Avian metapneumovirus causes acute respiratory tract infection and reductions in egg production in various avian species. We isolated and characterized an increasingly prevalent avian metapneumovirus subgroup C strain from meat-type commercial chickens with severe respiratory signs in China. Culling of infected flocks could lead to economic consequences.

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Avian metapneumovirus (aMPV) emerged as an important respiratory pathogen causing acute respiratory tract infection in avian species. Here we used a chicken aMPV subgroup C (aMPV/C) isolate to inoculate experimentally BALB/c mice and found that the aMPV/C can efficiently replicate and persist in the lungs of mice for at least 21 days with a peak viral load at day 6 postinoculation. Lung pathological changes were characterized by increased inflammatory cells. Immunochemical assay showed the presence of viral antigens in the lungs and significant upregulation of pulmonary inflammatory cytokines and chemokines including MCP-1, MIP-1α, RANTES, IL-1β, IFN-γ, and TNF-α were detected following inoculation. These results indicate for the first time that chicken aMPV/C may replicate in the lung of mice. Whether aMPV/C has potential as zoonotic pathogen, further investigation will be required.

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Design and fabrication of novel electrode materials with excellent specific capacitance and cycle stability are urgent for advanced energy storage devices, and the combinability of multiple modification methods is still insufficient. Herein, Ni 2+ , Zn 2+ double-cation-substitution Co carbonate hydroxide (NiZnCo-CH) nanosheets arrays were established on 3D copper with controllable morphology (3DCu@NiZnCo-CH). The selfstanding scalable dendritic copper offers a large surface area and promotes fast electron transport. The 3DCu@NiZnCo-CH electrode shows a markedly improved electrochemical performance with a high specific capacity of ∼1008 C g −1 at 1 A g −1 (3.2, 2.83, and 1.26 times larger than Co-CH, ZnCo-CH, and NiCo-CH, respectively) and outstanding rate capability (828.8 C g −1 at 20 A g −1 ) due to its compositional and structural advantages. Density functional theory (DFT) calculation results illustrate that cation doping adjusts the adsorption process and optimizes the charge transfer kinetics. Moreover, an aqueous hybrid supercapacitor based on 3DCu@NiZnCo-CH and rGO demonstrates a high energy density of 42.29 Wh kg −1 at a power density of 376.37 W kg −1 , along with superior cycling performance (retained 86.7% of the initial specific capacitance after 10,000 cycles). Impressively, these optimized 3DCu@NiZnCo-CH//rGO devices with ionic liquid can be operated stably in a large potential range of 4 V with greatly enhanced energy density and power capability (110.12 Wh kg −1 at a power density of 71.69 W kg −1 ). These findings may shed some light on the rational design of transition-metal compounds with tunable architectures by multiple modification methods for efficient energy storage.

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Due to the flexibility and mobility, unmanned aerial vehicle (UAV) can work as a movable sink to receive the data collected by sensors in wireless sensor networks (WSNs). This paper analyzes the capacity of UAV assisted data collection in WSNs, which provides a guideline for the parameters optimization of data collection in the presence of UAVs. In this paper, the service area of UAVs covers the area where sensors are distributed. The charging points for UAVs are placed around the service area, which provides energy supply for UAVs. The charging point is the starting and ending point of a UAV's trajectory. The service area is partitioned into multiple service cells. UAVs traverse these service cells to receive the data collected by the sensors in the service cells. The per-node capacity and average execution time of UAVs are used as two metrics to measure the performance of data collection in WSN. The upper and lower bounds of per-node capacity are derived respectively. It is discovered that the number of UAVs, the number of service cells and the trajectories of UAVs affect the per-node capacity of WSN. The pernode capacity can be optimized by adjusting the numbers of UAVs and service cells. Two path planning algorithms of UAVs are designed. With path planning, the per-node capacity is optimized to be closer to the upper bound, which achieves highly efficient data collection. The simulation results verify the correctness of the derived results.INDEX TERMS Wireless sensor networks, unmanned aerial vehicle, data collection, capacity analysis, path planning, trajectory optimization.

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