An insight into imbalanced Big Data classification: outcomes and challenges

Big Data applications are emerging during the last years, and researchers from many disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-and-conquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware. Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit the MapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.This work has been partially supported by the Spanish Ministry of Science and Technology under Projects TIN2014-57251-P and TIN2015-68454-R, the Andalusian Research Plan P11-TIC-7765, the Foundation BBVA Project 75/2016 BigDaPTOOLS, and the National Science Foundation (NSF) Grant IIS-1447795

RyR1 deficiency in congenital myopathies disrupts excitation-contraction coupling.

n skeletal muscle, excitation-contraction (EC) coupling is the process whereby the voltage-gated dihydropyridine receptor (DHPR) located on the transverse tubules activates calcium release from the sarcoplasmic reticulum by activating ryanodine receptor (RyR1) Ca(2+) channels located on the terminal cisternae. This subcellular membrane specialization is necessary for proper intracellular signaling and any alterations in its architecture may lead to neuromuscular disorders. In this study, we present evidence that patients with recessive RYR1-related congenital myopathies due to primary RyR1 deficiency also exhibit downregulation of the alfa 1 subunit of the DHPR and show disruption of the spatial organization of the EC coupling machinery. We created a cellular RyR1 knockdown model using immortalized human myoblasts transfected with RyR1 siRNA and confirm that knocking down RyR1 concomitantly downregulates not only the DHPR but also the expression of other proteins involved in EC coupling. Unexpectedly, this was paralleled by the upregulation of inositol-1,4,5-triphosphate receptors; functionally however, upregulation of the latter Ca(2+) channels did not compensate for the lack of RyR1-mediated Ca(2+) release. These results indicate that in some patients, RyR1 deficiency concomitantly alters the expression pattern of several proteins involved in calcium homeostasis and that this may influence the manifestation of these diseases