Applied Intelligence - The Open-Set recognition is an important topic in the pattern recognition research field. Different from the close-set recognition task, in the open-set recognition problem,... 相似文献
Fast high-precision patient-specific vascular tissue and geometric structure reconstruction is an essential task for vascular tissue engineering and computer-aided minimally invasive vascular disease diagnosis and surgery. In this paper, we present an effective vascular geometry reconstruction technique by representing a highly complicated geometric structure of a vascular system as an implicit function. By implicit geometric modelling, we are able to reduce the complexity and level of difficulty of this geometric reconstruction task and turn it into a parallel process of reconstructing a set of simple short tubular-like vascular sections, thanks to the easy-blending nature of implicit geometries on combining implicitly modelled geometric forms. The basic idea behind our technique is to consider this extremely difficult task as a process of team exploration of an unknown environment like a cave. Based on this idea, we developed a parallel vascular modelling technique, called Skeleton Marching, for fast vascular geometric reconstruction. With the proposed technique, we first extract the vascular skeleton system from a given volumetric medical image. A set of sub-regions of a volumetric image containing a vascular segment is then identified by marching along the extracted skeleton tree. A localised segmentation method is then applied to each of these sub-image blocks to extract a point cloud from the surface of the short simple blood vessel segment contained in the image block. These small point clouds are then fitted with a set of implicit surfaces in a parallel manner. A high-precision geometric vascular tree is then reconstructed by blending together these simple tubular-shaped implicit surfaces using the shape-preserving blending operations. Experimental results show the time required for reconstructing a vascular system can be greatly reduced by the proposed parallel technique.
目的基于分子对接和网络药理学探讨金银花防治新型冠状病毒肺炎(Corona Virus Disease 2019,COVID-19)的潜在作用机制。方法利用AutoDock Vina将金银花中14种化学成分与新型冠状病毒(Severe Acute Respiratory Syndrome Coronavirus2,SARS-CoV-2)S蛋白受体结合结构域与血管紧张素转化酶II(angiotensin Iconvertin genzyme2,ACE2)蛋白酶结构域复合物(Severe Acute Respiratory Syndrome Coronavirus 2 spike receptor-binding domain bound to the angiotensin I converting enzyme 2 receptor,SARS-CoV-2-S-RBD-ACE2)进行分子对接。利用中药系统药理学数据库与分析平台(Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, TCMSP)获取上述14种化学成分的作用靶点,使用GeneCards及美国国家生物技术信息中心(National Center for Biotechnology Information, NCBI)数据库获取COVID-19靶点,将药物与疾病交集靶点导入Cytoscape 3.7.0软件建立药物-化学成分-靶点-疾病网络,导入STRING数据库获取靶点蛋白质相互作用(protein protein interaction, PPI)网络,导入Bioconductor进行基因本体(geneontology,GO)功能注释和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路富集分析。结果分子对接结果显示金银花中14种化学成分与SARS-CoV-2-S-RBD-ACE2均有较好的结合活性,其中12种成分结合活性优于诊疗方案中4种COVID-19治疗药物;网络药理学结果显示金银花8种成分可通过干预46个靶点、149条通路发挥防治COVID-19的作用。结论金银花中多种化学成分可能通过与SARS-CoV-2-S-RBD-ACE2结合,影响复合物的稳定性,从而发挥治疗COVID-19的作用。金银花治疗COVID-19具有多成分、多靶点、多途径的特点,对COVID-19引起的免疫系统紊乱、炎症等具有潜在的治疗作用,研究结果可为金银花防治COVID-19作用机制提供一定的理论基础与科学依据。 相似文献
A general theoretical model was developed to predict the creep deformation and its effect on the stress relaxation and distribution in the multilayer systems under residual stress and external bending. Based on the proposed solution, a simplified solution for the special case of one film layer on a substrate is also presented. Finite element analysis was carried out to validate the presented model. Good agreements were observed between the finite element simulation and the prediction of the proposed model. In addition, the effects of film thickness on creep strain and stress distribution, the creep effect on neural axis location in the bilayer assembly subjected to the combination of residual stress and external bending were also discussed. 相似文献