Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspect...Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.展开更多
Mangosteen(Garcinia mangostana Linn.) is a well-known tropical tree indigenous to Southeast Asia. Its fruit's pericarp abounds with a class of isoprenylated xanthones which are referred as mangostins. Numerous in ...Mangosteen(Garcinia mangostana Linn.) is a well-known tropical tree indigenous to Southeast Asia. Its fruit's pericarp abounds with a class of isoprenylated xanthones which are referred as mangostins. Numerous in vitro and in vivo studies have shown that mangostins and their derivatives possess diverse pharmacological activities, such as antibacterial, antifungal, antimalarial, anticarcinogenic, antiatherogenic activities as well as neuroprotective properties in Alzheimer's disease(AD). This review article provides a comprehensive review of the pharmacological activities of mangostins and their derivatives to reveal their promising utilities in the treatment of certain important diseases, mainly focusing on the discussions of the underlying molecular targets/pathways, modes of action, and relevant structure-activity relationships(SARs). Meanwhile, the pharmacokinetics(PK) profile and recent toxicological studies of mangostins are also described for further druggability exploration in the future.展开更多
基金supported in part by the National Natural Science Foundation of China (Grant Nos.51975347 and 51907117)in part by the Shanghai Science and Technology Program (Grant No.22010501600).
文摘Regular fastener detection is necessary to ensure the safety of railways.However,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real railways.Existing supervised inspectionmethods have insufficient detection ability in cases of imbalanced samples.To solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state inspection.First,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener regions.Then,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal samples.Finally,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener dataset.Results show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener detection.Qualitative and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,respectively.In addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.
基金supported by the Leading Entrepreneurship Talents Program of Nanjing,China(No.2013B14007)the General Program of Social Development in the Plan of Key Research of Jiangsu Province(No.BE201568)
文摘Mangosteen(Garcinia mangostana Linn.) is a well-known tropical tree indigenous to Southeast Asia. Its fruit's pericarp abounds with a class of isoprenylated xanthones which are referred as mangostins. Numerous in vitro and in vivo studies have shown that mangostins and their derivatives possess diverse pharmacological activities, such as antibacterial, antifungal, antimalarial, anticarcinogenic, antiatherogenic activities as well as neuroprotective properties in Alzheimer's disease(AD). This review article provides a comprehensive review of the pharmacological activities of mangostins and their derivatives to reveal their promising utilities in the treatment of certain important diseases, mainly focusing on the discussions of the underlying molecular targets/pathways, modes of action, and relevant structure-activity relationships(SARs). Meanwhile, the pharmacokinetics(PK) profile and recent toxicological studies of mangostins are also described for further druggability exploration in the future.
