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Introduction: Risk factors for developing pancreatitis due to thiopurines in patients with inflammatory bowel disease (IBD) are not clearly identified. Our aim was to evaluate the predictive pharmacogenetic risk of pancreatitis in IBD patients treated with thiopurines. Methods: We conducted an observational pharmacogenetic study of acute pancreatitis events in a cohort study of IBD patients treated with thiopurines from the prospectively maintained ENEIDA registry biobank of GETECCU. Samples were obtained and the CASR, CEL, CFTR, CDLN2, CTRC, SPINK1, CPA1, and PRSS1 genes, selected based on their known association with pancreatitis, were fully sequenced. Results: Ninety-five cases and 105 controls were enrolled; a total of 57% were women. Median age at pancreatitis diagnosis was 39 years. We identified 81 benign variants (50 in cases and 67 in controls) and a total of 35 distinct rare pathogenic and unknown significance variants (10 in CEL, 21 in CFTR, 1 in CDLN2, and 3 in CPA1). None of the cases or controls carried pancreatitis-predisposing variants within the CASR, CPA1, PRSS1, and SPINK1 genes, nor a pathogenic CFTR mutation. Four different variants of unknown significance were detected in the CDLN and CPA1 genes; one of them was in the CDLN gene in a single patient with pancreatitis and 3 in the CPA1 gene in 5 controls. After the analysis of the variants detected, no significant differences were observed between cases and controls. Conclusion: In patients with IBD, genes known to cause pancreatitis seem not to be involved in thiopurine-related pancreatitis onset. [ABSTRACT FROM AUTHOR]

The identification of movement strategies in situations that are as ecologically valid as possible is essential for the understanding of lower limb interactions. This study considered the kinetic and kinematic data for the hip, knee and ankle joints from 376 block jump-landings when moving in the dominant and non-dominant directions from fourteen senior national female volleyball players. Two Machine Learning methods were used to generate the models from the dataset, Random Forest and Artificial Neural Networks. In addition, decision trees were used to detect which variables were relevant to discern the limb movement strategies and to provide a meaningful prediction. The results showed statistically significant differences when comparing the movement strategies between limb role (accuracy > 88.0% and > 89.3%, respectively), and when moving in the different directions but performing the same role (accuracy > 92.3% and > 91.2%, respectively). This highlights the importance of considering limb dominance, limb role and direction of movement during block jump-landings in the identification of which biomechanical variables are the most influential in the movement strategies. Moreover, Machine Learning allows the exploration of how the joints of both limbs interact during sporting tasks, which could provide a greater understanding and identification of risky movements and preventative strategies. All these detailed and valuable descriptions could provide relevant information about how to improve the performance of the players and how to plan trainings in order to avoid an overload that could lead to risk of injury. This highlights that, there is a necessity to consider the learning models, in which the spike approach unilaterally is taught before the block approach (bilaterally). Therefore, we support the idea of teaching bilateral approach before learning the spike, in order to improve coordination and to avoid asymmetries between limbs. [ABSTRACT FROM AUTHOR]

Congenital dyserythropoietic anemias (CDA) are disorders characterized by ineffective erythropoiesis and morphological anomalies in erythrocytes and erythroblasts. The purpose of this study is to identify the gene variants in patients diagnosed with CDA. We analyzed five unrelated patients and two siblings with a targeted panel of genes to CDA: CDAN1, CDIN1, SEC23B, KIF23, KLF1, and GATA1 genes. We found three novel variants in the CDIN1 gene (p.Leu136Val, p.Tyr247Cys, and p.Ile273Thr), four known variants in the SEC23B gene (p.Arg14Trp, p.Arg554Ter, p.Asp239Gly, and p.Ser436Leu), and one novel variant in the KIF23 gene (p.Leu945Trpfs*31). The in silico analysis of novel variants predict that they are pathogenic and, the in vitro study confirms the functional impact of the KIF23 variant on the protein location. [ABSTRACT FROM AUTHOR]

Objectives: To assess the effectiveness and safety of combining granulocyte‐monocyte apheresis (GMA) and vedolizumab (VDZ) in patients with refractory ulcerative colitis (UC). Methods: This retrospective, multicentre pilot study included all UC patients receiving both GMA and VDZ. We recorded data on GMA sessions, demographic characteristics, and clinical response. Effectiveness was assessed 1 and 6 months after finishing the GMA using the partial Mayo score, C‐reactive protein, and fecal calprotectin levels. Data were also compiled on VDZ intensification, use of new immunomodulators and colectomy during follow‐up. Results: Eight patients were included (mean age 46 years; 63% female; mean disease duration, 132 months; 50% E3). GMA was started after a loss of response to VDZ in all cases (25% primary nonresponse and 75% secondary loss of response). All had previously received anti‐TNF agents. VDZ was prescribed as the second‐, third‐, or fourth‐line biologic in 37%, 50%, and 13% of cases, respectively. Patients had a mean baseline partial Mayo score of 7.5 (SD 2.1) and received a median of 15 GMA sessions (range 5‐38). After a median follow‐up of 7.5 months (IQR 5‐12), partial Mayo score decreased after 1 and 6 months (P =.01 and.06, respectively). Three patients (38%) achieved steroid‐free clinical remission and five (63%) withdrew VDZ. Colectomy rate was 38%. No adverse events were observed during the combination therapy. Conclusions: This small case series suggests that combining GMA with VDZ could be a treatment option in selected cases of UC with an inadequate response to this biologic agent. [ABSTRACT FROM AUTHOR]

An increasing amount of unlabeled time series data available render the semi-supervised paradigm a suitable approach to tackle classification problems with a reduced quantity of labeled data. Self-labeled techniques stand out from semi-supervised classification methods due to their simplicity and the lack of strong assumptions about the distribution of the labeled and unlabeled data. This paper addresses the relevance of these techniques in the time series classification context by means of an empirical study that compares successful self-labeled methods in conjunction with various learning schemes and dissimilarity measures. Our experiments involve 35 time series datasets with different ratios of labeled data, aiming to measure the transductive and inductive classification capabilities of the self-labeled methods studied. The results show that the nearest-neighbor rule is a robust choice for the base classifier. In addition, the amending and multi-classifier self-labeled-based approaches reveal a promising attempt to perform semi-supervised classification in the time series context. [ABSTRACT FROM AUTHOR]

The term 'Big Data' has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme and Hadoop, its open-source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms. WIREs Data Mining Knowl Discov 2014, 4:380-409. doi: 10.1002/widm.1134 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

In time series analysis remaining autocorrelation in the errors of a model implies that it is failing to properly capture the structure of time-dependence of the series under study. This can be used as a diagnostic checking tool and as an indicator of the adequacy of the model. Through the study of the errors of the model in the Lagrange Multiplier testing framework, in this paper we derive (and validate using simulated and real world examples) a hypothesis test which allows us to determine if there is some left autocorrelation in the error series. This represents a new diagnostic checking tool for fuzzy rule-based modelling of time series and is an important step towards statistically sound modelling strategy for fuzzy rule-based models. [ABSTRACT FROM AUTHOR]

The article discusses various reports published within the issue including one on comparison of data driven models for the validation of residentials premises, one on a bibliometric study about the research based on hybridating the fuzzy logic field, and one on a context-sensitive approach to fuzzy modeling.