背景:沸石基咪唑盐框架8及其衍生物凭借优异的药物控释能力在组织工程领域展现出广泛的应用潜力。目的:综述沸石基咪唑盐框架8及其改性材料在活性氧生成与清除中的作用机制,探讨它们在抗肿瘤、抗菌及组织保护领域的应用潜力,分析未来发...背景:沸石基咪唑盐框架8及其衍生物凭借优异的药物控释能力在组织工程领域展现出广泛的应用潜力。目的:综述沸石基咪唑盐框架8及其改性材料在活性氧生成与清除中的作用机制,探讨它们在抗肿瘤、抗菌及组织保护领域的应用潜力,分析未来发展方向与挑战。方法:由第一作者通过中国知网、PubMed等数据库检索2000-2024年相关文献,中文检索关键词为“沸石基咪唑盐框架8,活性氧,抗菌,抗肿瘤,活性氧吸收,活性氧平衡,组织修复”,英文检索关键词为“ZIF-8,ROS,antibacterial,antitumor,ROS absorption,Balance of ROS,Tissue regeneration”,最终筛选69篇高质量文献进行综述分析。结果与结论:通过调控沸石基咪唑盐框架8及其改性材料的带隙结构、优化电子转移效率可显著提升光生载流子的分离与迁移效率,从而增强催化反应性能,提高活性氧的产生效率,实现更高效、更具靶向性的抗肿瘤及抗菌作用;同时,采用抗氧化酶系统或表面改性技术构建的活性氧清除装置,能够精准平衡多余活性氧,实现对细胞的有效保护。这种基于带隙调控与电子转移优化的双向调控机制,为动态管理活性氧生成与清除提供了重要策略,在抗肿瘤、抗菌及组织保护等领域展现出广阔的应用前景。展开更多
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt...Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.展开更多
In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds...In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.展开更多
目的探讨炎调方调过Fas/Caspase-8信号通路减轻脓毒症急性胃肠损伤小鼠炎症的机制。方法取70只BALB/c小鼠随机分为空白组、假手术组和造模小鼠组。通过盲肠结扎穿孔术(cecum ligation and puncture,CLP)构建脓毒症急性胃肠损伤小鼠模型...目的探讨炎调方调过Fas/Caspase-8信号通路减轻脓毒症急性胃肠损伤小鼠炎症的机制。方法取70只BALB/c小鼠随机分为空白组、假手术组和造模小鼠组。通过盲肠结扎穿孔术(cecum ligation and puncture,CLP)构建脓毒症急性胃肠损伤小鼠模型,将造模成功的小鼠随机分为模型组,炎调方低、中、高剂量组,ROCK抑制剂组。苏木素-伊红(HE)染色观察小鼠回肠组织病理学改变;ELISA法检测各组小鼠血清IL-17、IL-23水平;蛋白印迹法检测回肠组织Fas/Caspase-8信号通路蛋白Fas、FADD和Caspase-8的相对表达;TUNEL染色法检测回肠组织细胞凋亡情况。结果与空白组相比,模型组小鼠回肠组织肠黏膜萎缩明显、绒毛排列杂乱,可见断裂、脱落,上皮细胞细胞坏死脱落,炎症细胞浸润明显,小鼠血清中IL-17、IL-23水平升高(P<0.05),回肠组织中Fas、FADD和Caspase-8蛋白的表达升高(P<0.05),肠上皮细胞呈现明显的凋亡现象(P<0.05)。与模型组相比,炎调方组小鼠的回肠组织病理学改变均得到不同程度的改善,血清中IL-17、IL-23水平降低(P<0.05),且回肠组织中Fas、FADD和Caspase-8蛋白的表达降低(P<0.05),肠上皮细胞凋亡减少(P<0.05)。结论炎调方可以减轻肠黏膜组织损伤和肠道组织炎症反应,可能是通过调控Fas/Caspase-8信号通路抑制脓毒症急性胃肠损伤小鼠的肠上皮细胞凋亡来发挥作用的。展开更多
文摘背景:沸石基咪唑盐框架8及其衍生物凭借优异的药物控释能力在组织工程领域展现出广泛的应用潜力。目的:综述沸石基咪唑盐框架8及其改性材料在活性氧生成与清除中的作用机制,探讨它们在抗肿瘤、抗菌及组织保护领域的应用潜力,分析未来发展方向与挑战。方法:由第一作者通过中国知网、PubMed等数据库检索2000-2024年相关文献,中文检索关键词为“沸石基咪唑盐框架8,活性氧,抗菌,抗肿瘤,活性氧吸收,活性氧平衡,组织修复”,英文检索关键词为“ZIF-8,ROS,antibacterial,antitumor,ROS absorption,Balance of ROS,Tissue regeneration”,最终筛选69篇高质量文献进行综述分析。结果与结论:通过调控沸石基咪唑盐框架8及其改性材料的带隙结构、优化电子转移效率可显著提升光生载流子的分离与迁移效率,从而增强催化反应性能,提高活性氧的产生效率,实现更高效、更具靶向性的抗肿瘤及抗菌作用;同时,采用抗氧化酶系统或表面改性技术构建的活性氧清除装置,能够精准平衡多余活性氧,实现对细胞的有效保护。这种基于带隙调控与电子转移优化的双向调控机制,为动态管理活性氧生成与清除提供了重要策略,在抗肿瘤、抗菌及组织保护等领域展现出广阔的应用前景。
基金funded by Key research and development Program of Henan Province(No.251111211200)National Natural Science Foundation of China(Grant No.U2004163).
文摘Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy.
基金funded by the Joint Funds of the National Natural Science Foundation of China(U2341223)the Beijing Municipal Natural Science Foundation(No.4232067).
文摘In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection.
文摘目的探讨炎调方调过Fas/Caspase-8信号通路减轻脓毒症急性胃肠损伤小鼠炎症的机制。方法取70只BALB/c小鼠随机分为空白组、假手术组和造模小鼠组。通过盲肠结扎穿孔术(cecum ligation and puncture,CLP)构建脓毒症急性胃肠损伤小鼠模型,将造模成功的小鼠随机分为模型组,炎调方低、中、高剂量组,ROCK抑制剂组。苏木素-伊红(HE)染色观察小鼠回肠组织病理学改变;ELISA法检测各组小鼠血清IL-17、IL-23水平;蛋白印迹法检测回肠组织Fas/Caspase-8信号通路蛋白Fas、FADD和Caspase-8的相对表达;TUNEL染色法检测回肠组织细胞凋亡情况。结果与空白组相比,模型组小鼠回肠组织肠黏膜萎缩明显、绒毛排列杂乱,可见断裂、脱落,上皮细胞细胞坏死脱落,炎症细胞浸润明显,小鼠血清中IL-17、IL-23水平升高(P<0.05),回肠组织中Fas、FADD和Caspase-8蛋白的表达升高(P<0.05),肠上皮细胞呈现明显的凋亡现象(P<0.05)。与模型组相比,炎调方组小鼠的回肠组织病理学改变均得到不同程度的改善,血清中IL-17、IL-23水平降低(P<0.05),且回肠组织中Fas、FADD和Caspase-8蛋白的表达降低(P<0.05),肠上皮细胞凋亡减少(P<0.05)。结论炎调方可以减轻肠黏膜组织损伤和肠道组织炎症反应,可能是通过调控Fas/Caspase-8信号通路抑制脓毒症急性胃肠损伤小鼠的肠上皮细胞凋亡来发挥作用的。
