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Southern China is considered an important source of influenza virus pandemics because of the large, diverse viral reservoirs in poultry and swine. To examine the trend in influenza A virus of swine (IAV-S), an active surveillance program has been conducted from 2013 to 2015 in Guangdong, China. The phylogenetic analyses showed that the external genes of the isolates were assigned to the Eurasian avian-like swine (EA) H1N1 and/or human-like H3N2 lineages with multiple substitutions, indicating a notable genetic shift. Moreover, the internal genes derived from different origins (PB2, PB1, PA, NP: pdm/09 (pandemic influenza virus 2009)-origin, M: pdm/09- or EA-origin, NS: North American Triple Reassortant (TR)-origin have become the dominant backbone of IAV-S in southern China. According to the origins of the eight gene segments, the isolates can be categorized into five genotypes. The results of mice experiment showed that the YJ4 (genotype 1) and DG2 (genotype 4) are the most pathogenic to mice, and the viruses are observed in kidneys and brains, indicating the systemic infection. The alterations of the IAV-S gene composition supported the continued implementation of the intensive surveillance of IAV-S and the greater attention focused on potential shifts toward transmission to humans. [ABSTRACT FROM AUTHOR]

Micro-blogging systems have become one of the most important ways for information sharing. Network structure and users’ interactions such as forwarding behaviors have aroused considerable research attention, while mention, as a key feature in micro-blogging platforms which can improve the visibility of a message and direct it to a particular user beyond the underlying social structure, is seldom studied in previous works. In this paper, we empirically study the mention effect in information diffusion, using the dataset from a population-scale social media website. We find that users with high number of followers would receive much more mentions than others. We further investigate the effect of mention in information diffusion by examining the response probability with respect to the number of mentions in a message and observe a saturation at around 5 mentions. Furthermore, we find that the response probability is the highest when a reciprocal followship exists between users, and one is more likely to receive a target user’s response if they have similar social status. To illustrate these findings, we propose the response prediction task and formulate it as a binary classification problem. Extensive evaluation demonstrates the effectiveness of discovered factors. Our results have consequences for the understanding of human dynamics on the social network, and potential implications for viral marketing and public opinion monitoring. [ABSTRACT FROM AUTHOR]