The digital economy is considered as an effective measure to mitigate the negative economic impact of the Corona Virus Disease 2019 (COVID-19) epidemic. However, few studies evaluated the role of digital economy on the economic growth of countries along the “Belt and Road” and the impact of COVID-19 on their digital industries. This study constructed a comprehensive evaluation index system and applied a panel data regression model to empirically analyze the impact of digital economy on the economic growth of countries along the “Belt and Road” before COVID-19. Then, a Global Trade Analysis Project (GTAP) model was used to examine the impact of COVID-19 on their digital industries and trade pattern. Our results show that although there is an obvious regional imbalance in the digital economy development in countries along the “Belt and Road”, the digital economy has a significantly positive effect on their economic growth. The main impact mechanism is through promoting industrial structure upgrading, the total employment and restructuring of employment. Furthermore, COVID-19 has generally boosted the demand for the digital industries, and the impact from the demand side is much larger than that from the supply side. Specifically, the digital industries in Armenia, Israel, Latvia and Estonia have shown great growth potential during the epidemic. On the contrast, COVID-19 has brought adverse impacts to the digital industries in Ukraine, Egypt, Turkey, and the Philippines. The development strategies are proposed to bridge the “digital divide” of countries along the “Belt and Road,” and to strengthen the driving effect of the digital economy on industrial upgrading, employment and trade in the post-COVID-19 era.

Leptospirosis, caused by pathogenic Leptospira, is a worldwide zoonotic infection. The genus Leptospira includes at least 21 species clustered into three groups—pathogens, non-pathogens, and intermediates—based on 16S rRNA phylogeny. Research on Leptospira is difficult due to slow growth and poor transformability of the pathogens. Recent identification of extrachromosomal elements besides the two chromosomes in L. interrogans has provided new insight into genome complexity of the genus Leptospira. The large size, low copy number, and high similarity of the sequence of these extrachromosomal elements with the chromosomes present challenges in isolating and detecting them without careful genome assembly. In this study, two extrachromosomal elements were identified in L. borgpetersenii serovar Ballum strain 56604 through whole genome assembly combined with S1 nuclease digestion following pulsed-field gel electrophoresis (S1-PFGE) analysis. Further, extrachromosomal elements in additional 15 Chinese epidemic strains of Leptospira, comprising L. borgpetersenii, L. weilii, and L. interrogans, were successfully separated and identified, independent of genome sequence data. Southern blot hybridization with extrachromosomal element-specific probes, designated as lcp1, lcp2 and lcp3-rep, further confirmed their occurrences as extrachromosomal elements. In total, 24 plasmids were detected in 13 out of 15 tested strains, among which 11 can hybridize with the lcp1-rep probe and 11 with the lcp2-rep probe, whereas two can hybridize with the lcp3-rep probe. None of them are likely to be species-specific. Blastp search of the lcp1, lcp2, and lcp3-rep genes with a nonredundant protein database of Leptospira species genomes showed that their homologous sequences are widely distributed among clades of pathogens but not non-pathogens or intermediates. These results suggest that the plasmids are widely distributed in Leptospira species, and further elucidation of their biological significance might contribute to our understanding of biology and infectivity of pathogenic spirochetes.

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Based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bat algorithm (BA) to optimize the support vector machine, this paper proposed a combined model for short-term wind speed forecasting to predict the wind speed more accurately. Firstly, CEEMDAN was used to decompose the original wind speed time series into a series of subsequences with different frequencies. Secondly, the decomposed subsequences were forecasted by combined model of BA-SVM. Finally, the wind speed forecasting results was achieved by superposing each predicted subsequence. The simulation results suggest that the model improves the prediction accuracy and reduces the error.

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