Antigenic and immunogenic evaluation of Helicobacter pylori FlaA epitopes

Objective(s): Helicobacter pyloriare among most common human pathogens affecting at least half of the world’s population. Mobility is one of the important primary factors in bacterial colonization and invasion. The purpose of this research is cloning, expression, and purification of FlaA protein specific epitopes in order to evaluate their antigenicity and immunogenicity. Materials and Methods: The antigenic region of the flaA gene was bioinformatically predicted using Epitope mapping software’s and the predicted epitopes were expressed in a prokaryotic expression vector. The antigen was injected into the animal model (mice BALB/c) and some indicators including IgG1, IgG2a, IgA, IFN-γ, and IL 5 were measured. Results: The immunogenicity studies in animal models by measuring serum antibodies (IgG1, IgG2a, and IgA) and cytokines (IFN-γ and IL5) revealed that the rFlaA induces a proper immune response in animal models. Conclusion: The recombinant FlaA protein is antigenic and immunogenic. Therefore, it might be used in order to design of specific diagnostic kits and recombinant vaccines against H. pylori

On the Partial Decoding Delay of Sparse Network Coding

Sparse network coding (SNC) is a promising technique for reducing the complexity of random linear network coding (RLNC), by selecting a sparse coefficient matrix to code the packets. However, the performance of SNC for the average decoding delay (ADD) of the packets is still unknown. We study the performance of ADD and propose a Markov chain model to analyze this SNC metric. This model provides a lower bound for decoding delay of a generation as well as a lower bound for decoding delay of a portion of a generation. Results show that although RLNC provides a better decoding delay of an entire generation, SNC outperforms RLNC in terms of ADD per packet. Sparsity of the coefficient matrix is a key parameter for ADD per packet to transmit stream data. The proposed model enables us to select the appropriate degree of sparsity based on the required ADD. Numerical results validate that the proposed model would enable a precise evaluation of SNC technique behavior

On the Partial Decoding Delay of Sparse Network Coding

Sparse network coding (SNC) is a promising technique for reducing the complexity of random linear network coding (RLNC), by selecting a sparse coefficient matrix to code the packets. However, the performance of SNC for the average decoding delay (ADD) of the packets is still unknown. We study the performance of ADD and propose a Markov chain model to analyze this SNC metric. This model provides a lower bound for decoding delay of a generation as well as a lower bound for decoding delay of a portion of a generation. Results show that although RLNC provides a better decoding delay of an entire generation, SNC outperforms RLNC in terms of ADD per packet. Sparsity of the coefficient matrix is a key parameter for ADD per packet to transmit stream data. The proposed model enables us to select the appropriate degree of sparsity based on the required ADD. Numerical results validate that the proposed model would enable a precise evaluation of SNC technique behavior