Abstract:This study presents an allergenic protein prediction system that appears to be capable of producing high sensitivity and specificity. The proposed system is based on support vector machine (SVM) using evolutionary information in the form of an amino acid position specific scoring matrix (PSSM). The performance of this system is assessed by a 10-fold crossvalidation experiment using a dataset consisting of 693 allergens and 1041 non-allergens obtained from Swiss-Prot and Structural Database of Allergenic Proteins (SDAP). The PSSM method produced an accuracy of 90.1% in comparison to the methods based on SVM using amino acid, dipeptide composition, pseudo (5-tier) amino acid composition that achieved an accuracy of 86.3, 86.5 and 82.1% respectively. The results show that evolutionary information can be useful to build more effective and efficient allergen prediction systems.

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The spectral element method is successfully applied to the modeling of singleand two-layer slot coating flows. The free surface tracking and mesh deformation strategy are described first. The numerical model is then validated by comparing solutions for the flow structure and stability of single-layer slot coating flows with published experimental results. Finally, a two-layer slot coater is modeled where the fluid/fluid layer is treated as a distinct free surface, the position and shape of which is determined as part of the solution. The flow structure for this flow is then described.

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