Construction of a bivalent vaccine candidate against HAdV4/HAdV7 based on capsid-display strategy via Red-homologous recombination and counter-selection methodology

Human adenoviruses(HAdvs)are major respiratory pathogens.Specifically,human adenovirus type 4(HAdV4)and human adenovirus type 7(HAdV7)are known for causing fever and pneumonia,with docu-mented cases of fatalities among the population.In recent years,HAdV4/HAdv7 has been implicated in caus-ing substantial outbreaks,leading to increased morbidity in multiple countries.Most HAdV4 and HAdV7 infections have been reported in North America,Asia,Europe,Africa,South America,Oceania,and the Middle East.Most fatalities occurred in North America(the United States)and Asia(China and Singapore).Engineered recombinant adenoviruses have played a crucial role as vaccine vectors.In this study,we con-structed a recombinant adenovirus,Ad4ITRmut-Ad7E3,and evaluated it in vitro and in vivo.We observed that the replication rate of Ad4ITRmut-Ad7E3 was lower than that of the RI-67 strain,indicating that the mutation of inverted terminal repeats(ITRs)weakened the replication ability of HAdV4.Immunization of BALB/c mice with the bivalent Ad4ITRmut-Ad7E3 vaccine strain,administered by intraperitoneal injection and oral gavage,resulted in the elicitation of neutralizing antibodies targeting HAdV4 and HAdv7.This finding not only pro-vides a novel method and technique for the efficient construction of a polyvalent recombinant adenovirus vac-cine candidate against HAdV4 and HAdv7 but also against other prevalent adenovirus serotypes such as HAdV3,HAdV11,HAdV14,and HAdv55,from various regions.

Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model

To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with surrogatemodel (MOFA-SM) is proposed in this paper. Firstly, the population wasinitialized according to the chaotic mapping. Secondly, the external archive wasconstructed based on the preference sorting, with the lightweight clustering pruningstrategy. In the process of evolution, the elite solutions selected from archivewere used to guide the movement to search optimal solutions. Simulation resultsshow that the proposed algorithm can achieve better performance in terms ofconvergence iteration and stability.