In pressure or volume overload, hypertrophic growth of the myocardium is associated with myofibroblast differentiation, a process in which cardiac fibroblasts express smooth muscle α-actin (SMA). The signaling mechanisms that mediate force-induced myofibroblast differentiation and SMA expression are not defined. We examined the role of the Rho–Rho-kinase pathway in force-induced SMA expression in fibroblasts using an in vitro model system that applies static tensile forces (0.65 pN/μm2) to integrins via collagen-coated magnetite beads. Force maximally induced RhoA activation at 10 minutes that was localized to force application sites and required intact actin filaments. Force application induced phosphorylation of LIM kinase (5-10 minutes) and an early dephosphorylation of cofilin (5 minutes) that was followed by prolonged cofilin phosphorylation. These responses were blocked by Y27632, an inhibitor of Rho kinase. Force promoted actin filament assembly at force application sites (10-20 minutes), a process that required Rho kinase and cofilin. Force application induced nuclear translocation of the transcriptional co-activator MRTF-A but not MRTF-B. Nuclear translocation of MRTF-A required Rho kinase and intact actin filaments. Force caused 3.5-fold increases of SMA promoter activity that were completely blocked by transfection of cells with dominant-negative MRTF-A or by inhibition of Rho kinase or by actin filament disassembly. These data indicate that mechanical forces mediate actin assembly through the Rho–Rho-kinase–LIMK cofilin pathway. Force-mediated actin filament assembly promotes nuclear translocation of MRTF and subsequent activation of the SMA promoter to enhance SMA expression.
Tuning surface wettability can modulate the escape behaviour of water molecules to accelerate solar water evaporation.
Continuing success of research on social and computer networks requires open access to realistic measurement datasets. While these datasets can be shared, generally in the form of social or Internet graphs, doing so often risks exposing sensitive user data to the public. Unfortunately, current techniques to improve privacy on graphs only target specific attacks, and have been proven to be vulnerable against powerful de-anonymization attacks.Our work seeks a solution to share meaningful graph datasets while preserving privacy. We observe a clear tension between strength of privacy protection and maintaining structural similarity to the original graph. To navigate the tradeoff, we develop a differentiallyprivate graph model we call Pygmalion. Given a graph G and a desired level of ǫ-differential privacy guarantee, Pygmalion extracts a graph's detailed structure into degree correlation statistics, introduces noise into the resulting dataset, and generates a synthetic graph G ′ . G ′ maintains as much structural similarity to G as possible, while introducing enough differences to provide the desired privacy guarantee. We show that simply applying differential privacy to graphs results in the addition of significant noise that may disrupt graph structure, making it unsuitable for experimental study. Instead, we introduce a partitioning approach that provides identical privacy guarantees using much less noise. Applied to real graphs, this technique requires an order of magnitude less noise for the same privacy guarantees. Finally, we apply our graph model to Internet, web, and Facebook social graphs, and show that it produces synthetic graphs that closely match the originals in both graph structure metrics and behavior in application-level tests.
Background: Determining drug–disease associations is an integral part in the process of drug development. However, the identification of drug–disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug–disease associations is of great significance. Results: In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug–disease association prediction. Specifically, LAGCN first integrates the known drug–disease associations, drug–drug similarities and disease–disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug–disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision–recall curve of 0.3168 and an area under the receiver–operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset. Conclusion: LAGCN is a useful tool for predicting drug–disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.