Avian influenza (AI) is a serious infectious disease caused by avian influenza virus (AIV) belonging to type A Orthomyxovirus. In the present study, we developed an indirect enzyme-linked immunosorbent assay (ELISA) employing E. coli-expressed full-length nucleoprotein (NP) of H9N2 avian influenza virus for the detection and quantification of antibodies against AIV nucleoprotein. The NP-ELISA was compared with the AI agar gel propagation (AGP) test, haemagglutination inhibition (HI) test, and IDEXX-FlockChek ELISA using 263 sera. The NP-ELISA was significantly more sensitive than the AGP and HI tests, and showed 96.2% agreement ratio with IDEXX-FlockChek ELISA. With results obtained using the NP-ELISA, an ELISA titre (ET) prediction equation, with which the ELISA titres of a flock or individual chickens can be determined, was derived from a positive/negative (P/N) ratio standard curve. The NP-ELISA enables an alternative rapid serological diagnosis and is suitable for influenza A antibody screening, especially in species that harbour several influenza subtypes.
An immunochromatographic strip was developed for the detection of the H9 subtype of avian influenza viruses (H9AIVs) in poultry, using two monoclonal antibodies (MAb), 4C4 for H9AIV hemagglutinin (HA) and 4D4 for nucleoprotein. The 4C4 MAb was labeled with colloidal gold as the detection reagent, and the 4D4 MAb was blotted on the test line while a goat anti-mouse antibody was used on the control line of the nitrocellulose membrane. In comparison with the HA and HA inhibition (HI) tests, the strip was specific for the detection of H9AIV, with a sensitivity at 0.25 HA units within 10 min. Storage of the strips at room temperature for 6 months or at 4°C for 12 months did not change their sensitivity and specificity. Evaluation of the strip with experimental tracheal and cloacal swab samples collected from H9N2-infected chickens revealed that the strip detected the H9N2 viruses on day 3 postinoculation, earlier than the appearance of clinical symptoms. Application of the strip for the analysis of 157 tracheal or cloacal samples from potentially infected chickens on five poultry farms showed that four farms had chickens that were infected with H9AIV. Further characterization of 10 positive and 30 negative randomly selected samples showed that no single sample was false positive or negative, as determined by the standard virus isolation and HI assays. Therefore, the immunochromatographic strip for the detection of H9AIVs has high specificity, sensitivity, and stability. This finding, together with the advantages of rapid detection and easy operation and without the requirement for special skills and equipment, makes the strip suitable for onsite detection and the differentiation of H9AIVs from other viruses in poultry.
The emergence of connected autonomous vehicles (CAVs) is not only improving the efficiency of transportation, but also providing new opportunities for the sustainable development of transportation. Taking advantage of the energy consumption of CAVs to promote the sustainable development of transportation has attracted extensive public attention in recent years. This paper develops a mathematical approach to investigating the problem of the optimal implementation of dedicated CAV lanes while simultaneously considering economic and environmental sustainability. Specifically, the problem is described as a multi-objective bi-level programming model, in which the upper level is to minimize the system-level costs including travel time costs, CAV lane construction cost, and emission cost, whereas the lower level characterizes the multi-class network equilibrium with a heterogeneous traffic stream consisting of both human-driven vehicle (HVs) and CAVs. To address the multi-objective dedicated CAV lane implement problem, we propose an integrated solution framework that integrates a non-dominated sorting genetic algorithm II (NSGA-II) algorithm, diagonalized algorithm, and Frank–Wolfe algorithm. The NSGA-II was adopted to solve the upper-level model, i.e., hunting for the optimal CAV lanes implementation schemes. The diagonalized Frank–Wolfe (DFW) algorithm is used to cope with multi-class network equilibrium. Finally, numerical experiments were conducted to demonstrate the effectiveness of the proposed model and solution method. The experimental results show that the total travel time cost, total emission cost, and total energy consumption were decreased by about 12.03%, 10.42%, and 9.4%, respectively, in the Nguyen–Dupuis network as a result of implementing the dedicated CAV lanes.
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