Big Data technology has discarded traditional data modelling approaches as no longer applicable to distributed data processing. It is, however, largely recognised that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g. data analytics and reporting. In this paper, the third of its kind co-authored by mem-

Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a “top-down” method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders’ complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software “omics”-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.

BackgroundEndovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic stroke (AIS) secondary to large vessel occlusion. The elderly population has been under-represented in clinical trials on ET, and recent studies have reported higher morbidity and mortality in elderly patients than in their younger counterparts.ObjectiveTo use machine learning algorithms to develop a clinical decision support tool that can be used to select elderly patients for ET.MethodsWe used a retrospectively identified cohort of 110 patients undergoing ET for AIS at our institution to train a regression tree model that can predict 90-day modified Rankin Scale (mRS) scores. The identified algorithm, termed SPOT, was compared with other decision trees and regression models, and then validated using a prospective cohort of 36 patients.ResultsWhen predicting rates of functional independence at 90 days, SPOT showed a sensitivity of 89.36% and a specificity of 89.66% with an area under the receiver operating characteristic curve of 0.952. Performance of SPOT was significantly better than results obtained using National Institutes of Health Stroke Scale score, Alberta Stroke Program Early CT score, or patients’ baseline deficits. The negative predictive value for SPOT was >95%, and in patients who were SPOT-negative, we observed higher rates of symptomatic intracerebral hemorrhage after thrombectomy. With mRS scores prediction, the mean absolute error for SPOT was 0.82.ConclusionsSPOT is designed to aid clinical decision of whether to undergo ET in elderly patients. Our data show that SPOT is a useful tool to determine which patients to exclude from ET, and has been implemented in an online calculator for public use.

No abstract

No abstract

Words in Arabic consist of letters and short vowel symbols called diacritics inscribed atop regular letters. Changing diacritics may change the syntax and semantics of a word; turning it into another. This results in difficulties when comparing words based solely on string matching. Typically, Arabic NLP applications resort to morphological analysis to battle ambiguity originating from this and other challenges. In this article, we introduce three alternative algorithms to compare two words with possibly different diacritics. We propose the Subsume knowledge-based algorithm, the Imply rule-based algorithm, and the Alike machine-learning-based algorithm. We evaluated the soundness, completeness, and accuracy of the algorithms against a large dataset of 86,886 word pairs. Our evaluation shows that the accuracy of Subsume (100%), Imply (99.32%), and Alike (99.53%). Although accurate, Subsume was able to judge only 75% of the data. Both Subsume and Imply are sound, while Alike is not. We demonstrate the utility of the algorithms using a real-life use case -- in lemma disambiguation and in linking hundreds of Arabic dictionaries.

Typically, oracles used to test graphical user interface (GUI) programs highly depend on environmental factors that are not related to the functionality of the program, such as screen resolution and color schemes. To accommodate these non-functional variations, researchers suggested fuzzy comparison rules that determine whether the output of a GUI program matches the oracles. Others suggested computer vision based solutions that make use of computationally expensive image processing techniques to abstract the strict comparisons. Alternatively, we propose GUICOP, a system that checks whether a trace of a GUI execution violates a given GUI specification. GUICOP is composed of a GUI specification language, instrumented GUI libraries, and a checker. The alphabet of the specification language contains basic geometric shapes describing GUI components, events, and positional and temporal operators that express relative object positions and event timings, respectively. During program execution, the instrumented libraries capture positional and temporal information of components and associated triggered events in execution traces. The checker determines whether the traces satisfy the specifications. To evaluate GUICOP, we wrote 50 use cases that describe real GUI applications and used the GUICOP checker on the supported cases that successfully revealed violations.

Abstract. Reduction and abstraction techniques have been proposed to address the state space explosion problem in verification. In this paper, we present reduction and abstraction techniques for component-based systems modeled in BIP (Behavior, Interaction and Priority). Given a BIP system consisting of several atomic components, we select two atomic components amenable for reduction and compute their product. The resulting product component typically contains constants and branching bisimilar states. We use constant propagation to reduce the resulting component. Then we use a branching bisimulation abstraction to compute an abstraction of the product component. The presented method is fully implemented. Our results show a drastic improvement in verifying BIP systems.