MotivationAccurate detection, genotyping and downstream analysis of genomic variants from high-throughput sequencing data are fundamental features in modern production pipelines for genetic-based diagnosis in medicine or genomic selection in plant and animal breeding. Our research group maintains the Next-Generation Sequencing Experience Platform (NGSEP) as a precise, efficient and easy-to-use software solution for these features.ResultsUnderstanding that incorrect alignments around short tandem repeats are an important source of genotyping errors, we implemented in NGSEP new algorithms for realignment and haplotype clustering of reads spanning indels and short tandem repeats. We performed extensive benchmark experiments comparing NGSEP to state-of-the-art software using real data from three sequencing protocols and four species with different distributions of repetitive elements. NGSEP consistently shows comparative accuracy and better efficiency compared to the existing solutions. We expect that this work will contribute to the continuous improvement of quality in variant calling needed for modern applications in medicine and agriculture.Availability and implementationNGSEP is available as open source software at http://ngsep.sf.net.Supplementary information Supplementary data are available at Bioinformatics online.

Mobile, pervasive computing environments respond to users' requirements by providing access to and composition of various services over networked devices. In such an environment, service composition needs to satisfy a request's goal, and be mobile-aware even throughout service discovery and service execution. A composite service also needs to be adaptable to cope with the environment's dynamic network topology. Existing composition solutions employ goal-oriented planning to provide flexible composition, and assign service providers at runtime, to avoid composition failure. However, these solutions have limited support for complex service flows and composite service adaptation. This paper proposes a self-organizing, goal-driven service model for task resolution and execution in mobile pervasive environments. In particular, it proposes a decentralized heuristic planning algorithm based on backward-chaining to support flexible service discovery. Further, we introduce an adaptation architecture that allows execution paths to dynamically adapt, which reduces failures, and lessens re-execution effort for failure recovery. Simulation results show the suitability of the proposed mechanism in pervasive computing environments where providers are mobile, and it is uncertain what services are available. Our evaluation additionally reveals the model's limits with regard to network dynamism and resource constraints.

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The water distribution network (WDN) sectorisation problem is characterised by structural and hydraulic requirements that make existing graph partitioning techniques inadequate to find a good solution. Specifically, sector isolation and direct access to at least one source for each sector are not addressed. This study proposes a method to address structural requirements of water network sectorisation with minimum negative impact on the hydraulic requirements. This paper first elaborates the sectorisation problem and discusses the requirements of water network sectorisation. Then, it proposes a novel method, called WDN-PARTITION, which applies a new heuristic structural graph partitioning algorithm, combined with a many-objective optimisation procedure, to find near-optimal arrangements of nodes into sectors. The criteria of optimisation and their priorities can be specified for each case. The outcome of the method is a set of non-dominated sectorisation solutions, ranked lexicographically based on their values for the chosen criteria and their priorities, from which the final decision can be made by the domain experts. WDN-PARTITION has been implemented and integrated with a hydraulic network simulator. The simulation-based evaluation results demonstrate that WDN-PARTITION generally achieves its design objectives to partition a water network into isolated sectors with a minimal negative impact on the hydraulic performance criteria of the network.

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Background The Psoriasis Area and Severity Index (PASI) score is commonly used in clinical practice and research to monitor disease severity and determine treatment efficacy. Automating the PASI score with deep learning algorithms, like Convolutional Neural Networks (CNNs), could enable objective and efficient PASI scoring. Objectives To assess the performance of image-based automated PASI scoring in anatomical regions by CNNs and compare the performance of CNNs to image-based scoring by physicians.Methods Imaging series were matched to PASI subscores determined in real life by the treating physician. CNNs were trained using standardized imaging series of 576 trunk, 614 arm and 541 leg regions. CNNs were separately trained for each PASI subscore (erythema, desquamation, induration and area) in each anatomical region (trunk, arms and legs). The head region was excluded for anonymity. Additionally, PASI-trained physicians retrospectively determined imagebased subscores on the test set images of the trunk. Agreement with the real-life scores was determined with the intraclass correlation coefficient (ICC) and compared between the CNNs and physicians.Results Intraclass correlation coefficients between the CNN and real-life scores of the trunk region were 0.616, 0.580, 0.580 and 0.793 for erythema, desquamation, induration and area, respectively, with similar results for the arms and legs region. PASI-trained physicians (N = 5) were in moderate-good agreement (ICCs 0.706-0.793) with each other for image-based PASI scoring of the trunk region. ICCs between the CNN and real-life scores were slightly higher for erythema (0.616 vs. 0.558), induration (0.580 vs. 0.573) and area scoring (0.793 vs. 0.694) than image-based scoring by physicians. Physicians slightly outperformed the CNN on desquamation scoring (0.580 vs. 0.589).Conclusions Convolutional Neural Networks have the potential to automatically and objectively perform image-based PASI scoring at an anatomical region level. For erythema, desquamation and induration scoring, CNNs performed similar to physicians, while for area scoring CNNs outperformed physicians on image-based PASI scoring.

Software-as-a-Service (SaaS) applications are multi-tenant software applications that are delivered as highly configurable web services to individual customers, which are called tenants in this context. For reasons of complexity management and to lower maintenance cost, SaaS providers maintain and deploy a single version of the application code for all tenants. As a result, however, custom-made extensions for individual tenants cannot be e ciently integrated and managed. In this paper we show that by using a context-oriented programming model, cross-tier tenant-specific software variations can be easily integrated into the single-version application code base. Moreover, the selection of which variations to execute can be configured on a per tenant basis. Concretely, we provide a technical case study based on Google App Engine (GAE), a cloud platform for building multitenant web applications. We contribute by showing: (a) how ContextJ, a context-oriented programming (COP) language, can be used with GAE, (b) the increase in flexibility and customizability of tenant-specific software variations using ContextJ as compared to Google's dependency injection framework Guice, and (c) that the performance of using ContextJ is comparable to Guice. Based on these observations, we come to the conclusion that COP can be helpful for providing software variations in SaaS.

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