Medicinal leeches have been utilized in therapy for thousands of years. However, the adaptation physiology between leeches and hosts is not fully understand. To disclose the molecular mechanisms of adaptation between ...Medicinal leeches have been utilized in therapy for thousands of years. However, the adaptation physiology between leeches and hosts is not fully understand. To disclose the molecular mechanisms of adaptation between leech and host, the body transcriptomes of hunger and fed blood-sucking Poecilobdella javanica, Haemadipsa cavatuses, and Hirudo nipponia leeches were obtained by RNA sequencing, after comparison, a stratified unigenes group was obtained, which closely correlated to body distension. In the group, Rfamide receptor decreased significantly (P < 0.05) while serotonin receptor increased significantly (P < 0.05). Moreover, four KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, including cardiac muscle contraction, complement and coagulation cascades, renin-angiotensin system, and hypertrophic cardiomyopathy were significantly enriched. The unigenes annotation, neuroregulators correlation analysis and induced function of the KEGG pathways, were consistently supported the same result as: vasoconstriction and systole reaction enhance in hunger leeches and vice versa vasodilation and diastole increase in fed leeches, meanwhile, Interspecific comparison and correlative analyses of physiological function showed that the strongest reaction of induced heart failure from four KEGG occur in strongest reaction of systole in hungry P. javanica and in strongest reaction of diastole in fed H. nipponia. Overall, heart failure is likely a physiological function involved in feeding behaviour.展开更多
Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in futu...Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.展开更多
文摘Medicinal leeches have been utilized in therapy for thousands of years. However, the adaptation physiology between leeches and hosts is not fully understand. To disclose the molecular mechanisms of adaptation between leech and host, the body transcriptomes of hunger and fed blood-sucking Poecilobdella javanica, Haemadipsa cavatuses, and Hirudo nipponia leeches were obtained by RNA sequencing, after comparison, a stratified unigenes group was obtained, which closely correlated to body distension. In the group, Rfamide receptor decreased significantly (P < 0.05) while serotonin receptor increased significantly (P < 0.05). Moreover, four KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, including cardiac muscle contraction, complement and coagulation cascades, renin-angiotensin system, and hypertrophic cardiomyopathy were significantly enriched. The unigenes annotation, neuroregulators correlation analysis and induced function of the KEGG pathways, were consistently supported the same result as: vasoconstriction and systole reaction enhance in hunger leeches and vice versa vasodilation and diastole increase in fed leeches, meanwhile, Interspecific comparison and correlative analyses of physiological function showed that the strongest reaction of induced heart failure from four KEGG occur in strongest reaction of systole in hungry P. javanica and in strongest reaction of diastole in fed H. nipponia. Overall, heart failure is likely a physiological function involved in feeding behaviour.
基金supported by the Teaching Reform Research Project of Qinghai Minzu University,China(2021-JYYB-009)the“Chunhui Plan”Cooperative Scientific Research Project of the Ministry of Education of China(2018).
文摘Link prediction has attracted wide attention among interdisciplinaryresearchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks.Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connectedgraph. However, the complexity of the real world makes the complex networksabstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start linkprediction is favored as one of the most valuable subproblems of traditional linkprediction. However, due to the loss of many links in the observation network, thetopological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topologicalinformation from observed network becomes the key point to solve the problemof cold-start link prediction. In this paper, we propose a framework for solving thecold-start link prediction problem, a joint-weighted symmetric nonnegative matrixfactorization model fusing graph regularization information, based on low-rankapproximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designedgraph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain eachother. Finally, a unified framework for implementing cold-start link prediction isconstructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validationon five real networks with attributes shows that the proposed model has very goodpredictive performance when predicting missing edges of isolated nodes.
