Understanding obsessive-compulsive personality disorder in adolescence: a dimensional personality perspective

The validity of the Axis II Obsessive-Compulsive Personality Disorder (OCPD) category and its position within the Cluster C personality disorder (PDs) section of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, APA, 2000) continues to be a source of much debate. The present study examines the associations between general and maladaptive personality traits and OCPD symptoms, prior to and after controlling for co-occurring PD variance, in a general population sample of 274 Flemish adolescents and further explores the incremental validity of two different maladaptive trait measures beyond general traits. The results demonstrate that the number of (general and maladaptive) personality-OCPD associations decreases after controlling for a general personality pathology factor, with the FFM factor Conscientiousness and its maladaptive counterpart Compulsivity as remaining correlates of OCPD. The findings further suggest to complement the general NEO-PI-R (Costa & McCrae, 1992) scales with more maladaptive items to enable a more comprehensive description of personality pathology variance. Implications for understanding and assessing OCPD in the developmental context of adolescence are discussed

Fully automatic binary glioma grading based on pre-therapy MRI using 3D Convolutional Neural Networks

The optimal treatment strategy of newly diagnosed glioma is strongly influenced by tumour malignancy. Manual non-invasive grading based on MRI is not always accurate and biopsies to verify diagnosis negatively impact overall survival. In this paper, we propose a fully automatic 3D computer-aided diagnosis (CAD) system to non-invasively differentiate high-grade glioblastoma from lower-grade glioma. The approach consists of an automatic segmentation step to extract the tumour ROI followed by classification using a 3D convolutional neural network. Segmentation was performed using a 3D U-Net achieving a dice score of 88.53% which matches top performing algorithms in the BraTS 2018 challenge. The classification network was trained and evaluated on a large heterogeneous dataset of 549 patients reaching an accuracy of 91%. Additionally, the CAD system was evaluated on data from the Ghent University Hospital and achieved an accuracy of 92% which shows that the algorithm is robust to data from different centres

Not all job demands are equal: differentiating job hindrances and job challenges in the Job Demands-Resources model

This study aimed to integrate the differentiation between two types of job demands, as made in previous studies, in the Job-Demands Resources (JD-R) model. Specifically, this study aimed to examine empirically whether the differentiation between job hindrances and job challenges, next to the category of job resources, accounts for the unexpected positive relationships between particular types of job demands (e.g., workload) and employees' work engagement. Results of confirmatory factor analyses supported the differentiation between the three categories of job characteristics in two samples (N1=261 and N2=441). Further, structural equation modelling confirmed the hypotheses that job hindrances associate positively with exhaustion (i.e., the main component of burnout) and negatively with vigour (i.e., the main component of work engagement). Job resources displayed the reversed pattern of relations. Job challenges were positively related to vigour. Rather unexpectedly, they were unrelated to exhaustion. Based on these findings, we discuss the importance of the differentiation between different types of job demands in the JD-R model for both theory and practice

Measuring the effect of node aggregation on community detection

Many times the nodes of a complex network, whether deliberately or not, are aggregated for technical, ethical, legal limitations or privacy reasons. A common example is the geographic position: one may uncover communities in a network of places, or of individuals identified with their typical geographical position, and then aggregate these places into larger entities, such as municipalities, thus obtaining another network. The communities found in the networks obtained at various levels of aggregation may exhibit various degrees of similarity, from full alignment to perfect independence. This is akin to the problem of ecological and atomic fallacies in statistics, or to the Modified Areal Unit Problem in geography. We identify the class of community detection algorithms most suitable to cope with node aggregation, and develop an index for aggregability, capturing to which extent the aggregation preserves the community structure. We illustrate its relevance on real-world examples (mobile phone and Twitter reply-to networks). Our main message is that any node-partitioning analysis performed on aggregated networks should be interpreted with caution, as the outcome may be strongly influenced by the level of the aggregation.Comment: 12 pages, 5 figure

Vormen van Nederlands leren door volwassenen in Vlaanderen : een explorerend onderzoek naar aanbod en samenwerking

nrpages: 73status: Published onlin

Synthesis and penicillin‐binding protein inhibitory assessment of dipeptidic 4‐phenyl‐β‐lactams from α‐amino acid‐derived imines

Monocyclic beta-lactams revive the research field on antibiotics, which are threatened by the emergence of resistant bacteria. A six-step synthetic route was developed, providing easy access to new 3-amino-1-carboxymethyl-4-phenyl-beta-lactams, of which the penicillin-binding protein (PBP) inhibitory potency was demonstrated biochemically

Applying cascade-correlation neural networks to in-fill gaps in Mediterranean daily flow data series

The analyses of water resources availability and impacts are based on the study over time of meteorological and hydrological data trends. In order to perform those analyses properly, long records of continuous and reliable data are needed, but they are seldom available. Lack of records as in gaps or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management. Flow data series from gauging stations are not an exception. Over the last 20 years, forecasting models based on artificial neural networks (ANNs) have been increasingly applied in many fields of natural resources, including hydrology. This paper discusses results obtained on the application of cascade-correlation ANN models to predict daily water flow using Julian day and rainfall data provided by nearby weather stations in the Ebro river watershed (Northeast Spain). Five unaltered gauging stations showing a rainfall-dominated hydrological regime were selected for the study. Daily flow and weather data series covered 30 years to encompass the high variability of Mediterranean environments. Models were then applied to the in-filling of existing gaps under different conditions related to the characteristics of the gaps (6 scenarios). Results showed that when short periods before and after the gap are considered, this is a useful approach, although no general rule applied to all stations and gaps investigated. Models for low-water-flow periods provided better results (r = 0.76–0.8)