The Core Protein of Glypican Daily-Like Determines Its Biphasic Activity in Wingless Morphogen Signaling

Dally-like (Dlp) is a glypican-type heparan sulfate proteoglycan (HSPG), containing a protein core and attached glycosaminoglycan (GAG) chains. In Drosophila wing discs, Dlp represses short-range Wingless (Wg) signaling, but activates long-range Wg signaling. Here, we show that Dlp core protein has similar biphasic activity as wild-type Dlp. Dlp core protein can interact with Wg; the GAG chains enhance this interaction. Importantly, we find that Dlp exhibits a biphasic response, regardless of whether its glycosylphosphatidylinositol linkage to the membrane can be cleaved. Rather, the transition from signaling activator to repressor is determined by the relative expression levels of Dlp and the Wg receptor, Frizzled (Fz) 2. Based on these data, we propose that the principal function of Dlp is to retain Wg on the cell surface. As such, it can either compete with the receptor or provide ligands to the receptor, depending on the ratios of Wg, Fz2, and Dlp.National Institutes of Health American Cancer Society March of Dimes American Heart Associatio

Additional file 1 of Genetically predicted plasma levels of amino acids and metabolic dysfunction-associated fatty liver disease risk: a Mendelian randomization study

Additional file 1: Table S1. Proxy SNPs of genetic instruments identified in the MAFLD summary data. Table S2. Characteristics of SNPs instrumenting for amino acids in the MR analysis. Table S3. Genetic associations between genetic instrumental variables and MAFLD in two GWAS data used for discovery and replication analysis. Table S4. Proportion of variation in amino acids explained by genetic instruments. Table S5. Heterogeneity tested between SNP-specific causal estimates in the discovery MR analysis. Table S6. MR sensitivity analysis results using weighted median and MR-Egger regression methods. Table S7. Genetic variants associated with BMI, waist-to-hip ratio and whole body fat mass by searching the PhenoScanner database. Table S8. MR analysis results for each individual cohort and Cochrane’s Q statistic

Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

Abstract Objective In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke® video recording of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required

Additional file 1 of The hepato-ovarian axis: genetic evidence for a causal association between non-alcoholic fatty liver disease and polycystic ovary syndrome

Additional file 1: Table S1. Key characteristics of participating studies. Table S2. GWAS significant SNPs used as genetic instruments for fasting insulin and fasting glucose. Table S3. GWAS significant SNPs used as genetic instruments for serum SHBG levels and bioavailable testosterone levels in women. Table S4. Direct causal effects of NAFLD, fasting insulin, fasting glucose, serum SHBG levels, and serum bioavailable testosterone levels on PCOS risk via multivariable MR analysis. Table S5. Direct causal effects of NAFLD, fasting insulin, fasting glucose, and serum SHBG levels on serum bioavailable testosterone levels via multivariable MR analysis. Table S6. Direct causal effects of NAFLD, fasting insulin, and fasting glucose on serum SHBG levels via multivariable MR analysis. Table S7. Obesity-related genome-wide significant genetic variants. Table S8. Directional pleiotropy test using MR-Egger intercepts. Table S9. Horizontal pleiotropy test using MR-PRESSO. Table S10. Linkage disequilibrium score regression results on genetic correlations between NAFLD, fasting insulin, fasting glucose, SHBG, BT, and PCOS. Table S11. Indirect causal effects between NAFLD and PCOS via fasting insulin, serum SHBG levels, and serum bioavailable testosterone levels through step-wise MR analysis