Arildsenberry0272

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN. Copyright © 2019 Jinlong Hu et al.The Eastern Russell's viper, Daboia siamensis, is a WHO Category 1 medically important venomous snake. It has a wide but disjunct distribution in Southeast Asia. The specific antivenom, D. siamensis Monovalent Antivenom (DsMAV-Thailand) is produced in Thailand but not available in Indonesia, where a heterologous trivalent antivenom, Serum Anti Bisa Ular (SABU), is used instead. This study aimed to investigate the geographical venom variation of D. siamensis from Thailand (Ds-Thailand) and Indonesia (Ds-Indonesia), and the immunorecognition of the venom proteins by antivenoms. Methods The venom proteins were decomplexed with reverse-phase high-performance liquid chromatography and sodium dodecyl sulfate-polyacrylamide gel electrophoresis, followed by in-solution tryptic digestion, nano-liquid chromatography-tandem mass spectrometry and protein identification. The efficacies of DsMAV-Thailand and SABU in binding the various venom fractions were assessed using an enzyme-linked immunosorbent assay optimized for iserved antigenicity that allowed effective immunorecognition by DsMAV-Thailand but not by SABU, consistent with the neutralization efficacy of the antivenoms. A specific, appropriate antivenom is needed in Indonesia to treat Russell's viper envenomation.Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that often persists into adulthood. ADHD and related personality traits, such as impulsivity and callousness, are caused by genetic and environmental factors and their interplay. Epigenetic modifications of DNA, including methylation, are thought to mediate between such factors and behavior and may behave as biomarkers for disorders. Here, we set out to study DNA methylation in persistent ADHD and related traits. We performed epigenome-wide association studies (EWASs) on peripheral whole blood from participants in the NeuroIMAGE study (age range 12-23 years). We compared participants with persistent ADHD (n = 35) with healthy controls (n = 19) and with participants with remittent ADHD (n = 19). Additionally, we performed EWASs of impulsive and callous traits derived from the Conners Parent Rating Scale and the Callous-Unemotional Inventory, respectively, across all participants. For every EWAS, the linear regression model analyzeor these findings, which thus might reflect environmental influences. In conclusion, in this pilot study with a small sample size, we observed several DNA-methylation-disorder/trait associations of potential significance for ADHD and the related behavioral traits. Although we do not wish to draw conclusions before replication in larger, independent samples, cholesterol signaling and metabolism may be of relevance for the onset and/or persistence of ADHD. Copyright © 2020 Meijer, Klein, Hannon, van der Meer, Hartman, Oosterlaan, Heslenfeld, Hoekstra, Buitelaar, Mill and Franke.Background Recent study demonstrates the comprehensive effects of gut microbiota on complex diseases or traits. Androgen Receptor Antagonist However, limited effort has been conducted to explore the potential relationships between gut microbiota and BMD. Methods We performed a polygenetic risk scoring (PRS) analysis to systematically explore the relationships between gut microbiota and body BMD. Significant SNP sets associated with gut microbiota were derived from previous genome-wide association study (GWAS). In total, 2,294 to 5,065 individuals with BMD values of different sites and their genotype data were obtained from UK Biobank cohort. The gut microbiota PRS of each individual was computed from the SNP genotype data for each study subject of UK Biobank by PLINK software. Using computed PRS as the instrumental variables of gut microbiota, Pearson correlation analysis of individual PRS values and BMD values was finally conducted to test the potential association between gut microbiota and target trait. Results In total, 31 BMD traits were selected as outcome to assess their relationships with gut microbiota. After adjusted for age, sex, body mass index, and the first 5 principal components (PCs) as the covariates using linear regression model, pelvis BMD (P = 0.0437) showed suggestive association signal with gut microbiota after multiple testing correction. Conclusion Our study findings support the weak relevance of gut microbiota with the development of BMD. Copyright © 2020 Cheng, Qi, Ma, Zhang, Cheng, Liang, Liu, Li, Kafle, Wen and Zhang.For precision medicine, there is a need to identify genes that accurately distinguish the physiological state or response to a particular therapy, but this can be challenging. Many methods of analyzing differential expression have been established and applied to this problem, such as t-test, edgeR, and DEseq2. A common feature of these methods is their focus on a linear relationship (differential expression) between gene expression and phenotype. However, they may overlook nonlinear relationships due to various factors, such as the degree of disease progression, sex, age, ethnicity, and environmental factors. Maximal information coefficient (MIC) was proposed to capture a wide range of associations of two variables in both linear and nonlinear relationships. However, with MIC it is difficult to highlight genes with nonlinear expression patterns as the genes giving the most strongly supported hits are linearly expressed, especially for noisy data. It is thus important to also efficiently identify nonlinearly expressed genes in order to unravel the molecular basis of disease and to reveal new therapeutic targets.