经济合作与发展组织(Organization for Economic Co-operation and Development,OECD)2021年颁布虹鳟鱼鳃上皮细胞系RTgill-W1急性毒性试验标准方法(OECD 249,2021)作为鱼类急性毒性试验或其预试验的体外替代方法,因试验体系小、通量高...经济合作与发展组织(Organization for Economic Co-operation and Development,OECD)2021年颁布虹鳟鱼鳃上皮细胞系RTgill-W1急性毒性试验标准方法(OECD 249,2021)作为鱼类急性毒性试验或其预试验的体外替代方法,因试验体系小、通量高、周期短而具良好的应用潜能。但方法被认为对神经毒物可能存在预测偏差,尚无对金属化合物的应用报道。此外,细胞毒性数据与鱼体内数据的相关性未知。目前尚无采用该标准方法的实际应用报道,故方法实际适用性不明确。因此,采用涵盖水生毒性试验常用参比物、金属化合物、神经毒物在内的10种化学品进行该方法应用评价研究。结果表明,参照标准方法,同一实验室结果具可重复性及再现性。除通过细胞凋亡途径导致毒性效应的多菌灵外,其余化学品细胞急性毒性EC 50值与标准鱼种(OECD 203推荐鱼种及稀有鮈鲫)急性毒性LC_(50)值呈良好线性关系(相关系数(r 2)为0.7009~0.7975)。细胞急性毒性EC 50与鱼类急性毒性LC_(50)存在数值差异(差异在10倍以内),但同一实验室采用标准方法建立化学品细胞毒性与标准鱼种的体内急性毒性良好转换关系后,可通过细胞急性毒性准确预测鱼体内急性毒性。此外,OECD 249方法可拓展用于评估高分子量、难溶化学品是否可透过生物膜并产生毒性,决定其登记申报时是否可豁免水生毒性试验。展开更多
3D object detection is one of the most challenging research tasks in computer vision. In order to solve the problem of template information dependency of 3D object proposal in the method of 3D object detection based o...3D object detection is one of the most challenging research tasks in computer vision. In order to solve the problem of template information dependency of 3D object proposal in the method of 3D object detection based on 2.5D information, we proposed a 3D object detector based on fusion of vanishing point and prior orientation, which estimates an accurate 3D proposal from 2.5D data, and provides an excellent start point for 3D object classification and localization. The algorithm first calculates three mutually orthogonal vanishing points by the Euler angle principle and projects them into the pixel coordinate system. Then, the top edge of the 2D proposal is sampled by the preset sampling pitch, and the first one vertex is taken. Finally, the remaining seven vertices of the 3D proposal are calculated according to the linear relationship between the three vanishing points and the vertices, and the complete information of the 3D proposal is obtained. The experimental results show that this proposed method improves the Mean Average Precision score by 2.7% based on the Amodal3Det method.展开更多
基金Supported by the National Natural Science Foundation of China(61772328,61802253,61831018)
文摘3D object detection is one of the most challenging research tasks in computer vision. In order to solve the problem of template information dependency of 3D object proposal in the method of 3D object detection based on 2.5D information, we proposed a 3D object detector based on fusion of vanishing point and prior orientation, which estimates an accurate 3D proposal from 2.5D data, and provides an excellent start point for 3D object classification and localization. The algorithm first calculates three mutually orthogonal vanishing points by the Euler angle principle and projects them into the pixel coordinate system. Then, the top edge of the 2D proposal is sampled by the preset sampling pitch, and the first one vertex is taken. Finally, the remaining seven vertices of the 3D proposal are calculated according to the linear relationship between the three vanishing points and the vertices, and the complete information of the 3D proposal is obtained. The experimental results show that this proposed method improves the Mean Average Precision score by 2.7% based on the Amodal3Det method.
