During the epidemic period of the novel H7N9 viruses, an influenza A (H9N2) virus was isolated from a 7-year-old boy with influenza-like illness in Yongzhou city of Hunan province in November 2013. To identify the possible source of infection, environmental specimens collected from local live poultry markets epidemiologically linked to the human case in Yongzhou city were tested for influenza type A and its subtypes H5, H7, and H9 using real-time RT-PCR methods as well as virus isolation, and four other H9N2 viruses were isolated. The real-time RT-PCR results showed that the environment was highly contaminated with avian influenza H9 subtype viruses (18.0%). Sequencing analyses revealed that the virus isolated from the patient, which was highly similar (98.5-99.8%) to one of isolates from environment in complete genome sequences, was of avian origin. Based on phylogenetic and antigenic analyses, it belonged to genotype S and Y280 lineage. In addition, the virus exhibited high homology (95.7-99.5%) of all six internal gene lineages with the novel H7N9 and H10N8 viruses which caused epidemic and endemic in China. Meanwhile, it carried several mammalian adapted molecular residues including Q226L in HA protein, L13P in PB1 protein, K356R, S409N in PA protein, V15I in M1 protein, I28V, L55F in M2 protein, and E227K in NS protein. These findings reinforce the significance of continuous surveillance of H9N2 influenza viruses.

In this paper, the authors show that structured social media data can act as an accurate predictor for wireless data demand patterns at a high spatial-temporal resolution. A casestudy is performed on Greater London covering a 5000km 2 area. The data used includes over 0.6 million geo-tagged Twitter data, over 1 million mobile phone data demand records, and UK census data. The analysis shows that social media activity (Tweets/s n) can accurately predict the long-term traffic demand for both the uplink and downlink channels. The relationship between social media activity and traffic demand obeys a power law and the model explains for over 71-79% of the variance in real traffic demand. This is a significant improvement over existing methods of long-term traffic prediction such as census population data (R 2 =0.57). The authors also show that social media data can also forward predict short-term traffic demand for up to 2 hours on the same day and for the same time in the following 2-3 days.

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A multi-level fluorescence enhancement was presented by a bis–bis(urea)-decorated tetraphenylethene ligand through anion coordination and binding of methyl viologen.