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Xiaoyin Bu,1,2,* Weifeng Pan,1,* Junhui Wang,1 Liping Liu,1 Zhao Yin,1 Hua Jin,1 Qifa Liu,1 Lei Zheng,3 Haitao Sun,4 Ya Gao,3 Baohong Ping1,2 1Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China; 2Department of Hematology, Huiqiao Medical Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China; 3Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China; 4Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, People’s Republic of China*These authors contributed equally to this workCorrespondence: Baohong Ping, Department of Hematology, Huiqiao Medical Center, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China, Tel +86 15625042109, Fax +86 2062781875, Email nfyypingbaohong66@163.com Ya Gao, Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China, Email smugaoya_729@163.comBackground: Acute graft-versus-host disease (aGVHD) initiated by intestinal barrier dysfunction and gut microbiota dysbiosis, remains one of the main obstacles for patients undergoing allogenic hematopoietic stem cell transplantation (allo-HSCT) to achieve good prognosis. Studies have suggested that mesenchymal stem cells (MSCs) can suppress immune responses and reduce inflammation, and human leukocyte antigen-G5 (HLA-G5) plays an important role in the immunomodulatory effects of MSCs, but very little is known about the potential mechanisms in aGVHD. Thus, we explored the effect of HLA-G5 on the immunosuppressive properties of human amnion MSCs (hAMSCs) and demonstrated its mechanism related to the gut microbiota at the intestinal barrier in aGVHD.Methods: Patients undergoing allo-HSCT were enrolled to detect the levels of plasm

In hydro-power systems, the fault of equipment is an important potential threat for the safe production of electricity. Therefore, the automation and intelligence of fault diagnosis becomes the popular issue in the research on hydro-power system. In this paper, a knowledge graph-based method is put forth to diagnose faults occurred in hydro-power systems, since the knowledge graph can store structured and unstructured data for better fault diagnosis and intelligently search the reasons of the faults. First, we model the knowledge graph for hydro-power plants, where the rational path for the fault reason is formulated. Then, the bi-directional long short-term memory (Bi-LSTM) with conditional random field (CRF) is used to extract the entities and relations to the given documents, which record the phenomenon and reasons for the occurred faults. Moreover, the attention scheme is employed in the Bi-LSTM to weigh the closer relationships to improve the diagnosis accuracy. An automatic diagnosis algorithm is developed to improve the diagnosing efficiency by constructing rational paths, with which directive and in-directive factors for occurring faults can be traced. Simulation results reveal that the intelligent search method with a knowledge graph can effectively find the reason, locate the position, and provide useful suggestions for the occurred faults.