目的:观察逍遥散对咪喹莫特诱导的银屑病样小鼠模型皮损及抑郁神经递质的干预作用。方法:36只BALB/c雄性小鼠,背部备皮后,随机分为空白对照组、模型组、甲氨蝶呤组和逍遥散高、中、低剂量组,每组6只,除对照组外,其余各组小鼠均用5%咪喹...目的:观察逍遥散对咪喹莫特诱导的银屑病样小鼠模型皮损及抑郁神经递质的干预作用。方法:36只BALB/c雄性小鼠,背部备皮后,随机分为空白对照组、模型组、甲氨蝶呤组和逍遥散高、中、低剂量组,每组6只,除对照组外,其余各组小鼠均用5%咪喹莫特乳膏背部涂抹诱导银屑病样皮损。采用银屑病皮损面积和疾病严重程度(psoriasis area and severity index,PASI)每日进行评分;糖水偏好实验探究小鼠行为学差异;光镜下观察皮损组织形态学变化及表皮厚度;免疫组化法检测皮损组织中T淋巴细胞表面标志物CD3的表达情况;免疫荧光法检测皮损组织中Ki67的表达情况;液相色谱-质谱联用技术检测小鼠海马区和下丘脑区脑组织肾上腺素(adrenaline,AD)、γ-氨基丁酸(γ-aminobutylic acid,GABA)、谷氨酸(glutamate,Glu)、多巴胺(dopamine,DA)及其代谢产物等单胺类神经递质的含量。结果:逍遥散各剂量组和甲氨蝶呤组背部皮损较模型组有明显改善,PASI评分和表皮厚度均显著低于模型组(P<0.05);逍遥散各剂量组和甲氨蝶呤组皮损中Ki67和CD3^+T细胞的水平均较模型组显著降低(P<0.05);空白对照组及逍遥散高剂量组小鼠体质量变化幅度显著小于模型组(P<0.05);空白对照组糖水偏好率显著高于模型组(P<0.01),甲氨蝶呤组和逍遥散各剂量组糖水偏好率与模型组相比有一定升高趋势,但差异无统计学意义;空白对照组小鼠海马区3,4-二羟基苯乙酸(3,4-Dihydroxyphenylacetic acid,DOPAC)、AD、GLU和GABA含量较模型组显著降低(P<0.05),DA和高香草酸(homovanillic acid,HVA)的含量较模型组无显著差异(P>0.05);空白对照组小鼠下丘脑区AD和GABA的含量较模型组显著降低(P<0.05),DA、DOPAC、HVA和GLU的含量较模型组无显著差异(P>0.05);逍遥散高剂量组下丘脑区AD的含量较模型组显著增多(P<0.01),逍遥散低剂量组下丘脑区HVA的含量较模型组显著增多(P<0.01)。PASI评分与海马区DOPAC、AD、GLU和GABA的含量及下丘脑区AD、GLU和GABA的含量呈负相关,即小鼠背部皮损越严重,抑郁相关神经递质表达量越低,表明小鼠抑郁程度加重。结论:逍遥散可以改善咪喹莫特诱导的银屑病样小鼠皮损,并改善其抑郁行为学,上调抑郁症相关单胺类神经递质的表达水平;咪喹莫特诱导的银屑病样小鼠皮损与抑郁相关神经递质的表达呈负相关,其抑郁程度随银屑病皮损的加重而加重。展开更多
Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image qua...Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image quality and data processing capabilities,large-scale crop classification is still challenging.This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE)and Sentinel-1 and Sentinel-2 images.We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September),combined monthly composite images of reflectance bands,vegetation indices and polarization bands as input features,and then performed crop classification using a Random Forest(RF)classifier.The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area,and the overall accuracy(OA)reached 89.75%.Through experiments,we also found that the classification performance using time-series images is significantly better than that using single-period images.Compared with the use of traditional bands only(i.e.,the visible and near-infrared bands),the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly,followed by the addition of red-edge bands.Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%,respectively,compared to using only the Sentinel-2 reflectance bands.The analysis of timeliness revealed that when the July image is available,the increase in the accuracy of crop classification is the highest.When the Sentinel-1 and Sentinel-2 images for May,June,and July are available,an OA greater than 80%can be achieved.The results of this study are applicable to large-scale,high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.展开更多
文摘目的:观察逍遥散对咪喹莫特诱导的银屑病样小鼠模型皮损及抑郁神经递质的干预作用。方法:36只BALB/c雄性小鼠,背部备皮后,随机分为空白对照组、模型组、甲氨蝶呤组和逍遥散高、中、低剂量组,每组6只,除对照组外,其余各组小鼠均用5%咪喹莫特乳膏背部涂抹诱导银屑病样皮损。采用银屑病皮损面积和疾病严重程度(psoriasis area and severity index,PASI)每日进行评分;糖水偏好实验探究小鼠行为学差异;光镜下观察皮损组织形态学变化及表皮厚度;免疫组化法检测皮损组织中T淋巴细胞表面标志物CD3的表达情况;免疫荧光法检测皮损组织中Ki67的表达情况;液相色谱-质谱联用技术检测小鼠海马区和下丘脑区脑组织肾上腺素(adrenaline,AD)、γ-氨基丁酸(γ-aminobutylic acid,GABA)、谷氨酸(glutamate,Glu)、多巴胺(dopamine,DA)及其代谢产物等单胺类神经递质的含量。结果:逍遥散各剂量组和甲氨蝶呤组背部皮损较模型组有明显改善,PASI评分和表皮厚度均显著低于模型组(P<0.05);逍遥散各剂量组和甲氨蝶呤组皮损中Ki67和CD3^+T细胞的水平均较模型组显著降低(P<0.05);空白对照组及逍遥散高剂量组小鼠体质量变化幅度显著小于模型组(P<0.05);空白对照组糖水偏好率显著高于模型组(P<0.01),甲氨蝶呤组和逍遥散各剂量组糖水偏好率与模型组相比有一定升高趋势,但差异无统计学意义;空白对照组小鼠海马区3,4-二羟基苯乙酸(3,4-Dihydroxyphenylacetic acid,DOPAC)、AD、GLU和GABA含量较模型组显著降低(P<0.05),DA和高香草酸(homovanillic acid,HVA)的含量较模型组无显著差异(P>0.05);空白对照组小鼠下丘脑区AD和GABA的含量较模型组显著降低(P<0.05),DA、DOPAC、HVA和GLU的含量较模型组无显著差异(P>0.05);逍遥散高剂量组下丘脑区AD的含量较模型组显著增多(P<0.01),逍遥散低剂量组下丘脑区HVA的含量较模型组显著增多(P<0.01)。PASI评分与海马区DOPAC、AD、GLU和GABA的含量及下丘脑区AD、GLU和GABA的含量呈负相关,即小鼠背部皮损越严重,抑郁相关神经递质表达量越低,表明小鼠抑郁程度加重。结论:逍遥散可以改善咪喹莫特诱导的银屑病样小鼠皮损,并改善其抑郁行为学,上调抑郁症相关单胺类神经递质的表达水平;咪喹莫特诱导的银屑病样小鼠皮损与抑郁相关神经递质的表达呈负相关,其抑郁程度随银屑病皮损的加重而加重。
基金funded by the National Key R&D Program of China(2017YFD0201803)the Talent Recruitment Project of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences.
文摘Rapid and accurate access to large-scale,high-resolution crop-type distribution maps is important for agricultural management and sustainable agricultural development.Due to the limitations of remote sensing image quality and data processing capabilities,large-scale crop classification is still challenging.This study aimed to map the distribution of crops in Heilongjiang Province using Google Earth Engine(GEE)and Sentinel-1 and Sentinel-2 images.We obtained Sentinel-1 and Sentinel-2 images from all the covered study areas in the critical period for crop growth in 2018(May to September),combined monthly composite images of reflectance bands,vegetation indices and polarization bands as input features,and then performed crop classification using a Random Forest(RF)classifier.The results show that the Sentinel-1 and Sentinel-2 monthly composite images combined with the RF classifier can accurately generate the crop distribution map of the study area,and the overall accuracy(OA)reached 89.75%.Through experiments,we also found that the classification performance using time-series images is significantly better than that using single-period images.Compared with the use of traditional bands only(i.e.,the visible and near-infrared bands),the addition of shortwave infrared bands can improve the accuracy of crop classification most significantly,followed by the addition of red-edge bands.Adding common vegetation indices and Sentinel-1 data to the crop classification improved the overall classification accuracy and the OA by 0.2 and 0.6%,respectively,compared to using only the Sentinel-2 reflectance bands.The analysis of timeliness revealed that when the July image is available,the increase in the accuracy of crop classification is the highest.When the Sentinel-1 and Sentinel-2 images for May,June,and July are available,an OA greater than 80%can be achieved.The results of this study are applicable to large-scale,high-resolution crop classification and provide key technologies for remote sensing-based crop classification in small-scale agricultural areas.
