The rapid growth of logistics distribution highlights the problems including the imperfect infrastructure of logistics distribution network, the serious shortage of distribution capacity of each individual enterprise, and the high cost of distribution in China. While the development of sharing economy makes it possible to achieve the integration of whole social logistic resources, big data technology can grasp customer’s logistics demand accurately on the basis of analyzing the customer’s logistics distribution preference, which contributes to the integration and optimization of the whole logistics resources. This paper proposes a kind of intensive distribution logistics network considering sharing economy, which assumes that all the social logistics suppliers build a strategic alliance, and individual idle logistics resources are also used to deal with distribution needs. Analyzing customer shopping behavior by the big data technology to determine customer’s logistics preference on the basis of dividing the customer’s logistics preference into high speed, low cost, and low pollution and then constructing the corresponding objective function model according to different logistics preferences, we obtain the intensive distribution logistics network model and solve it with heuristic algorithm. Furthermore, this paper analyzes the mechanism of interest distribution of the participants in the distribution network and puts forward an improved interval Shapley value method considering both satisfaction and contribution, with case verifying the feasibility and effectiveness of the model. The results showed that, compared with the traditional Shapley method, distribution coefficient calculated by the improved model could be fairer, improve stakeholder satisfaction, and promote the sustainable development of the alliance as well.
BackgroundToxoplasma gondii is an apicomplexan parasite that affects the health of humans and livestock, and an effective vaccine is urgently required. Nanoparticles can modulate and improve cellular and humoral immune responses.MethodsIn the current study, poly (D, L-lactic-co-glycolic acid) (PLGA) nanoparticles were used as a delivery system for the T. gondii dense granule antigens GRA12 and GRA7. BALB/c mice were injected with the vaccines and protective efficacy was evaluated.ResultsMice immunized with PLGA+GRA12 exhibited significantly higher IgG, and a noticeable predominance of IgG2a over IgG1 was also observed. There was a 1.5-fold higher level of lymphocyte proliferation in PLGA+GRA12-injected mice compared to Alum+GRA12-immunized mice. Higher levels of IFN-g and IL-10 and a lower level of IL-4 were detected, indicating that Th1 and Th2 immune responses were induced but the predominant response was Th1. There were no significant differences between Alum+GRA7-immunized and PLGA+GRA7-immunized groups. Immunization with these four vaccines resulted in significantly reduced parasite loads, but they were lowest in PLGA+GRA12-immunized mice. The survival times of mice immunized with PLGA+GRA12 were also significantly longer than those of mice in the other vaccinated groups.ConclusionThe current study indicated that T. gondii GRA12 recombinant protein encapsulated in PLGA nanoparticles is a promising vaccine against acute toxoplasmosis, but PLGA is almost useless for enhancing the immune response induced by T. gondii GRA7 recombinant protein.
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