Background: Crimean-Congo hemorrhagic fever virus (CCHFV) is a member of the nairovirus, a genus in the Bunyaviridae family, which causes a life threatening disease in human. Currently, there is no vaccine against CCHFV and detailed structural analysis of CCHFV proteins remains undefined. The CCHFV M RNA segment encodes two viral surface glycoproteins known as Gn and Gc. Viral glycoproteins can be considered as key targets for vaccine development.Objectives: The current study aimed to investigate structural bioinformatics of CCHFV Gn protein and design a construct to make a recombinant bacmid to express by baculovirus system.Materials and Methods: To express the Gn protein in insect cells that can be used as antigen in animal model vaccine studies. Bioinformatic analysis of CCHFV Gn protein was performed and designed a construct and cloned into pFastBacHTb vector and a recombinant Gn-bacmid was generated by Bac to Bac system.Results: Primary, secondary, and 3D structure of CCHFV Gn were obtained and PCR reaction with M13 forward and reverse primers confirmed the generation of recombinant bacmid DNA harboring Gn coding region under polyhedron promoter.Conclusions: Characterization of the detailed structure of CCHFV Gn by bioinformatics software provides the basis for development of new experiments and construction of a recombinant bacmid harboring CCHFV Gn, which is valuable for designing a recombinant vaccine against deadly pathogens like CCHFV.

‎A perfect STAR packing in a given graph $G$ can be defined as a spanning subgraph of $G$‎, ‎wherein each component is isomorphic to the STAR graph $K_{1,3}$‎. ‎A perfect STAR packing of a fullerene graph $G$ is of type $P0$ if all the centers of STARs lie on hexagons of $G$‎. ‎Many fullerene graphs arise from smaller fullerene graphs by applying some transformations‎. ‎In this paper‎, ‎we introduce two transformations for fullerene graphs that have the perfect STAR packing of type $P0$ and examine some characteristics of the graphs obtained from this transformation‎.

Non-Dimensional STAR pattern recognition uses planar angles as its recognition feature. This feature is independent of image focal length and optical axis offset. However, this independency doesn’t mean that the algorithm conserves its robustness in presence of any type of errors. These errors arise from poor hardware calibration and software inaccuracy that causes the angles to differ from their true amounts stored in the database. In order to evaluate the effect of angle differences on algorithm performance, overall disposition of bright point centers is modeled. The monte-carlo simulation method is then used to evaluate the algorithm’s performance for different amounts of error.Results demonstrate that 0.1 pixel size error is admissible in conserve the trade-off between desired update frequency, hardware accuracy and algorithm’s robustness.

Suppose G is a finite non-abelian group and Γ(G) is a graph with non-central conjugacy classes of G as its vertex set. Two vertices L and K in Γ(G) are adjacent if there are a ∈ L and b ∈ K such that ab = ba. This graph is called the commuting conjugacy class graph of G. The purpose of this paper is to compute the commuting conjugacy class graph of the finite 2−groups Gn(m) and G[n]