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The full-length cDNA sequence of prophenoloxidase was obtained through RACE technology. The complete cDNA sequence is 3 721-bp long, containing an open reading frame (ORF) of 1 881 bp, a 154-bp 5′-untranslated region, and a 1 686- bp 3′-untranslated region with three potential functional poly(A) signals (AATAAA). The molecular mass of the deduced amino acid sequence (627 aa) was 72.3 kDa with an estimated p I of 5.88. It contained putative copper-binding sites (copper A: 131, 135, 167 and copper B: 301, 305, 341), and a tentative complement-like motif (GCGWPDHL). Eight potential N-linked glycosylation sites were predicted to be present in P . clarkii prophenoloxidase. Similar to those in other arthropod prophenoloxidases reported so far, no signal peptide was detected in the crayfish prophenoloxidase. The phylogenetic trees confirmed that P . clarkii prophenoloxidase was most closely related to that of freshwater crayfish P . leniusculus and more closely related to other crustacean prophenoloxidases from shrimp, prawn, and lobster than to the insect prophenoloxidases. Besides, two putative introns were found in this sequence of genomic DNA.

The goal of this paper is to discuss a new evolutionary strategy for the multiple objective design optimization of internal aerodynamic shape operating with transonic flow. The distributed optimization strategy discussed here and inspired from Lions’ new distributed control approach (J.L. Lions, Distributed active control approach for pde systems, Fourth WCCM CD-ROM, Buenos Aires, Argentina, 1998) relies on genetic algorithms (GAs). GAs are different from traditional optimization tools and based on digital imitation of biological evolution. Game theory replaces here a global optimization problem by a non-cooperative game based on Nash equilibrium with several players solving local constrained sub-optimization tasks. The transonic flow simulator uses a full potential solver taking advantage of domain decomposition methods and GAs for the matching of non-linear local solutions. The main idea developed here is to combine domain decomposition methods for the flow solver with the geometrical optimization procedure using local shape parameterization. Numerical results are presented for a simple nozzle inverse problem with subsonic and transonic shocked flows. A comparison of the nozzle reconstruction using domain decomposition method (DDM) or not for the simulation of the flow is then presented through evolutionary computations and convergence of the two surface parts of the throat is discussed. The above results illustrate the robustness and primising inherent parallelism of GAs for mastering the complexity of 3D optimizations.