Efficacy of a parainfluenza virus 5 (PIV5)-based H7N9 vaccine in mice and guinea pigs: antibody titer towards HA was not a good indicator for protection.

H7N9 has caused fatal infections in humans. A safe and effective vaccine is the best way to prevent large-scale outbreaks in the human population. Parainfluenza virus 5 (PIV5), an avirulent paramyxovirus, is a promising vaccine vector. In this work, we generated a recombinant PIV5 expressing the HA gene of H7N9 (PIV5-H7) and tested its efficacy against infection with influenza virus A/Anhui/1/2013 (H7N9) in mice and guinea pigs. PIV5-H7 protected the mice against lethal H7N9 challenge. Interestingly, the protection did not require antibody since PIV5-H7 protected JhD mice that do not produce antibody against lethal H7N9 challenge. Furthermore, transfer of anti-H7 serum did not protect mice against H7N9 challenge. PIV5-H7 generated high HAI titers in guinea pigs, however it did not protect against H7N9 infection or transmission. Intriguingly, immunization of guinea pigs with PIV5-H7 and PIV5 expressing NP of influenza A virus H5N1 (PIV5-NP) conferred protection against H7N9 infection and transmission. Thus, we have obtained a H7N9 vaccine that protected both mice and guinea pigs against lethal H7N9 challenge and infection respectively.

Combination methods for identifying influential nodes in networks

Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.