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Xi Lin,1,2 Junwan Lu,1,2 Changrui Qian,1,2 Hailong Lin,1– 3 Qiaoling Li,1– 3 Xueya Zhang,1– 3 Hongmao Liu,1– 3 Zhewei Sun,1,2 Danying Zhou,1,2 Wei Lu,1,2 Mei Zhu,4 Hailin Zhang,2,3 Teng Xu,5 Kewei Li,1,2 Qiyu Bao,1– 3 Li Lin2,3 1Key Laboratory of Medical Genetics of Zhejiang Province, Key Laboratory of Laboratory Medicine, Ministry of Education of China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China; 2Institute of Biomedical Informatics, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China; 3The Second Affiliated Hospital and Children’s Hospital, Wenzhou Medical University, Wenzhou, 325027, People’s Republic of China; 4Department of Clinical Laboratory, Zhejiang Hospital, Hangzhou, Zhejiang, 310013, People’s Republic of China; 5Institute of Translational Medicine, Baotou Central Hospital, Baotou, 014040, People’s Republic of ChinaCorrespondence: : Li LinThe Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou, 325027, People’s Republic of ChinaTel +86-577-88002134Email linli97@126.comQiyu BaoInstitute of Biomedical Informatics, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of ChinaTel/Fax +86-577-86699398Email baoqy@genomics.cnPurpose: An increasing frequency of antibiotic resistance has been observed in both clinical and environmental Aeromonas hydrophila isolates in recent years. However, there are still very few in-depth studies regarding the role of plasmids in the antibiotic resistance of A. hydrophila. Hence, we investigated the molecular and functional characterization of a multidrug-resistant plasmid encoding an NDM-like metallo-β-lactamase, AFM-1, in the clinical A

Hongmao Liu,1– 3,* Hailong Lin,1– 3,* Zhewei Sun,2,3 Xinyi Zhu,1– 3 Xueya Zhang,1– 3 Qiaoling Li,1– 3 Junwan Lu,2,3 Xi Lin,2,3 Li Lin,1,3 Kewei Li,2,3 Mei Zhu,4 Qiyu Bao,2,3 Teng Xu,5 Yunliang Hu,1– 3 Hailin Zhang1,3 1The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou 325027, People’s Republic of China; 2Key Laboratory of Medical Genetics of Zhejiang Province, Key Laboratory of Laboratory Medicine, Ministry of Education, China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou 325035, People’s Republic of China; 3Institute of Biomedical Informatics, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou 325035, People’s Republic of China; 4Department of Clinical Laboratory, Zhejiang Hospital, Hangzhou, Zhejiang 310013, People’s Republic of China; 5Institute of Translational Medicine, Baotou Central Hospital, Baotou 014040, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yunliang Hu; Hailin ZhangThe Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou 325027, People’s Republic of ChinaTel/Fax +86-577-86699398; Tel +86-577-88002134Email huyunliang66@163.com; zhlwz97@hotmail.comBackground: This study was designed to characterize the dissemination mechanism and genetic context of Klebsiella pneumoniae carbapenemase (KPC) genes in carbapenem-resistant Klebsiella pneumoniae (CRKP) isolates.Methods: A retrospective analysis was performed on CRKP strains isolated from a teaching hospital of Wenzhou Medical University during 2015– 2017. Polymerase chain reaction (PCR)-based amplification and whole-genome sequencing (WGS) were used to analyze the genetic context of the blaKPC-2 gene. Conjugation experiments were performed to evaluate th

Movie graphs play an important role to bridge heterogenous modalities of videos and texts in human-centric retrieval. In this work, we propose Graph Wasserstein Correlation Analysis (GWCA) to deal with the core issue therein, i.e, cross heterogeneous graph comparison. Spectral graph filtering is introduced to encode graph signals, which are then embedded as probability distributions in a Wasserstein space, called graph Wasserstein metric learning. Such a seamless integration of graph signal filtering together with metric learning results in a surprise consistency on both learning processes, in which the goal of metric learning is just to optimize signal filters or vice versa. Further, we derive the solution of the graph comparison model as a classic generalized eigenvalue decomposition problem, which has an exactly closed-form solution. Finally, GWCA together with movie/text graphs generation are unified into the framework of movie retrieval to evaluate our proposed method. Extensive experiments on MovieGrpahs dataset demonstrate the effectiveness of our GWCA as well as the entire framework.

Graph convolutional networks (GCNs) have shown the powerful ability in text structure representation and effectively facilitate the task of text classification. However, challenges still exist in adapting GCN on learning discriminative features from texts due to the main issue of graph variants incurred by the textual complexity and diversity. In this paper, we propose a dual-attention GCN to model the structural information of various texts as well as tackle the graph-invariant problem through embedding two types of attention mechanisms, i.e. the connection-attention and hop-attention, into the classic GCN. To encode various connection patterns between neighbour words, connection-attention adaptively imposes different weights specified to neighbourhoods of each word, which captures the short-term dependencies. On the other hand, the hop-attention applies scaled coefficients to different scopes during the graph diffusion process to make the model learn more about the distribution of context, which captures long-term semantics in an adaptive way. Extensive experiments are conducted on five widely used datasets to evaluate our dual-attention GCN, and the achieved state-of-the-art performance verifies the effectiveness of dual-attention mechanisms.