食源性蛋白淀粉样纤维化聚集具有独特的结构特性,蚕豆11S蛋白(fava bean 11S protein,FP)作为一种可持续蛋白资源,表现出巨大的潜力。该研究探究了蚕豆11S蛋白淀粉样纤维化聚集(fibrotic aggregation of 11S protein in fava bean,FPF)...食源性蛋白淀粉样纤维化聚集具有独特的结构特性,蚕豆11S蛋白(fava bean 11S protein,FP)作为一种可持续蛋白资源,表现出巨大的潜力。该研究探究了蚕豆11S蛋白淀粉样纤维化聚集(fibrotic aggregation of 11S protein in fava bean,FPF)在形成过程中的动态演变,包括其结构表征和功能特性。6 g/100 mL的FP通过酸热处理(pH 2,85℃)不同时间(0~24 h)后得到FPF。处理后的样品通过硫黄素T、荧光、二酪氨酸、透射电子显微镜、傅里叶红外光谱等进行结构表征,结果表明FP先在酸热过程中水解成多肽,再自组装成富含β-折叠结构的FPF(由0 h的34.44%增加到24 h的45.89%)。通过起泡性、乳化性和凝胶特性等对FPF功能特性进行表征,与FP相比,反应24 h后的FPF具有更好的起泡性、乳化性和凝胶特性。此外,FPF在体外细胞实验中没有表现出细胞毒性。研究结果为FPF的形成规律提供了理论支撑。展开更多
Progressive photoreceptor cell death is one of the main pathological features of age-related macular degeneration and eventually leads to vision loss.Ferroptosis has been demonstrated to be associated with retinal deg...Progressive photoreceptor cell death is one of the main pathological features of age-related macular degeneration and eventually leads to vision loss.Ferroptosis has been demonstrated to be associated with retinal degenerative diseases.However,the molecular mechanisms underlying ferroptosis and photoreceptor cell death in age-related macular degeneration remain largely unexplored.Bioinformatics and biochemical analyses in this study revealed xC^(–),solute carrier family 7 member 11-regulated ferroptosis as the predominant pathological process of photoreceptor cell degeneration in a light-induced dry age-related macular degeneration mouse model.This process involves the nuclear factor-erythroid factor 2-related factor 2-solute carrier family 7 member 11-glutathione peroxidase 4 signaling pathway,through which cystine depletion,iron ion accumulation,and enhanced lipid peroxidation ultimately lead to photoreceptor cell death and subsequent visual function impairment.We demonstrated that solute carrier family 7 member 11 overexpression blocked this process by inhibiting oxidative stress in vitro and in vivo.Conversely,solute carrier family 7 member 11 knockdown or the solute carrier family 7 member 11 inhibitor sulfasalazine and ferroptosis-inducing agent erastin aggravated H_(2)O_(2)-induced ferroptosis of 661W cells.These findings indicate solute carrier family 7 member 11 may be a potential therapeutic target for patients with retinal degenerative diseases including age-related macular degeneration.展开更多
In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes ...In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes an enhanced pavement crack detection model named Star-YOLO11.This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network.The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency.To enhance the accuracy of pavement crack detection and improve model efficiency,three key modifications to the YOLO11 architecture are proposed.Firstly,the original C3k2 backbone is replaced with a StarBlock-based structure,forming the Star-s50 feature extraction backbone network.This lightweight redesign reduces computational complexity while maintaining detection precision.Secondly,to address the inefficiency of the original Partial Self-attention(PSA)mechanism in capturing localized crack features,the convolutional prior-aware Channel Prior Convolutional Attention(CPCA)mechanism is integrated into the channel dimension,creating a hybrid CPC-C2PSA attention structure.Thirdly,the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network(SMAFPN)based on the Multi-Branch Auxiliary Feature Pyramid Network architecture,which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks.Additionally,a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed.This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation.Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9%(3.5%higher than the baseline),a mean average precision(mAP)of 90.3%(+2.6%),and an F1-score of 85.8%(+0.5%),while reducing the model size by 18.8%and reaching a frame rate of 225.73 frames per second(FPS)for real-time detection.It shows potential for lightweight deployment in pavement crack detection tasks.展开更多
As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a no...As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.展开更多
In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in...In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.展开更多
针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络...针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络,检测潜在动态区域,并结合Lucas-Kanade(LK)光流法识别其中的动态特征点,从而在剔除动态特征点的同时保留静态特征点,提高特征点利用率和位姿估计精度。此外,新增语义地图构建线程,通过去除YOLO11n识别到的动态物体点云,并融合前端提取的语义信息,实现静态语义地图的构建。在TUM数据集上的实验结果表明,相较于ORB-SLAM3,该算法在高动态序列数据集中的定位精度提升了95.02%,验证了该算法在动态环境下的有效性,能显著提升视觉SLAM系统的定位精度和地图构建质量。展开更多
根肿病和草害严重威胁油菜的产量和品质。为选育抗根肿病(clubroot-resistant,CR)和抗除草剂(herbicide-resistant,HR)的油菜品种,通过分子标记辅助选择聚合育种策略将抗根肿病位点CRb和PbBa8.1、抗除草剂位点ALS1R和ALS3R导入油菜常规...根肿病和草害严重威胁油菜的产量和品质。为选育抗根肿病(clubroot-resistant,CR)和抗除草剂(herbicide-resistant,HR)的油菜品种,通过分子标记辅助选择聚合育种策略将抗根肿病位点CRb和PbBa8.1、抗除草剂位点ALS1R和ALS3R导入油菜常规品种中双11(ZS11)中,获得3个改良株系ZS11CR(CRb+PbBa8.1)、ZS11HR(ALS1R+ALS3R)和ZS11CHR(CRb+PbBa8.1+ALS1R+ALS3R)。利用根肿菌4号生理小种(湖北枝江)和噻吩磺隆除草剂(45 g a.i.ha^(-1))对ZS11CR、ZS11HR和ZS11CHR的抗性进行评价,结果表明:ZS11CR、ZS11CHR对4号生理小种抗性达到免疫水平,ZS11HR、ZS11CHR对噻吩磺隆除草剂抗性显著。田间农艺性状调查结果表明,ZS11CR、ZS11HR和ZS11CHR的株高较ZS11一定程度增加,而在开花期、分枝数、主花序角果数、角果长、每角果粒数、千粒重等性状上没有显著差异。本研究获得了3个改良株系,其中ZS11CR具有根肿病抗性、ZS11HR具有除草剂抗性、ZS11CHR兼具根肿病抗性和除草剂抗性,这些改良株系不仅目标性状得到了改良,同时维持了ZS11的优良农艺性状,具有一定的应用潜力。展开更多
基金supported by the National Natural Science Foundation of China,Nos.82171076(to XS)and U22A20311(to XS),82101168(to TL)Shanghai Science and technology Innovation Action Plan,No.23Y11901300(to JS)+1 种基金Science and Technology Commission of Shanghai Municipality,No.21ZR1451500(to TL)Shanghai Pujiang Program,No.22PJ1412200(to BY)。
文摘Progressive photoreceptor cell death is one of the main pathological features of age-related macular degeneration and eventually leads to vision loss.Ferroptosis has been demonstrated to be associated with retinal degenerative diseases.However,the molecular mechanisms underlying ferroptosis and photoreceptor cell death in age-related macular degeneration remain largely unexplored.Bioinformatics and biochemical analyses in this study revealed xC^(–),solute carrier family 7 member 11-regulated ferroptosis as the predominant pathological process of photoreceptor cell degeneration in a light-induced dry age-related macular degeneration mouse model.This process involves the nuclear factor-erythroid factor 2-related factor 2-solute carrier family 7 member 11-glutathione peroxidase 4 signaling pathway,through which cystine depletion,iron ion accumulation,and enhanced lipid peroxidation ultimately lead to photoreceptor cell death and subsequent visual function impairment.We demonstrated that solute carrier family 7 member 11 overexpression blocked this process by inhibiting oxidative stress in vitro and in vivo.Conversely,solute carrier family 7 member 11 knockdown or the solute carrier family 7 member 11 inhibitor sulfasalazine and ferroptosis-inducing agent erastin aggravated H_(2)O_(2)-induced ferroptosis of 661W cells.These findings indicate solute carrier family 7 member 11 may be a potential therapeutic target for patients with retinal degenerative diseases including age-related macular degeneration.
基金funded by the Jiangxi SASAC Science and Technology Innovation Special Project and the Key Technology Research and Application Promotion of Highway Overload Digital Solution.
文摘In response to the challenges in highway pavement distress detection,such as multiple defect categories,difficulties in feature extraction for different damage types,and slow identification speeds,this paper proposes an enhanced pavement crack detection model named Star-YOLO11.This improved algorithm modifies the YOLO11 architecture by substituting the original C3k2 backbone network with a Star-s50 feature extraction network.The enhanced structure adjusts the number of stacked layers in the StarBlock module to optimize detection accuracy and improve model efficiency.To enhance the accuracy of pavement crack detection and improve model efficiency,three key modifications to the YOLO11 architecture are proposed.Firstly,the original C3k2 backbone is replaced with a StarBlock-based structure,forming the Star-s50 feature extraction backbone network.This lightweight redesign reduces computational complexity while maintaining detection precision.Secondly,to address the inefficiency of the original Partial Self-attention(PSA)mechanism in capturing localized crack features,the convolutional prior-aware Channel Prior Convolutional Attention(CPCA)mechanism is integrated into the channel dimension,creating a hybrid CPC-C2PSA attention structure.Thirdly,the original neck structure is upgraded to a Star Multi-Branch Auxiliary Feature Pyramid Network(SMAFPN)based on the Multi-Branch Auxiliary Feature Pyramid Network architecture,which adaptively fuses high-level semantic and low-level spatial information through Star-s50 connections and C3k2 extraction blocks.Additionally,a composite dataset augmentation strategy combining traditional and advanced augmentation techniques is developed.This strategy is validated on a specialized pavement dataset containing five distinct crack categories for comprehensive training and evaluation.Experimental results indicate that the proposed Star-YOLO11 achieves an accuracy of 89.9%(3.5%higher than the baseline),a mean average precision(mAP)of 90.3%(+2.6%),and an F1-score of 85.8%(+0.5%),while reducing the model size by 18.8%and reaching a frame rate of 225.73 frames per second(FPS)for real-time detection.It shows potential for lightweight deployment in pavement crack detection tasks.
基金funded by the Open Access Initiative of the University of Bremen and the DFG via SuUB BremenThe authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP2/367/46)+1 种基金This research is supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘As urban landscapes evolve and vehicular volumes soar,traditional traffic monitoring systems struggle to scale,often failing under the complexities of dense,dynamic,and occluded environments.This paper introduces a novel,unified deep learning framework for vehicle detection,tracking,counting,and classification in aerial imagery designed explicitly for modern smart city infrastructure demands.Our approach begins with adaptive histogram equalization to optimize aerial image clarity,followed by a cutting-edge scene parsing technique using Mask2Former,enabling robust segmentation even in visually congested settings.Vehicle detection leverages the latest YOLOv11 architecture,delivering superior accuracy in aerial contexts by addressing occlusion,scale variance,and fine-grained object differentiation.We incorporate the highly efficient ByteTrack algorithm for tracking,enabling seamless identity preservation across frames.Vehicle counting is achieved through an unsupervised DBSCAN-based method,ensuring adaptability to varying traffic densities.We further introduce a hybrid feature extraction module combining Convolutional Neural Networks(CNNs)with Zernike Moments,capturing both deep semantic and geometric signatures of vehicles.The final classification is powered by NASNet,a neural architecture search-optimized model,ensuring high accuracy across diverse vehicle types and orientations.Extensive evaluations of the VAID benchmark dataset demonstrate the system’s outstanding performance,achieving 96%detection,94%tracking,and 96.4%classification accuracy.On the UAVDT dataset,the system attains 95%detection,93%tracking,and 95%classification accuracy,confirming its robustness across diverse aerial traffic scenarios.These results establish new benchmarks in aerial traffic analysis and validate the framework’s scalability,making it a powerful and adaptable solution for next-generation intelligent transportation systems and urban surveillance.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia,Grant No.KFU250098.
文摘In this study,an automated multimodal system for detecting,classifying,and dating fruit was developed using a two-stage YOLOv11 pipeline.In the first stage,the YOLOv11 detection model locates individual date fruits in real time by drawing bounding boxes around them.These bounding boxes are subsequently passed to a YOLOv11 classification model,which analyzes cropped images and assigns class labels.An additional counting module automatically tallies the detected fruits,offering a near-instantaneous estimation of quantity.The experimental results suggest high precision and recall for detection,high classification accuracy(across 15 classes),and near-perfect counting in real time.This paper presents a multi-stage pipeline for date fruit detection,classification,and automated counting,employing YOLOv11-based models to achieve high accuracy while maintaining real-time throughput.The results demonstrated that the detection precision exceeded 90%,the classification accuracy approached 92%,and the counting module correlated closely with the manual tallies.These findings confirm the potential of reducing manual labour and enhancing operational efficiency in post-harvesting processes.Future studies will include dataset expansion,user-centric interfaces,and integration with harvesting robotics.
文摘针对动态场景导致视觉定位与建图(simultaneous localization and mapping,SLAM)算法位姿估计精度低和地图质量差等问题,提出一种结合深度学习的动态视觉SLAM算法。该算法在ORB-SLAM3前端引入轻量化且目标识别率高的YOLO11n目标检测网络,检测潜在动态区域,并结合Lucas-Kanade(LK)光流法识别其中的动态特征点,从而在剔除动态特征点的同时保留静态特征点,提高特征点利用率和位姿估计精度。此外,新增语义地图构建线程,通过去除YOLO11n识别到的动态物体点云,并融合前端提取的语义信息,实现静态语义地图的构建。在TUM数据集上的实验结果表明,相较于ORB-SLAM3,该算法在高动态序列数据集中的定位精度提升了95.02%,验证了该算法在动态环境下的有效性,能显著提升视觉SLAM系统的定位精度和地图构建质量。
文摘根肿病和草害严重威胁油菜的产量和品质。为选育抗根肿病(clubroot-resistant,CR)和抗除草剂(herbicide-resistant,HR)的油菜品种,通过分子标记辅助选择聚合育种策略将抗根肿病位点CRb和PbBa8.1、抗除草剂位点ALS1R和ALS3R导入油菜常规品种中双11(ZS11)中,获得3个改良株系ZS11CR(CRb+PbBa8.1)、ZS11HR(ALS1R+ALS3R)和ZS11CHR(CRb+PbBa8.1+ALS1R+ALS3R)。利用根肿菌4号生理小种(湖北枝江)和噻吩磺隆除草剂(45 g a.i.ha^(-1))对ZS11CR、ZS11HR和ZS11CHR的抗性进行评价,结果表明:ZS11CR、ZS11CHR对4号生理小种抗性达到免疫水平,ZS11HR、ZS11CHR对噻吩磺隆除草剂抗性显著。田间农艺性状调查结果表明,ZS11CR、ZS11HR和ZS11CHR的株高较ZS11一定程度增加,而在开花期、分枝数、主花序角果数、角果长、每角果粒数、千粒重等性状上没有显著差异。本研究获得了3个改良株系,其中ZS11CR具有根肿病抗性、ZS11HR具有除草剂抗性、ZS11CHR兼具根肿病抗性和除草剂抗性,这些改良株系不仅目标性状得到了改良,同时维持了ZS11的优良农艺性状,具有一定的应用潜力。
