Multiple SNP Markers Reveal Fine-Scale Population and Deep Phylogeographic Structure in European Anchovy (Engraulis encrasicolus L.)

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Deep learning review and its applications

Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed.

LDLR and PCSK9 Are Associated with the Presence of Antiphospholipid Antibodies and the Development of Thrombosis in aPLA Carriers

Introduction The identification of the genetic risk factors that could discriminate non-thrombotic from thrombotic antiphospholipid antibodies (aPLA) carriers will improve prognosis of these patients. Several human studies have shown the presence of aPLAs associated with atherosclerotic plaque, which is a known risk factor for thrombosis. Hence, in order to determine the implication of atherosclerosis in the risk of developing thrombosis in aPLA positive patients, we performed a genetic association study with 3 candidate genes, APOH, LDLR and PCSK9. Material & Methods For genetic association study we analyzed 190 aPLA carriers -100 with non-thrombotic events and 90 with thrombotic events-and 557 healthy controls. Analyses were performed by chi(2) test and were corrected by false discovery rate. To evaluate the functional implication of the newly established susceptibility loci, we performed expression analyses in 86 aPLA carrier individuals (43 with thrombotic manifestations and 43 without it) and in 45 healthy controls. Results Our results revealed significant associations after correction in SNPs located in LDLR gene with aPLA carriers and thrombotic aPLA carriers, when compared with healthy controls. The most significant association in LDLR gene was found between SNP rs129083082 and aPLA carriers in recessive model (adjusted P-value = 2.55 x 10(-3); OR = 2.18; 95% CI = 1.49-3.21). Furthermore, our work detected significant allelic association after correction between thrombotic aPLA carriers and healthy controls in SNP rs562556 located in PCSK9 gene (adjusted P-value = 1.03 x 10(-2); OR = 1.60; 95% CI = 1.24-2.06). Expression level study showed significantly decreased expression level of LDLR gene in aPLA carriers (P-value < 0.0001; 95% CI 0.16-2.10; SE 0.38-1.27) in comparison to the control group. Discussion Our work has identified LDLR gene as a new susceptibility gene associated with the development of thrombosis in aPLA carriers, describing for the first time the deregulation of LDLR expression in individuals with aPLAs. Besides, thrombotic aPLA carriers also showed significant association with PCSK9 gene, a regulator of LDLR plasma levels. These results highlight the importance of atherosclerotic processes in the development of thrombosis in patients with aPLA.