(E)-N′-(Furan-2-ylmethyl­ene)-4-(quinolin-8-yl­oxy)butanohydrazide

In the title mol­ecule, C18H17N3O3, the dihedral angle between the mean planes of the furan ring and the quinoline group is 77.4 (2)°. In the crystal structure, inter­molecular N—H⋯N hydrogen bonds link the mol­ecules into centrosymmetric dimers

Multiple functions of the von Willebrand Factor A domain in matrilins: secretion, assembly, and proteolysis

The von Willebrand Factor A (vWF A) domain is one of the most widely distributed structural modules in cell-matrix adhesive molecules such as intergrins and extracellular matrix proteins. Mutations in the vWF A domain of matrilin-3 cause multiple epiphyseal dysplasia (MED), however the pathological mechanism remains to be determined. Previously we showed that the vWF A domain in matrilin-1 mediates formation of a filamentous matrix network through metal-ion dependent adhesion sites in the domain. Here we show two new functions of the vWF A domain in cartilage-specific matrilins (1 and 3). First, vWF A domain regulates oligomerization of matrilins. Insertion of a vWF A domain into matrilin-3 converts the formation of a mixture of matrilin-3 tetramer, trimer, and dimer into a tetramer only, while deletion of a vWF A domain from matrilin-1 converts the formation of the native matrilin-1 trimer into a mixture of trimer and dimer. Second, the vWF A domain protects matrilin-1 from proteolysis. We identified a latent proteolytic site next to the vWF A2 domain in matrilin-1, which is sensitive to the inhibitors of matrix proteases. Deletion of the abutting vWF A domain results in degradation of matrilin-1, presumably by exposing the adjacent proteolytic site. In addition, we also confirmed the vWF A domain is vital for the secretion of matrilin-3. Secretion of the mutant matrilin-3 harbouring a point mutation within the vWF A domain, as occurred in MED patients, is markedly reduced and delayed, resulting from intracellular retention of the mutant matrilin-3. Taken together, our data suggest that different mutations/deletions of the vWF A domain in matrilins may lead to distinct pathological mechanisms due to the multiple functions of the vWF A domain

Ghrelin Stimulates Hepatocyte Proliferation via Regulating Cell Cycle Through GSK3β/Β-Catenin Signaling Pathway

Background/Aims: Obesity is associated with a reduction in ghrelin, a 28 aa gastric hormone. Whether reduced ghrelin contributes to the impaired proliferation of hepatocytes associated with obesity-related steatosis remains largely unknown. Here we examined the effects of ghrelin on the proliferation of hepatocytes derived from lean and obese mice. Methods: AML 12 cells or hepatocytes isolated from mice fed normal chow diet (NCD) or high fat diet (HFD) were used. Effects of ghrelin on hepatocyte proliferation were detected with CCK8 assay and EdU staining. Cell cycle was analyzed by flow cytometry. Levels of proliferation markers was examined by Western blot. Results: Growth hormone secretagogue receptor 1a (GHS-R1a) mRNA and protein were present in hepatocytes. Levels of GHS-R1a were increased upon ghrelin treatment. Ghrelin significantly increased hepatocyte proliferation measured by Cell Counting Kit-8(CCK8) assay and EdU staining in a dose- and time-dependent manner. Proportion of cells in S phase was markedly increased upon treatment with ghrelin. Ghrelin significantly increased levels of proliferating cell nuclear antigen (PCNA) and cyclin D1, while reducing p27 in hepatocytes from mice fed NCD or HFD. Deletion of GHS-R1a completely abolished the effects of ghrelin in cultured hepatocytes. Ghrelin stimulated the phosphorylation of glycogen synthase kinase 3 beta (GSK3β), leading to subsequent increase of nuclear β-catenin in hepatocytes derived from lean and obese mice. This effect was dependent on the GHS-R1a. Conclusion: Ghrelin activates GHS-R1a to stimulate hepatocyte proliferation via GSK3/β-catenin signaling pathway

Recognizing Multidimensional Engagement of E-learners Based on Multi-channel Data in E-learning Environment

Despite recent advances in MOOC, the current e-learning systems have advantages of alleviating barriers by time differences, and geographically spatial separation between teachers and students. However, there has been a 'lack of supervision' problem that e-learner's learning unit state(LUS) can't be supervised automatically. In this paper, we present a fusion framework considering three channel data sources: 1) videos/images from a camera, 2) eye movement information tracked by a low solution eye tracker and 3) mouse movement. Based on these data modalities, we propose a novel approach of multi-channel data fusion to explore the learning unit state recognition. We also propose a method to build a learning state recognition model to avoid manually labeling image data. The experiments were carried on our designed online learning prototype system, and we choose CART, Random Forest and GBDT regression model to predict e-learner's learning state. The results show that multi-channel data fusion model have a better recognition performance in comparison with single channel model. In addition, a best recognition performance can be reached when image, eye movement and mouse movement features are fused.Comment: 4 pages, 4 figures, 2 table