Background:The compound Luteolin-7-rutinoside(L7R)is a flavone derivative of luteolin,predominantly identified in plant species belonging to the families Asteraceae.Conversely,Myristic acid is characterized by its str...Background:The compound Luteolin-7-rutinoside(L7R)is a flavone derivative of luteolin,predominantly identified in plant species belonging to the families Asteraceae.Conversely,Myristic acid is characterized by its structure as a 14-carbon,unsaturated fatty acid.In this investigation,we endeavor to elucidate the putative mechanisms underlying the therapeutic effects of Myristic Acid and Luteolin 7-rutinoside in the context of oral cancer treatment,employing network pharmacology coupled with molecular docking methodologies.Methods:The protein targets of Myristic Acid and Luteolin 7-rutinoside were identified through a search on the Swiss Target Database.Subsequently,a compound-target network was constructed using Cytoscape 3.9.1.Targets associated with OC were retrieved from the OMIM and GeneCards databases.The overlap between compound targets and OC-related targets was determined,and the resulting shared targets were subjected to protein-protein interaction(PPI)network analysis using the STRING database.Additionally,gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were conducted on the identified targets.Molecular docking were performed to investigate the interactions between the core target and the active compound.Results:The component target network comprises 103 nodes and 102 edges.Among the proteins in the protein-protein interaction(PPI)network,those with higher degrees are TNF,PPARG,and TP53.Analysis through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways indicates that the treatment of OC with Myristic Acid and Luteolin 7-rutinoside primarily involves the regulation of miRNA transcription and inflammatory response.The identified signaling pathways include Pathways in cancer,PPAR signaling pathway,EGFR signaling pathway,and TNF signaling pathway.Molecular docking studies reveal that Luteolin 7-rutinoside and Myristic acid exhibit higher affinity towards TNF,PPARG,TP53,and EGFR.Conclusion:This study reveals the potential molecular mechanism of Myristic Acid and Luteolin 7-rutinoside in the treatment of oral cancer,and provides a reference for subsequent basic research.展开更多
棉田虫害的快速检测与准确识别是预防棉田虫害、提高棉花品质的重要前提。针对真实棉田环境下昆虫相似度高、背景干扰严重的问题,该研究提出一种ECSF-YOLOv7棉田虫害检测模型。首先,采用EfficientFormerV2作为特征提取网络,以加强网络...棉田虫害的快速检测与准确识别是预防棉田虫害、提高棉花品质的重要前提。针对真实棉田环境下昆虫相似度高、背景干扰严重的问题,该研究提出一种ECSF-YOLOv7棉田虫害检测模型。首先,采用EfficientFormerV2作为特征提取网络,以加强网络的特征提取能力并减少模型参数量;同时,将卷积注意力模块(convolution block attention module,CBAM)嵌入到模型的主干输出端,以增强模型对小目标的特征提取能力并削弱背景干扰;其次,使用GSConv卷积搭建Slim-Neck颈部网络结构,在减少模型参数量的同时保持模型的识别精度;最后,采用Focal-EIOU(focal and efficient IOU loss,Focal-EIOU)作为边界框回归损失函数,加速网络收敛并提高模型的检测准确率。结果表明,改进的ECSF-YOLOv7模型在棉田虫害测试集上的平均精度均值(mean average precision,mAP)为95.71%,检测速度为69.47帧/s。与主流的目标检测模型YOLOv7、SSD、YOLOv5l和YOLOX-m相比,ECSF-YOLOv7模型的mAP分别高出1.43、9.08、1.94、1.52个百分点,并且改进模型具有参数量更小、检测速度更快的优势,可为棉田虫害快速准确检测提供技术支持。展开更多
针对遥感图像中小目标数量众多且背景复杂所导致的识别精度低的问题,提出了一种改进的遥感图像小目标检测方法。该方法基于改进的YOLOv7网络模型,将双级路由注意力机制加入至下采样阶段以构建针对小目标的特征提取模块MP-ATT(max poolin...针对遥感图像中小目标数量众多且背景复杂所导致的识别精度低的问题,提出了一种改进的遥感图像小目标检测方法。该方法基于改进的YOLOv7网络模型,将双级路由注意力机制加入至下采样阶段以构建针对小目标的特征提取模块MP-ATT(max pooling-attention),使得模型更加关注小目标的特征,提高小目标检测精度。为了加强对小目标的细节感知能力,使用DCNv3(deformable convolution network v3)替换骨干网络中的二维卷积,以此构建新的层聚合模块ELAN-D。为网络设计新的小目标检测层以获取更精细的特征信息,从而提升模型的鲁棒性。同时使用MPDIoU(minimum point distance based IoU)替换原模型中的CIoU来优化损失函数,以适应遥感图像的尺度变化。实验表明,所提出的方法在DOTA-v1.0数据集上取得了良好效果,准确率、召回率和平均准确率(mean average precision,mAP)相比原模型分别提升了0.4、4.0、2.3个百分点,证明了该方法能够有效提升遥感图像中小目标的检测效果。展开更多
文摘Background:The compound Luteolin-7-rutinoside(L7R)is a flavone derivative of luteolin,predominantly identified in plant species belonging to the families Asteraceae.Conversely,Myristic acid is characterized by its structure as a 14-carbon,unsaturated fatty acid.In this investigation,we endeavor to elucidate the putative mechanisms underlying the therapeutic effects of Myristic Acid and Luteolin 7-rutinoside in the context of oral cancer treatment,employing network pharmacology coupled with molecular docking methodologies.Methods:The protein targets of Myristic Acid and Luteolin 7-rutinoside were identified through a search on the Swiss Target Database.Subsequently,a compound-target network was constructed using Cytoscape 3.9.1.Targets associated with OC were retrieved from the OMIM and GeneCards databases.The overlap between compound targets and OC-related targets was determined,and the resulting shared targets were subjected to protein-protein interaction(PPI)network analysis using the STRING database.Additionally,gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were conducted on the identified targets.Molecular docking were performed to investigate the interactions between the core target and the active compound.Results:The component target network comprises 103 nodes and 102 edges.Among the proteins in the protein-protein interaction(PPI)network,those with higher degrees are TNF,PPARG,and TP53.Analysis through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways indicates that the treatment of OC with Myristic Acid and Luteolin 7-rutinoside primarily involves the regulation of miRNA transcription and inflammatory response.The identified signaling pathways include Pathways in cancer,PPAR signaling pathway,EGFR signaling pathway,and TNF signaling pathway.Molecular docking studies reveal that Luteolin 7-rutinoside and Myristic acid exhibit higher affinity towards TNF,PPARG,TP53,and EGFR.Conclusion:This study reveals the potential molecular mechanism of Myristic Acid and Luteolin 7-rutinoside in the treatment of oral cancer,and provides a reference for subsequent basic research.
文摘棉田虫害的快速检测与准确识别是预防棉田虫害、提高棉花品质的重要前提。针对真实棉田环境下昆虫相似度高、背景干扰严重的问题,该研究提出一种ECSF-YOLOv7棉田虫害检测模型。首先,采用EfficientFormerV2作为特征提取网络,以加强网络的特征提取能力并减少模型参数量;同时,将卷积注意力模块(convolution block attention module,CBAM)嵌入到模型的主干输出端,以增强模型对小目标的特征提取能力并削弱背景干扰;其次,使用GSConv卷积搭建Slim-Neck颈部网络结构,在减少模型参数量的同时保持模型的识别精度;最后,采用Focal-EIOU(focal and efficient IOU loss,Focal-EIOU)作为边界框回归损失函数,加速网络收敛并提高模型的检测准确率。结果表明,改进的ECSF-YOLOv7模型在棉田虫害测试集上的平均精度均值(mean average precision,mAP)为95.71%,检测速度为69.47帧/s。与主流的目标检测模型YOLOv7、SSD、YOLOv5l和YOLOX-m相比,ECSF-YOLOv7模型的mAP分别高出1.43、9.08、1.94、1.52个百分点,并且改进模型具有参数量更小、检测速度更快的优势,可为棉田虫害快速准确检测提供技术支持。
文摘针对遥感图像中小目标数量众多且背景复杂所导致的识别精度低的问题,提出了一种改进的遥感图像小目标检测方法。该方法基于改进的YOLOv7网络模型,将双级路由注意力机制加入至下采样阶段以构建针对小目标的特征提取模块MP-ATT(max pooling-attention),使得模型更加关注小目标的特征,提高小目标检测精度。为了加强对小目标的细节感知能力,使用DCNv3(deformable convolution network v3)替换骨干网络中的二维卷积,以此构建新的层聚合模块ELAN-D。为网络设计新的小目标检测层以获取更精细的特征信息,从而提升模型的鲁棒性。同时使用MPDIoU(minimum point distance based IoU)替换原模型中的CIoU来优化损失函数,以适应遥感图像的尺度变化。实验表明,所提出的方法在DOTA-v1.0数据集上取得了良好效果,准确率、召回率和平均准确率(mean average precision,mAP)相比原模型分别提升了0.4、4.0、2.3个百分点,证明了该方法能够有效提升遥感图像中小目标的检测效果。
