| 1 | Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering显示文摘The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation capability.In a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among samples.Combining these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering assignment.First,the diversity and amount of input information are enhanced to generate highly useful representations for downstream tasks.Subsequently,the topological graph is constructed to express the spatial relationship of features.For a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively optimized.Three external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering performances.Experimental results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service dataset.The GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech datasets.Moreover,DBI indicates the dispersion of cluster distribution and compactness within the cluster. | Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li | 2022 | Tsinghua Science and Technology2022，27，5： | 1 |
| 2 | Prevention and control strategies for emergency,limited-term,and elective operations in pediatric surgery during the epidemic period of COVID-19显示文摘The outbreak of coronavirus disease 2019(COVID-19)caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)has spread to more than 100 countries.Children approved to be susceptible to SARS-CoV-2 infection.Preventing and controlling the epidemic while ensuring orderly flows of pediatric surgery clinical work has proven to be a big challenge for both patients and clinicians during the epidemic.Based on the transmission characteristics of SARS-CoV-2 and the requirements for prevention and control of COVID-19,the authors proposed some concrete measures and practical strategies of managing emergency,limited-term,and elective pediatric surgeries during the epidemic period. | Daxing Tang Jinfa Tou Jinhu Wang Qingjiang Chen Wei Wang Jinjin Huang Hangyan Zhao Jia Wei Zheming Xu Dongyan Zhao Junfen Fu Qiang Shu | 2020 | World Journal of Pediatric Surgery2020，3，1： | 0 |
| 3 | The multifaceted role of placental growth factor in the pathogenesis and progression of bronchial asthma and pulmonary fibrosis:Therapeutic implications显示文摘Placental growth factor(PlGF)is a glycosylated dimeric protein that is homologous to vascular endothelial growth factor(VEGF).PlGF expression is upregulated in patients with bronchial asthma,suggesting that it plays a role in the pathogenesis of asthma.Bronchial asthma is characterized by chronic airway inflammation and airway hyperresponsiveness(AHR).After recurrent asthma attacks,pulmonary fibrosis develops and leads to airway remo-deling and a further decline in lung function.In this review,we focused on the pivotal role of PlGF in chronic airway inflammation,AHR,and airway remodeling during bronchial asthma.Furthermore,we summarized data showing that PlGF may be a potential therapeutic target in bronchial asthma. | Dan Huang Gege Liu Zhiyi Xu Shushu Chen Cuili Wang Dewei Liu Jiahao Cao Junfen Cheng Bin Wu Dong Wu | 2023 | Genes & Diseases2023，10，4： | 0 |
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