Preface: Semantic Web technologies for mobile and pervasive environments

Artificial Intelligence provides a rich set of methods and tools for implementing the Ambient Intelligence vision, i.e. to transform our environments into smart spaces assisting as with our everyday tasks in an intelligent, seamless and non-obtrusive way. Among them, Semantic Web technologies, such as RDF, ontology languages and others, can be used to address several of the challenges that come with this vision, mainly with respect to modelling, sharing and reasoning with context information. This thematic issue demonstrates their capabilities by presenting three different Semantic Web-based solutions for mobile and computing environments

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Pseudomembranous Trigonitis: A Common but Underrecognized Urological Entity

Pseudomembranous trigonitis is the term used to describe squamous metaplastic changes of the bladder trigone, which affect nearly 40% of adult females. We present the characteristics of this underrecognized clinical entity and encourage further relevant research

Health-related quality of life aspects of the ‘Periodontitis prevalence in ulcerative colitis and Crohn's disease’ (PPCC) cohort

Aim: To assess whether oral health problems affect disease-specific quality of life (QoL) of inflammatory bowel disease (IBD) patients, and vice versa, whether IBD affects oral-health-related QoL. Materials and Methods: Individuals reporting IBD and matched controls were surveyed on general anamnestic information, oral-health-related questions and the Oral Health Impact Profile (OHIP)-5. IBD patients were additionally surveyed on years since diagnosis, disease activity and severity as well as health-related QoL (Short Inflammatory Bowel Disease Questionnaire, sIBDQ). OHIP-5 and sIBDQ were defined as primary outcome parameters, and several predictors and confounders were used in adjusted univariable and multivariable regression analyses. Results: Answers from 1108 IBD patients and 3429 controls were analysed. Compared with controls, IBD patients reported significantly more frequently an oral impact on daily life and worse oral-health-related QoL, with Crohn's disease (CD) patients being more severely affected than ulcerative colitis (UC) patients. The diagnosis of UC and CD, having <20 teeth, severe periodontitis and stressful daily-life experience were associated with a higher prevalence of poor oral-health-related QoL. Among IBD patients, an impaired IBD-specific, health-related QoL was significantly associated with the diagnosis of CD and depression, IBD activity and severity, having <20 teeth, presence of oral lesions and stressful daily-life experience, while a longer time since diagnosis was significantly associated with an improved IBD-specific, health-related QoL. Conclusions: The results of the present study indicate, for the first time, that oral health problems are associated with an impairment of IBD-specific health-related QoL, and vice versa, IBD is associated with an impaired oral health-related QoL. This emphasizes the potential advantages of including dental professionals in the multi-disciplinary treatment teams of IBD patients

Advanced data fusion: Random forest proximities and pseudo-sample principle towards increased prediction accuracy and variable interpretation

Data fusion has gained much attention in the field of life sciences, and this is because analysis of biological samples may require the use of data coming from multiple complementary sources to express the samples fully. Data fusion lies in the idea that different data platforms detect different biological entities. Therefore, if these different biological compounds are then combined, they can provide comprehensive profiling and understanding of the research question in hand. Data fusion can be performed in three different traditional ways: low-level, mid-level, and high-level data fusion. However, the increasing complexity and amount of generated data require the development of more sophisticated fusion approaches. In that regard, the current study presents an advanced data fusion approach (i.e. proximities stacking) based on random forest proximities coupled with the pseudo-sample principle. Four different data platforms of 130 samples each (faecal microbiome, blood, blood headspace, and exhaled breath samples of patients who have Crohn's disease) were used to demonstrate the classification performance of this new approach. More specifically, 104 samples were used to train and validate the models, whereas the remaining 26 samples were used to validate the models externally. Mid-level, high-level, as well as individual platform classification predictions, were made and compared against the proximities stacking approach. The performance of each approach was assessed by calculating the sensitivity and specificity of each model for the external test set, and visualized by performing principal component analysis on the proximity matrices of the training samples to then, subsequently, project the test samples onto that space. The implementation of pseudo-samples allowed for the identification of the most important variables per platform, finding relations among variables of the different data platforms, and the ex-amination of how variables behave in the samples. The proximities stacking approach outperforms both mid-level and high-level fusion approaches, as well as all individual platform predictions. Concurrently, it tackles significant bottlenecks of the traditional ways of fusion and of another advanced fusion way discussed in the paper, and finally, it contradicts the general belief that the more data, the merrier the result, and therefore, considerations have to be taken into account before any data fusion analysis is conducted. (c) 2021 Published by Elsevier B.V

The Overlap Between Binge Eating Behaviors and Polycystic Ovarian Syndrome: An Etiological Integrative Model

Studies indicate that Polycystic Ovarian Syndrome (PCOS) features (e.g. insulin instability, food cravings,overproduction of androgens and menstrual irregularities) are associated with increased appetite, impairedimpulse control and feelings of body dissatisfaction. Counter intuitively, binge eating behaviors have been shownto reinforce PCOS symptomatology, precipitating concurrently body dissatisfaction, weight gain, insulin instabilityand overproduction of androgens. The present systematic literature review aspires to investigate the relationshipbetween binge eating, in the broader context of eating disorder behaviors, and Polycystic Ovarian Syndrome(PCOS), taking into account shared characteristics between EDs (Eating Disorders) and PCOS. To address thisaim, the PRISMA guidelines are adopted. A total of 21 studies, which investigated the presence of binge eating inPCOS population and the presence of PCOS in EDs population, were synthesized. Findings suggested that anincreased prevalence of binge eating has been reported in women with Polycystic Ovarian Syndrome (PCOS);and that women suffering from BN (Bulimia Nervosa) and BED (Binge Eating Disorder) are more likely to displaypolycystic ovaries. Further research on their shared liability is required in order to inform more efficientprevention and treatment initiatives for populations presenting with comorbid features