Background: Bone morphogenetic protein 5 (BMP5) has been identified as one of the important risk factors for microtia; however, the link between them has yet to be clarified. In this study, we aimed to demonstrate the relationship of BMP5 with mitochondrial function and investigate the specific role of mitochondria in regulating microtia development.Methods: BMP5 expression was measured in auricular cartilage tissues from patients with and without microtia. The effects of BMP5 knockdown on cellular function and mitochondrial function were also analyzed in vitro. Changes in genome-wide expression profiles were measured in BMP5-knockdown cells.Finally, the specific impact of BMP5 down-regulation on mitochondrial fat oxidation was analyzed in vitro.Results: BMP5 expression was down-regulated in the auricular cartilage tissues of microtia patients.BMP5 down-regulation inhibited various cellular functions in vitro, including cell proliferation, mobility, and cytoactivity. The functional integrity of mitochondria was also damaged, accompanied by a decrease in mitochondrial membrane potential, reactive oxygen species (ROS) neutralization, and reduced adenosine triphosphate (ATP) production. Carnitine O-palmitoyltransferase 2 and diacylglycerol acyltransferase 2, two of the key regulators of mitochondrial lipid oxidation, were also found to be decreased by BMP5 downregulation.Conclusions: Down-regulation of BMP5 affects glycerolipid metabolism and fatty acid degradation, leading to mitochondrial dysfunction, reduced ATP production, and changes in cell function, and ultimately resulting in microtia. This research provides supporting evidence for an important role of BMP5 downregulation in affecting mitochondrial metabolism in cells, and sheds new light on the mechanisms underlying the pathogenesis of microtia.
The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time. Most past studies on employee turnover have focused on analyzing impact factors or using simple network centrality measures. In this paper, we study the problem from a completely new perspective by modeling users' historical job records as a dynamic bipartite graph. Specifically, we propose a bipartite graph embedding method with temporal information called dynamic bipartite graph embedding (DBGE) to learn the vector representation of employees and companies. Our approach not only considers the relations between employees and companies but also incorporates temporal information embedded in consecutive work records. We first define the Horary Random Walk on a bipartite graph to generate a sequence for each vertex in chronological order. Then, we employ the skip-gram model to obtain a temporal low-dimensional vector representation for each vertex and apply machine learning methods to predict employee turnover behavior by combining embedded features with employees' basic information. Experiments on a real-world dataset collected from one of China's largest online professional social networks show that the features learned through DBGE can significantly improve turnover prediction performance. Moreover, experiments on public Amazon and Taobao datasets show that our approach achieves better performance in the link prediction and visualization task than other graph embedding methods that do not consider temporal information. INDEX TERMS Graph embedding, employee turnover prediction, dynamic bipartite graph, horary random walk.
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.