsession. In relation to perceived exertion, verified by OMNI-RES scale, 2-way analysis of variance for repeated measures showed an interaction between condition and time (F = 28.445; p ≤ 0.001), where the perceived exertion was decreased after the a-tDSC condition and increased after the c-tDSC condition. In strict terms of performance, it seems to be beneficial to attend a session of 20 minutes a-tDSC, when strength training practitioners can no longer support high-volume training and have increased responses in the perceived exertion.info:eu-repo/semantics/publishedVersio

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Neurostimulation, doping, and the spirit of sport

There is increasing interest in using neuro-stimulation devices to achieve an ergogenic effect in elite athletes. Although the World Anti-Doping Authority (WADA) does not currently prohibit neuro-stimulation techniques, a number of researchers have called on WADA to consider its position on this issue. Focusing on trans-cranial direct current stimulation (tDCS) as a case study of an imminent so-called ‘neuro-doping’ intervention, we argue that the emerging evidence suggests that tDCS may meet WADA’s own criteria (pertaining to safety, performance-enhancing effect, and incompatibility with the ‘spirit of sport’) for a method’s inclusion on its list of prohibited substances and methods. We begin by surveying WADA’s general approach to doping, and highlight important limitations to the current evidence base regarding the performance-enhancing effect of pharmacological doping substances. We then review the current evidence base for the safety and efficacy of tDCS, and argue that despite significant shortcomings, it may be sufficient for WADA to consider prohibiting tDCS, in light of the comparable flaws in the evidence base for pharmacological doping substances. In the second half of the paper, we argue that the question of whether WADA ought to ban tDCS turns significantly on the question of whether it is compatible with the ‘spirit of sport’ criterion. We critique some of the previously published positions on this, and advocate our own sport-specific and application-specific approach. Despite these arguments, we finally conclude by suggesting that tDCS ought to be monitored rather than prohibited due to compelling non-ideal considerations

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure

Abstract: Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies

A Deep Learning Approach for Change Points Detection in InSAR Time Series

Interferometric SAR (InSAR) algorithms exploit synthetic aperture radar (SAR) images to estimate ground displacements, which are updated at each new satellite acquisition, over wide areas. The analysis of the resulting time series finds its application, among others, in monitoring tasks regarding seismic faults, subsidence, landslides, and urban structures, for which an accurate and timely response is required. Typical analyses consist of identifying among the numerous time series the ones that exhibit an anomalous displacement, thus deserving to be further investigated. In practice, this is realised by selecting the time series which are characterised by trend changes w.r.t. the historical behaviour. In this work, we propose a Deep Learning approach for change point detection in InSAR time series. The designed architecture combines Long Short-Term Memory (LSTM) cells, to model the temporal correlation among samples in the input time series, and Time-Gated LSTM (TGLSTM) cells, to consider the sampling rate as additional information during learning. We further propose a solution to the lack of ground truth by developing a suitable pipeline for realistic data simulation. The method has been developed and validated through a large suite of experiments. Both quantitative and qualitative analyses have been conducted to demonstrate the detection capabilities of the learned model and how it is a valid alternative to the statistical reference algorithm. We further applied the developed method in a real continuous monitoring project to analyse InSAR time series over the Tuscany region in Italy, proving its effectiveness in the real domain