Development of mouse monoclonal antibody for detecting hemagglutinin of avian influenza A(H7N9) virus and preventing virus infection.

Many cases of avian influenza A(H7N9) virus infection in humans have been reported since its first emergence in 2013. The disease is of concern because most patients have become severely ill with roughly 30% mortality rate. Because the threat in public health caused by H7N9 virus remains high, advance preparedness is essentially needed. In this study, the recombinant H7N9 hemagglutinin (HA) was expressed in insect cells and purified for generation of two monoclonal antibodies, named F3-2 and 1C6B. F3-2 can only recognize the H7N9 HA without having cross-reactivity with HA proteins of H1N1, H3N2, H5N1, and H7N7. 1C6B has the similar specificity with F3-2, but 1C6B can also bind to H7N7 HA. The binding epitope of F3-2 is mainly located in the region of H7N9 HA(299-307). The binding epitope of 1C6B is located in the region of H7N9 HA(489-506). F3-2 and 1C6B could not effectively inhibit the hemagglutination activity of H7N9 HA. However, F3-2 can prevent H7N9 HA from trypsin cleavage and can bind to H7N9 HA which has undergone pH-induced conformational change. F3-2 also has the ability of binding to H7N9 viral particles and inhibiting H7N9 virus infection to MDCK cells with the IC50 value of 22.18 µg/mL. In addition, F3-2 and 1C6B were utilized for comprising a lateral flow immunochromatographic test strip for specific detection of H7N9 HA. KEY POINTS: • Two mouse monoclonal antibodies, F3-2 and 1C6B, were generated for recognizing the novel binding epitopes in H7N9 HA. • F3-2 can prevent H7N9 HA from trypsin cleavage and inhibit H7N9 virus infection to MDCK cells. • F3-2 and 1C6B were developed as a lateral flow immunochromatographic test for specific detection of H7N9 HA.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.