通过肠道菌群改变研究青砖茶(GBT)对小鼠非酒精性脂肪肝(NAFLD)的预防作用。C57BL/6小鼠随机分为5组,即正常对照组(NC),模型对照组(MC),阳性药物对照组(PC)以及青砖茶低剂量组(LD)、高剂量组(HD),高脂饲料喂养小鼠建立NAFLD模型,同时预...通过肠道菌群改变研究青砖茶(GBT)对小鼠非酒精性脂肪肝(NAFLD)的预防作用。C57BL/6小鼠随机分为5组,即正常对照组(NC),模型对照组(MC),阳性药物对照组(PC)以及青砖茶低剂量组(LD)、高剂量组(HD),高脂饲料喂养小鼠建立NAFLD模型,同时预防性给予低、高剂量青砖茶水提取物和阳性药物血脂康,分别测定小鼠的体重、食物利用率、肝重、肝指数、TC、LDL-C/HDL-C及ALT含量,HE染色和油红O染色观察肝组织病理切片,ELISA检测肝组织IL-1β、IL-18含量变化,16 S rDNA V3-V4区高通量测序分析肠道菌群变化,Spearman相关性分析方法分析菌群与NAFLD表型的相关性。与模型组比较,青砖茶组小鼠体重、食物利用率、肝重、肝指数、血清TC、LDL-C/HDL-C、ALT、肝组织TC、IL-1β、IL-18含量均有显著性降低,肝脏病变程度有所改善;肠道菌群分析及相关性分析显示Bacteroides物种丰度降低,且与NAFLD表型呈正相关,Lactobacillus、Alloprevotella、Saccharibacteria_genera_incertae_sedis物种丰度增加,且与NAFLD表型呈负相关,与NAFLD表型相关性最强的菌群为Bacteroides和Lactobacillus。青砖茶对NAFLD具有一定的预防作用,其作用可能与影响肠道菌群的改变有关。展开更多
Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management.For adapting to the complexity of the field environment in var...Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management.For adapting to the complexity of the field environment in various detection situations,such as illumination changes,color variation,fruit overlap,and branches and leaves shading,a robust algorithm for detecting and counting apples based on their color and shape modes was proposed.Firstly,BP(back propagation)neural network was used to train apple color identification model.Accordingly the irrelevant background was removed by using the trained neural network model and the image only containing the apple color pixels was acquired.Then apple edge detection was carried out after morphological operations on the obtained image.Finally,the image was processed by using circle Hough transform algorithm,and apples were located with the help of calculating the center coordinates of each apple edge circle.The validation experimental results showed that the correlation coefficient of R2 between the proposed approaches based counting and manually counting reached 0.985.It illustrated that the proposed algorithm could be used to detect and count apples from apple trees’images taken in field environment with a high precision and strong anti-jamming feature.展开更多
文摘通过肠道菌群改变研究青砖茶(GBT)对小鼠非酒精性脂肪肝(NAFLD)的预防作用。C57BL/6小鼠随机分为5组,即正常对照组(NC),模型对照组(MC),阳性药物对照组(PC)以及青砖茶低剂量组(LD)、高剂量组(HD),高脂饲料喂养小鼠建立NAFLD模型,同时预防性给予低、高剂量青砖茶水提取物和阳性药物血脂康,分别测定小鼠的体重、食物利用率、肝重、肝指数、TC、LDL-C/HDL-C及ALT含量,HE染色和油红O染色观察肝组织病理切片,ELISA检测肝组织IL-1β、IL-18含量变化,16 S rDNA V3-V4区高通量测序分析肠道菌群变化,Spearman相关性分析方法分析菌群与NAFLD表型的相关性。与模型组比较,青砖茶组小鼠体重、食物利用率、肝重、肝指数、血清TC、LDL-C/HDL-C、ALT、肝组织TC、IL-1β、IL-18含量均有显著性降低,肝脏病变程度有所改善;肠道菌群分析及相关性分析显示Bacteroides物种丰度降低,且与NAFLD表型呈正相关,Lactobacillus、Alloprevotella、Saccharibacteria_genera_incertae_sedis物种丰度增加,且与NAFLD表型呈负相关,与NAFLD表型相关性最强的菌群为Bacteroides和Lactobacillus。青砖茶对NAFLD具有一定的预防作用,其作用可能与影响肠道菌群的改变有关。
基金The authors acknowledge that this research was supported by Chinese National Science and Technology Support Program(2012BAH29B04)863 Project(2012AA101900).
文摘Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management.For adapting to the complexity of the field environment in various detection situations,such as illumination changes,color variation,fruit overlap,and branches and leaves shading,a robust algorithm for detecting and counting apples based on their color and shape modes was proposed.Firstly,BP(back propagation)neural network was used to train apple color identification model.Accordingly the irrelevant background was removed by using the trained neural network model and the image only containing the apple color pixels was acquired.Then apple edge detection was carried out after morphological operations on the obtained image.Finally,the image was processed by using circle Hough transform algorithm,and apples were located with the help of calculating the center coordinates of each apple edge circle.The validation experimental results showed that the correlation coefficient of R2 between the proposed approaches based counting and manually counting reached 0.985.It illustrated that the proposed algorithm could be used to detect and count apples from apple trees’images taken in field environment with a high precision and strong anti-jamming feature.
