In order to improve the bioavailability of lutein(LUT),a novel lutein-stevio side nanoparticle(LUT-STE)were prepared previously,but the information about LUT-STE on protecting of eye health was limited.This study inve...In order to improve the bioavailability of lutein(LUT),a novel lutein-stevio side nanoparticle(LUT-STE)were prepared previously,but the information about LUT-STE on protecting of eye health was limited.This study investigated the effect of LUT-STE on antioxidant activity of H_(2)O_(2)-induced human retinal pigment epithelial(ARPE)cells.LUT and LUT-STE(final concentration of 5μg/mL)significantly enhanced cell viability from(74.84±5.10)%to(81.92±10.01)%(LUT)and(89.33±4.34)%(LUT-STE),and inhibited the cell apoptosis(P<0.05).After pretreatment with LUT-STE in ARPE cells,the levels of superoxide dismutase(SOD),catalase(CAT)and glutathion peroxidase(GSH-Px)in ARPE cells were significantly increased(P<0.05),the contents of reactive oxygen species(ROS)and malondialdehyde(MDA)were decreased.In addition,the vascular endothelial growth factor(VEGF)levels were inhibited by 13.61%and 17.39%,respectively,pretreatment with LUT and LUT-STE.Western blotting results showed that the pretreatment with LUT-STE inhibited the expression of caspase-9 and caspase-3 and up-regulated Bcl-2/Bax pathway to inhibit H_(2)O_(2)-induced apoptosis.In summary,the novel delivery LUT-STE had more pronounced inhibitory effect on H_(2)O_(2)-induced damage in human ARPE cells.展开更多
为准确高效检测糙皮侧耳(Pleurotus ostreatus)黄斑病,构建基于YOLOv5s的黄斑病检测模型YOLOv5s-GCE。该模型在YOLOv5s模型基础上引入轻量化GhostNet结构,将坐标注意力(coordinate attention,CA)模块嵌入到YOLOv5s主干网络中,并利用增...为准确高效检测糙皮侧耳(Pleurotus ostreatus)黄斑病,构建基于YOLOv5s的黄斑病检测模型YOLOv5s-GCE。该模型在YOLOv5s模型基础上引入轻量化GhostNet结构,将坐标注意力(coordinate attention,CA)模块嵌入到YOLOv5s主干网络中,并利用增强交并比(enhanced intersection over union,EIOU)损失函数替换原YOLOv5s网络的完整交并比(complete intersection over union,CIOU)损失函数,利用自建的黄斑病数据集,对YOLOv5s-GCE模型进行消融和对比实验,并将该模型部署在RK3588S人工智能开发板上进行测试。结果表明:相比于原始YOLOv5s模型,YOLOv5s-GCE模型的平均精度均值(mean average precision,mAP)为92.7%(提高2.7%),复杂度显著降低,参数量、权重大小和浮点运算量(giga floating-pointoperations per second,GFLOPs)分别降低44.7%、43.4%和47.2%;YOLOv5s-GCE模型的整体性能优于SSD、YOLOv7、YOLOv8n和Faster R-CNN典型的目标检测模型。部署在RK3588S开发板上的YOLOv5s-GCE模型检测速度可达每秒30.49帧,mAP值为90.2%,可以满足糙皮侧耳黄斑病实时检测需求,研究结果为后续研发食用菌病害智能检测装置提供参考。展开更多
基金the National Natural Science Foundation of China (31801541)the Independent Innovation Fund Project of Agricultural Science and Technology in Jiangsu Province (CX (22)3065)+1 种基金Major Scientific and Technological Achievements Transformation Project of Taizhou (SCG 202105)the Taizhou Science and Technology Support Plan (TN202106)。
文摘In order to improve the bioavailability of lutein(LUT),a novel lutein-stevio side nanoparticle(LUT-STE)were prepared previously,but the information about LUT-STE on protecting of eye health was limited.This study investigated the effect of LUT-STE on antioxidant activity of H_(2)O_(2)-induced human retinal pigment epithelial(ARPE)cells.LUT and LUT-STE(final concentration of 5μg/mL)significantly enhanced cell viability from(74.84±5.10)%to(81.92±10.01)%(LUT)and(89.33±4.34)%(LUT-STE),and inhibited the cell apoptosis(P<0.05).After pretreatment with LUT-STE in ARPE cells,the levels of superoxide dismutase(SOD),catalase(CAT)and glutathion peroxidase(GSH-Px)in ARPE cells were significantly increased(P<0.05),the contents of reactive oxygen species(ROS)and malondialdehyde(MDA)were decreased.In addition,the vascular endothelial growth factor(VEGF)levels were inhibited by 13.61%and 17.39%,respectively,pretreatment with LUT and LUT-STE.Western blotting results showed that the pretreatment with LUT-STE inhibited the expression of caspase-9 and caspase-3 and up-regulated Bcl-2/Bax pathway to inhibit H_(2)O_(2)-induced apoptosis.In summary,the novel delivery LUT-STE had more pronounced inhibitory effect on H_(2)O_(2)-induced damage in human ARPE cells.
文摘为准确高效检测糙皮侧耳(Pleurotus ostreatus)黄斑病,构建基于YOLOv5s的黄斑病检测模型YOLOv5s-GCE。该模型在YOLOv5s模型基础上引入轻量化GhostNet结构,将坐标注意力(coordinate attention,CA)模块嵌入到YOLOv5s主干网络中,并利用增强交并比(enhanced intersection over union,EIOU)损失函数替换原YOLOv5s网络的完整交并比(complete intersection over union,CIOU)损失函数,利用自建的黄斑病数据集,对YOLOv5s-GCE模型进行消融和对比实验,并将该模型部署在RK3588S人工智能开发板上进行测试。结果表明:相比于原始YOLOv5s模型,YOLOv5s-GCE模型的平均精度均值(mean average precision,mAP)为92.7%(提高2.7%),复杂度显著降低,参数量、权重大小和浮点运算量(giga floating-pointoperations per second,GFLOPs)分别降低44.7%、43.4%和47.2%;YOLOv5s-GCE模型的整体性能优于SSD、YOLOv7、YOLOv8n和Faster R-CNN典型的目标检测模型。部署在RK3588S开发板上的YOLOv5s-GCE模型检测速度可达每秒30.49帧,mAP值为90.2%,可以满足糙皮侧耳黄斑病实时检测需求,研究结果为后续研发食用菌病害智能检测装置提供参考。
