The high computational cost of current assembly methods for the long, noisy single molecular sequencing (SMS) reads has prevented them from assembling large genomes. We introduce an ultra-fast alignment method based on a novel global alignment score. For large human SMS data, our method is 7X faster than MHAP for pairwise alignment and 15X faster than BLASR for reference mapping. We develop a Mapping, Error Correction and de novo Assembly Tool (MECAT) by integrating our new alignment and error correction methods, with the Celera Assembler. MECAT is capable of producing high quality de novo assembly of large genome from SMS reads with low computational cost. MECAT produces reference-quality assemblies of Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster and reconstructs the human CHM1 genome with 15% longer NG50 in only 7600 CPU core hours using 54X SMS reads and a Chinese Han genome in 19200 CPU core hours using 102X SMS reads.
The development of network technology has created various platforms and methods for information dissemination. When rumors spread in social networks, they will rapidly spread and may cause social harm. Also, there are groups in social networks that create and spread rumors for the purpose of profit, thus expanding the scope of rumors. Therefore, based on the theory of complex network propagation dynamics, the study of the propagation law of rumors and the design of effective prevention and control strategies is of practical importance and theoretical significance for understanding the propagation laws of rumors and controlling the outbreak of rumors. The spreading process of rumors on social network platforms is focused here. The intentional spreader based on the classic rumor-spreading model is introduced. First, 2SIR rumor-spreading models on homogeneous and heterogeneous networks are established, respectively. Second, the steady-state analysis was separately carried out, and the corresponding propagation critical value was obtained: in the homogeneous network, the condition for the large-scale spread of rumors is α > m / k ¯ or β > δ / k ¯ ; in the heterogeneous network, the condition for the large-scale spread of rumors is α > m k ¯ / k 2 ¯ or β > δ k ¯ / k 2 ¯ . Finally, the simulation calculation and model feasibility verification were carried out on the model. The results show that the theoretical propagation threshold corresponds with the simulation results. According to the simulation results, the final influence of rumors has significantly decreased with decreasing values of β (intentional spreading rate) instead of α (unintentional spreading rate). It can be concluded that in the real-life rumor control process, more resources need to be invested in reducing the rate of intentional transmission instead of being indiscriminately put on controlling all spreaders of rumors.
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