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cerana. Copyright © 2020 Wang, Zhu, Yan, Yan, Zheng and Zeng.Previous observational studies have shown that the serum uric acid (UA) level is decreased in persons with multiple sclerosis (MS). We used the two-sample Mendelian randomization (MR) method to determine whether the serum UA level is causally associated with the risk of MS. We screened 26 single-nucleotide polymorphisms (SNPs) in association with serum UA level (p less then 5 × 10-8) from a large genome-wide meta-analysis involving 110,347 individuals. The SNP outcome effects were obtained from two large international genetic studies of MS involving 38,589 individuals and 27,148 individuals. A total of 18 SNPs, including nine proxy SNPs, were included in the MR analysis. The estimate based on SNP rs12498742 that explained the largest proportion of variance showed that the odds ratio (OR) of UA (per mg/dl increase) for MS was 1.00 [95% confidence interval (CI) 0.90-1.11; p = 0.96]. The main MR analysis based on the random effects inverse variance weighted method showed that the pooled OR was 1.05 (95% CI 0.92-1.19; p = 0.50). Although there was no evidence of net horizontal pleiotropy in MR-Egger regression (p = 0.48), excessive heterogeneity was found via Cochran's Q statistic (p = 9.6 × 10-4). The heterogeneity showed a substantial decrease after exclusion of two outlier SNPs (p = 0.17). The pooled ORs for the other MR methods ranged from 0.89 (95% CI 0.65-1.20; p = 0.45) to 1.05 (95% CI 0.96-1.14; p = 0.29). The results of sensitivity analyses and additional analyses all showed similar pooled estimates. MR analyses by using 81 MS -associated SNPs as instrumental variables showed that genetically predicted risk of MS was not significantly associated with serum UA level. The pooled OR was 1.00 (95% CI 0.99-1.02; p = 0.74) for the main MR analysis. This MR study does not support a causal effect of genetically determined serum UA level on the risk of MS, nor does it support a causal effect of genetically determined risk of MS on serum UA level. Copyright © 2020 Niu, Song, Wang and Xu.Three-dimensional culture systems that allow generation of monolayered epithelial cell spheroids are widely used to study epithelial function in vitro. Epithelial spheroid formation is applied to address cellular consequences of (mono)-genetic disorders, that is, ciliopathies, in toxicity testing, or to develop treatment options aimed to restore proper epithelial cell characteristics and function. With the potential of a high-throughput method, the main obstacle to efficient application of the spheroid formation assay so far is the laborious, time-consuming, and bias-prone analysis of spheroid images by individuals. Hundredths of multidimensional fluorescence images are blinded, rated by three persons, and subsequently, differences in ratings are compared and discussed. Here, we apply supervised learning and compare strategies based on machine learning versus deep learning. While deep learning approaches can directly process raw image data, machine learning requires transformed data of features extracted from fluorescence images. We verify the accuracy of both strategies on a validation data set, analyse an experimental data set, and observe that different strategies can be very accurate. Deep learning, however, is less sensitive to overfitting and experimental batch-to-batch variations, thus providing a rather powerful and easily adjustable classification tool. Copyright © 2020 Soetje, Fuellekrug, Haffner and Ziegler.Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. Screening Library research buy A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy. Copyright © 2020 Feng, Hao, Shi, Xia, Huang, Yu and Zhou.Long-term domestication and selective breeding have increased the silk yield of the domestic silkworm (Bombyx mori) by several times the amount of the silk yield of its wild ancestor (Bombyx mandarina). However, little is known about the molecular mechanisms behind the increase in silk yield during domestication. Based on dynamic patterns of functional divergence in the silk gland between domestic and wild silkworms, we found that at early and intermediate stages of silk gland development, the up-regulated genes of the domestic silkworm were mainly involved in DNA integration, nucleic acid binding, and transporter activity, which are related to the division and growth of cells. This has led to the posterior silk gland (PSG) of the domestic silkworm having significantly more cells ("factories" of fibroin protein synthesis) than that of the wild silkworm. At the late stage of silk gland development, the up-regulated genes in the domestic silkworm was enriched in protein processing and ribosome pathways, suggestht © 2020 Zhou, Fu, Li, Zhang, Yu, Qiu, Zhang and Zhang.Birth weight of pigs is an important economic factor in the livestock industry. The identification of the genes and variants that underlie birth weight is of great importance. In this study, we integrated two genotyping methods, single nucleotide polymorphism (SNP) chip analysis and restriction site associated DNA sequencing (RAD-seq) to genotype genome-wide SNPs. In total, 45,175 and 139,634 SNPs were detected with the SNP chip and RAD-seq, respectively. The genome-wide association study (GWAS) of the combined SNP panels identified two significant loci located at chr1 97,745,041 and chr4 112,031,589, that explained 6.36% and 4.25% of the phenotypic variance respectively. To reduce interval containing causal variants, we imputed sequence-level SNPs in the GWAS identified regions and fine-mapped the causative variants into two narrower genomic intervals a ∼100 kb interval containing 71 SNPs and a broader ∼870 kb interval with 432 SNPs. This fine-mapping highlighted four promising candidate genes, SKOR2, SMAD2, VAV3, and NTNG1.