In recent years, microRNAs (miRNAs) and tRNA-derived RNA fragments (tRFs) have been reported extensively following different approaches of identification and analysis. Comprehensively analyzing the present approaches to overcome the existing variations, we developed a benchmarking methodology each for the identification of miRNAs and tRFs, termed as miRNA Prediction Methodology (miRPreM) and tRNA-induced small non-coding RNA Prediction Methodology (tiRPreM), respectively. We emphasized the use of respective genome of organism under study for mapping reads, sample data with at least two biological replicates, normalized read count support and novel miRNA prediction by two standard tools with multiple runs. The performance of these methodologies was evaluated by using Oryza coarctata, a wild rice species as a case study for model and non-model organisms. With organism-specific reference genome approach, 98 miRNAs and 60 tRFs were exclusively found. We observed high accuracy (13 out of 15) when tested these genome-specific miRNAs in support of analyzing the data with respective organism. Such a strong impact of miRPreM, we have predicted more than double number of miRNAs (186) as compared with the traditional approaches (79) and with tiRPreM, we have predicted all known classes of tRFs within the same small RNA data. Moreover, the methodologies presented here are in standard form in order to extend its applicability to different organisms rather than restricting to plants. Hence, miRPreM and tiRPreM can fulfill the need of a comprehensive methodology for miRNA prediction and tRF identification, respectively, for model and non-model organisms.

We explore the idea of making aesthetic decorative patterns that contain multiple visual codes. We chart an iterative collaboration with ceramic designers and a restaurant to refine a recognition technology to work reliably on ceramics, produce a pattern book of designs, and prototype sets of tableware and a mobile app to enhance a dining experience. We document how the designers learned to work with and creatively exploit the technology, enriching their patterns with embellishments and backgrounds and developing strategies for embedding codes into complex designs. We discuss the potential and challenges of interacting with such patterns. We argue for a transition from designing 'codes to patterns' that reflects the skills of designers alongside the development of new technologies.

SUMMARY: Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI's use of standard data formats. AVAILABILITY AND IMPLEMENTATION: All SBSI binaries and source-code are freely available from http://sourceforge.net/projects/sbsi under an Apache 2 open-source license. The server-side SBSINumerics runs on any Unix-based operating system; both SBSIVisual and SBSIDispatcher are written in Java and are platform independent, allowing use on Windows, Linux and Mac OS X. The SBSI project website at http://www.sbsi.ed.ac.uk provides documentation and tutorials.