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SummaryScientific legacy workflows are often developed over many years, poorly documented and implemented with scripting languages. In the context of our cross-disciplinary projects we face the problem of maintaining such scientific workflows. This paper presents the Workflow Instrumentation for Structure Extraction (WISE) method used to process several ad-hoc legacy workflows written in Python and automatically produce their workflow structural skeleton. Unlike many existing methods, WISE does not assume input workflows to be preprocessed in a known workflow formalism. It is also able to identify and analyze calls to external tools. We present the method and report its results on several scientific workflows.

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During the last years, the number of hardware implementations based on Field Programmable Gate Arrays (FPGAs) is increasing because it satisfies the high speed of system and hardware cost constraints. FPGAs implementation allows the building of rapid prototypes reducing development times and board area. However, since FPGAs has been improved to satisfy speed and size constraints, it is not evident that these devices could satisfy low-power consumption constraint. Compared to ASICs, FPGAs are generally perceived as non low-power consumption devices, whose only advantage is programmability and more recently dynamic reconfigurability. In this work we present an study of dynamic and static power consumption of FPGAs that allows the designer to acquire a better understanding of how power consumption is generated and distributed inside the FPGAs. Based on these results, a genetic algorithm will be used to minimize critical long paths and optimize the internal resources during the Place & Route process in order to optimize power consumption while keeping a high performance. Categories: Circuit Design

The effects of residue substitution in protein can be dramatic and predicting its impact may benefit scientists greatly. Like in many scientific domains there are various methods and tools available to address the potential impact of a mutation on the structure of a protein. The identification of these methods, their availability, the time needed to gain enough familiarity with them and their interface, and the difficulty of integrating their results in a global view where all view points can be visualized often limit their use. In this paper, we present the Structural Prediction for pRotein fOlding UTility System (SPROUTS) workflow and describe our method for designing, documenting, and maintaining the workflow. The focus of the workflow is the thermodynamic contribution to stability, which can be considered as acceptable for small proteins. It compiles the predictions from various sources calculating the ΔΔG upon point mutation, together with a consensus from eight distinct algorithms, with a prediction of the mean number of interacting residues during the process of folding, and a sub domain structural analysis into fragments that may potentially be considered as autonomous folding units, i.e., with similar conformations alone and in the protein body. The workflow is implemented and available online. We illustrate its use with the analysis of the engrailed homeodomain (PDB code 1enh).