Service Oriented Architecture (SOA) is an approach for building distributed systems that deliver application functionality as a set of self-contained business-aligned services with well-defined and discoverable interfaces. This paper presents a systematic and architecture-centric framework, named Service Oriented Architecture Framework (SOAF), to ease the definition, the design and the realization of SOA in order to achieve a better business and IT alignment. The proposed framework is businessprocess centric and comprises a set of structured activities grouped in five phases. It incorporates a range of techniques and guidelines for systematically identifying services, deciding service granularity and modeling services while integrating existing operational/legacy systems. The results from a pilot validation of SOAF for SOA enablement of a realistic Securities Trading application are presented. Best practices and lessons learned are also discussed.

Protein engineering is often applied to tailor substrate specificity, enantioselectivity, or stability of enzymes according to the needs of a process. In rational engineering approaches, molecular docking and molecular dynamics simulations are often used to compare transition states of wild-type and enzyme variants. Besides affecting the transition state energies by mutations, the entry of the substrate and its positioning in the active site (Michaelis complex) is also often studied, and mutagenesis of residues forming the substrate entry tunnel can have a profound impact on activity and selectivity. In this study, we combine the strengths of such a tunnel approach with MD followed by semiempirical QM calculations that allow the identification of beneficial positions and an in silico screening of possible variants. We exemplify this strategy in the expansion of the substrate scope of Chromobacterium violaceum amine transaminase toward sterically demanding substrates. Two double mutants (F88L/C418­(G/L)) proposed by the modeling showed >200-fold improved activities in the conversion of 1-phenylbutylamine and enabled the asymmetric synthesis of this amine from the corresponding ketone, which was not possible with the wild-type. The correlation of interaction energies and geometrical parameters (distance of the substrate’s carbonyl carbon to the cofactor’s amino group) as obtained in the simulations suggests that this strategy can be used for in silico prediction of variants facilitating an efficient entry and placement of a desired substrate as a first requirement for catalysis. However, when choosing amino acid positions for substitution and modeling, additional knowledge of the enzymatic reaction mechanism is required, as residues that are involved in the catalytic machinery or that guarantee the structural integrity of the enzyme will not be recognized by the developed algorithm and should be excluded manually.

Abstract

Recently, compressive sensing (CS) has emerged as a powerful tool for solving a class of inverse/underdetermined problems in computer vision and image processing. In this paper, we investigate the application of CS paradigms on single image super-resolution (SR) problems that are considered to be the most challenging in this class. In light of recent promising results, we propose novel tools for analyzing sparse representation-based inverse problems using redundant dictionary basis. Further, we provide novel results establishing tighter correspondence between SR and CS. As such, we gain insights into questions concerning regularizing the solution to the underdetermined problem, such as follows. 1) Is sparsity prior alone sufficient? 2) What is a good dictionary? 3) What is the practical implication of noncompliance with theoretical CS hypothesis? Unlike in other underdetermined problems that assume random downprojections, the low-resolution image formation model employed in CS-based SR is a deterministic down-projection that may not necessarily satisfy some critical assumptions of CS. We further investigate the impact of such projections in concern to the above questions.

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