MOTIVATION: MicroRNAs form an important class of RNA regulators that has been studied extensively. The miRBase and Rfam database provide rich, frequently updated information on both pre-miRNAs and their mature forms. These data sources, however, rely on individual data submission and thus are neither complete nor consistent in their coverage across different miRNA families. Quantitative studies of miRNA evolution therefore are difficult or impossible on this basis. RESULTS: We present here a workflow and a corresponding implementation, MIRfix, that automatically curates miRNA datasets by improving alignments of their precursors, the consistency of the annotation of mature miR and miR* sequence, and the phylogenetic coverage. MIRfix produces alignments that are comparable across families and sets the stage for improved homology search as well as quantitative analyses. AVAILABILITY AND IMPLEMENTATION: MIRfix can be downloaded from https://github.com/Bierinformatik/MIRfix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Views and abstraction levels are two major concepts introduced in the software engineering domain in order to enhance the architectural organization of complex systems' requirements. Despite numerous and substantial works that built and refined those concepts, they still less secure and less standardized to be part of a rough software architecture. This paper begins with a survey of evolution of the role and usage of those concepts in software architecture, and ends with an overview of an approach integrating the views and abstraction levels concepts within a single rough multi-views/multi-abstraction levels software architecture and respecting their usage in an evolutionary architectural specification analysis. The main benefits of this contribution was to allow system architects to solve more efficiently the complexity problems; and allow them to build a complex, organized and coherent architecture; and finally enhance the communication and harmony among different stakeholders.

Providing a fair and secure medium access scheme is a fundamental requirement in centralized wireless networks. However, IEEE 802.11 systems do not provide any fairness control mechanism and assume a cooperative behavior of participating nodes. We tackle the problem of misbehavior on the MAC layer, mainly the backoff manipulation attack, and we propose the Random Backoff Control (RBC) mechanism. This mechanism provides misbehavior detection by controlling the backoff selection process to ensure a fair distribution of network resources. Simulation results based on OMNeT++ show that RBC ensures a reasonable fairness in presence of selfish nodes and is indeed efficient in mitigating misbehavior on the MAC layer.