The widespread availability of personal mobile devices, combined with the increasing availability of stationary public devices such as large interactive displays, creates new opportunities for computer-supported collaborative work. In particular, these two factors enable the emergence of collaborative scenarios, whether planned or spontaneous, in any location, and previous obstacles to such collaborative settings such as limitations on the number of devices available for use and infrastructure costs can be overcome more easily. As hardware restrictions diminish, the need for software toolkits that simplify the development of distributed collaborative applications allowing for device heterogeneity, true multi-user interaction and spontaneous emergence increases. In this article, we describe the Toolkit for Web-based Interactive Collaborative Environments whose aim is to address these issues. This is done using current standard web technologies extended for real-time application (and structured using specific development guidelines) while ensuring compatibility with the manifold new evolutions in the currently ongoing development of open web platform (HTML5, websockets, etc). While our own work has mainly focused on synchronous co-located collaborative systems (same place/same time), our solution, the technologies used, as well as the concepts that are introduced are easily extendable for remote and/or asynchronous collaboration.

Interaction with multiple mouse pointers is becoming widespread for collocated collaboration on shared displays, but most technologies used to implement it do not support telepresent users. While web technologies are a common standard for applications that enable telepresent collaboration, they do not support the multipointer interaction needed for collaboration in collocated settings. We propose an approach to provide multipointer interaction in web applications by adding multipointer support to the web browser and addressing pointer handling in a JavaScript framework.

MOTIVATION: Robust computer algorithms are required to interpret the vast amounts of proteomic data currently being produced and to generate generalized models which are applicable to 'real world' scenarios. One such scenario is the classification of bacterial species. These vary immensely, some remaining remarkably stable whereas others are extremely labile showing rapid mutation and change. Such variation makes clinical diagnosis difficult and pathogens may be easily misidentified. RESULTS: We applied artificial neural networks (Neuroshell 2) in parallel with cluster analysis and principal components analysis to surface enhanced laser desorption/ionization (SELDI)-TOF mass spectrometry data with the aim of accurately identifying the bacterium Neisseria meningitidis from species within this genus and other closely related taxa. A subset of ions were identified that allowed for the consistent identification of species, classifying >97% of a separate validation subset of samples into their respective groups. AVAILABILITY: Neuroshell 2 is commercially available from Ward Systems.