Abstract This publication presents a solution approach to oncological aftercare for cancer patients by means of artificial intelligence (AI) methods. This approach shall support patients in overcoming the after-effects of therapy effectively with suitable supportive actions and health-care professionals in goal-oriented planning of these actions. Different AI methods are used for analyzing patients’ needs for supportive actions depending on the available health data and for a monitoring of these actions. Decision support methods are used for effective planning of actions based on the AI results of analysis. The solution approach is realized in the form of a web application for health-care professionals, which allows for data analysis and planning of actions, and a mobile application for patients, which facilitates documentation and monitoring of supportive actions. In combination, they facilitate a closed-loop workflow for the effective cooperation of health-care professionals and cancer patients. The solution approach is illustrated for an exemplary case scenario of colorectal cancer.

MOTIVATION: The vast amount of already available and currently generated read mapping data requires comprehensive visualization, and should benefit from bioinformatics tools offering a wide spectrum of analysis functionality from just one source. Appropriate handling of multiple mapped reads during mapping analyses remains an issue that demands improvement. RESULTS: The capabilities of the read mapping analysis and visualization tool ReadXplorer were vastly enhanced. Here, we present an even finer granulated read mapping classification, improving the level of detail for analyses and visualizations. The spectrum of automatic analysis functions has been broadened to include genome rearrangement detection as well as correlation analysis between two mapping data sets. Existing functions were refined and enhanced, namely the computation of differentially expressed genes, the read count and normalization analysis and the transcription start site detection. Additionally, ReadXplorer 2 features a highly improved support for large eukaryotic data sets and a command line version, enabling its integration into workflows. Finally, the new version is now able to display any kind of tabular results from other bioinformatics tools. AVAILABILITY AND IMPLEMENTATION: http://www.readxplorer.org CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

This paper proposes a framework for the integration of handwriting recognition into natural user interfaces. As more and more pen-enabled touch displays are available, we make use of the distinction between touch actions and pen actions. Furthermore, we apply a recently introduced mode detection approach to distinguish between handwritten strokes and graphics drawn with the pen. These ideas are implemented in the Touch & Write SDK which can be used for various applications. In order to evaluate the effectiveness of our approach, we have conducted experiments for an annotation scenario. We asked several users to mark and label several objects in videos. We have measured the labeling time when using our novel user interaction system and compared it to the time needed when using common labeling tools. Furthermore, we compare our handwritten input paradigm to other existing systems. It turns out that the annotation is performed much faster when using our method and the user experience is also much better.