Robustness of Trans-European Gas Networks

Here we uncover the load and fault-tolerant backbones of the trans-European gas pipeline network. Combining topological data with information on inter-country flows, we estimate the global load of the network and its tolerance to failures. To do this, we apply two complementary methods generalized from the betweenness centrality and the maximum flow. We find that the gas pipeline network has grown to satisfy a dual-purpose: on one hand, the major pipelines are crossed by a large number of shortest paths thereby increasing the efficiency of the network; on the other hand, a non-operational pipeline causes only a minimal impact on network capacity, implying that the network is error-tolerant. These findings suggest that the trans-European gas pipeline network is robust, i.e., error tolerant to failures of high load links.Comment: 11 pages, 8 figures (minor changes

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Performance analysis of a parallel, multi-node pipeline for DNA sequencing

Post-sequencing DNA analysis typically consists of read mapping followed by variant calling and is very time-consuming, even on a multi-core machine. Recently, we proposed Halvade, a parallel, multi-node implementation of a DNA sequencing pipeline according to the GATK Best Practices recommendations. The MapReduce programming model is used to distribute the workload among different workers. In this paper, we study the impact of different hardware configurations on the performance of Halvade. Benchmarks indicate that especially the lack of good multithreading capabilities in the existing tools (BWA, SAMtools, Picard, GATK) cause suboptimal scaling behavior. We demonstrate that it is possible to circumvent this bottleneck by using multiprocessing on high-memory machines rather than using multithreading. Using a 15-node cluster with 360 CPU cores in total, this results in a runtime of 1 h 31 min. Compared to a single-threaded runtime of similar to 12 days, this corresponds to an overall parallel efficiency of 53%