Abstract
Entomopathogenic nematodes can be considered effective biocontrol agents of pest insects in aquatic habitat. Larvae of Culex quinquefsciatus Say were exposed to infective juveniles of Heterorhabditis bacteriophora, H. indica, Steinernema carpocapsae, and S. feltiae under laboratory conditions. The bioassay studies revealed the suppressive role of H. bacteriophora and H. indica nematode in controlling the mosquito, C. quinquefasciatus. They successfully established themselves in the host cadaver and produced infective juveniles. On the other hand, both S. carpocapsae and S. feltiae failed to establish in the host larvae or attain significant host mortality values. This is the first report of parasitism of entomopathogenic nematodes isolates from Egypt against larvae of C. quinquefasciatus, with promising results. Therefore, further studies must be carried out to determine if these nematodes would be effective as autochthonous agents for the control of Culex sp. and other mosquitoes of sanitary interest.

Major Histocompatibility complex (MHC) molecules play an essential role in introducing and regulation immune system. The MHC molecules are divided into two classes, class I and class II which are differ in size of their binding pockets. Determining which peptides bind to a specific MHC molecule is fundamental to understanding the basis of immunity, and for the development of vaccines and immunotherapeutic for autoimmune diseases and cancer. Due to the variability of the locations of the class II binding cores, the process for predicting the affinity of these peptides is difficult.This paper investigates a new method for predicting peptides binding to MHC class II molecules and its affinity using genetic algorithms and metaheuristics. The algorithm is based on a fitness function that builds a scoring matrix for all suggested motifs in a specific iteration to test the motif ability to be one of the real motifs in the nature. The genetic algorithmpresented here shows increased prediction accuracy with higher number of true positives and negatives on almost of MHC class II alleles,about 80 percent of peptides were correctly classified when testing dataset from IEDB[26]. Generally, these results indicate that GA has a strong ability for MHC Class II binding prediction.