MOTIVATION: BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP's first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. RESULTS: Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. AVAILABILITY AND IMPLEMENTATION: BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d.

MOTIVATION: Processing of Optical Coherence Tomography (OCT) biofilm images is currently restricted to a set of custom-made MATLAB scripts. None of the tools currently available for biofilm image processing (including those developed for Confocal Laser Scanning Microscopy-CLSM) enable a fully automatic processing of 2D OCT images. RESULTS: A novel software tool entitled Biofilm Imaging and Structure Classification Automatic Processor (BISCAP) is presented. It was developed specifically for the automatic processing of 2D OCT biofilm images. The proposed approach makes use of some of the key principles used in CLSM image processing, and introduces a novel thresholding algorithm and substratum detection strategy. Two complementary pixel continuity checks are executed, enabling very detailed pixel characterizations. BISCAP delivers common structural biofilm parameters and a set of processed images for biofilm analysis. A novel biofilm 'compaction parameter' is suggested. The proposed strategy was tested on a set of 300 images with highly satisfactory results obtained. BISCAP is a Python-based standalone application, not requiring any programming knowledge or property licenses, and where all operations are managed via an intuitive Graphical User Interface. The automatic nature of this image processing strategy decreases biasing problems associated to human-perception and allows a reliable comparison of outputs. AVAILABILITY AND IMPLEMENTATION: BISCAP and a collection of biofilm images obtained from OCT scans can be found at: https://github.com/diogonarciso/BISCAP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The identification, classification and recording of events leading to deterioration of wine quality is essential for developing appropriate strategies avoid them. This work introduces an adverse event reporting and learning system that can help preventing hazards and ensure the quality wines. The Eindhoven Classification Method (ECM) has been extended and adapted to the incidents of the wine industry. Logic Programming was used for Knowledge Representation and Reasoning (KRR) in order to model the universe of discourse, even in the presence of incomplete data, information or knowledge. On the other hand, the evolutionary process of the body of knowledge is to be understood as a process of devaluation, enabling the automatic extraction of knowledge and the generation of reports to identify the most relevant causes of errors lead to a poor wine quality. In addition, the answers to the problem are object of formal evidence through theorem proving.

The number of mobile devices accessing the Internet is continuously increasing, creating new problems. For example, when many wireless clients try to access the same video stream in the same hot spot, multicast transmission must be used. However, multicast packets are unacknowledged and may present a high loss rate under adverse conditions which may jeopardize the quality of the streaming. In a severe scenario, packet losses may affect all video frames, preventing the clients from receiving the video stream at all. Therefore, we propose a novel reliable multicast protocol, tailored to streaming applications, that also scales to many clients under the same access point. It uses a feedback repeat request mechanism per client and such requests are adequately scheduled by the streaming server. This mechanism, which is the focus of this paper, also allows differentiating the quality of service provided to the clients, polling their feedback independently. We show experimental and simulation results that confirm the desired scalability and reliable streaming, with efficient bandwidth usage. The results also show a clear superiority in mitigating lost packets with respect to other approaches to the same problem, with benefits reaching and even going beyond one order of magnitude in packets recovery.