Next-generation sequencing technologies enable the rapid identification of viral infection of diseased organisms. However, despite a consistent decrease in sequencing costs, it is difficult to justify their use in large-scale surveys without a virus sequence enrichment technique. As the majority of plant viruses have an RNA genome, a common approach is to extract the double-stranded RNA (dsRNA) replicative form, to enrich the replicating virus genetic material over the host background. The traditional dsRNA extraction is time-consuming and labour-intensive. We present an alternative method to enrich dsRNA from plant extracts using anti-dsRNA monoclonal antibodies in a pull-down assay. The extracted dsRNA can be amplified by reverse transcriptase-polymerase chain reaction and sequenced by next-generation sequencing. In our study, we have selected three distinct plant hosts: M aori potato (Solanum tuberosum), rengarenga (Arthropodium cirratum) and broadleaved dock (Rumex obtusifolius) representing a cultivated crop, a New Zealand-native ornamental plant and a weed, respectively. Of the sequence data obtained, 31-74% of the reads were of viral origin, and we identified five viruses including Potato virus Y and Potato virus S in potato; Turnip mosaic virus in rengarenga (a new host record); and in the dock sample Cherry leaf roll virus and a novel virus belonging to the genus Macluravirus. We believe that this new assay represents a significant opportunity to upscale virus ecology studies from environmental, primary industry and/or medical samples.

In this paper we present Malai, a model-based user interface development environment. . It completes works on data manipulation techniques used to link source data to user interfaces. We show how Malai can improve modularity and usability of interactive systems by considering actions, interactions and instruments as reusable first-class objects. Malai has been successfully used for the development of several post-WIMP interactive systems. We introduce each Malai component using the same example: a vector graphics editor.

International audienceAmong model comprehension tools, model slicers are tools that extract a subset from a model, for a specific purpose. Model slicers are tools that let modelers rapidly gather relevant knowledge from large models. However, existing slicers are dedicated to one modeling language. This is an issue when we observe that new domain specific modeling languages (DSMLs), for which we want slicing abilities, are created almost on a daily basis. This paper proposes the Kompren language to model and generate model slicers for any DSL (e.g. software development and building architecture) and for different purposes (e.g. monitoring and model comprehension). Kompren's abilities for model slicers construction is based on case studies from various domains