Continuum discretised coupled-channels （CDCC） method with a 10Be（0＋） ＋ n two-body cluster model is applied to systematically analyze the elastic scattering of the halo nucleus alBe from the proton target at vari...Continuum discretised coupled-channels （CDCC） method with a 10Be（0＋） ＋ n two-body cluster model is applied to systematically analyze the elastic scattering of the halo nucleus alBe from the proton target at various incident energies below 100 MeV/nucleon. Using the renormalized 10Be-p potential deduced from the 10Be ＋ p elastic scattering data, the differential cross sections of 11 Be ＋ p scattering are well reproduced by the CDCC calculations without any further adjustment parameters, demonstrating the applicability of this approach for describing the scattering of exotic nuclei based on the scattering of the less exotic core nuclei.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
Background The clinical manifestations of nonclassical 11beta-hydroxylase deficiency are very similar to those of nonclassical 21-hydroxylase deficiency.For this study,we investigated the relationship between the clin...Background The clinical manifestations of nonclassical 11beta-hydroxylase deficiency are very similar to those of nonclassical 21-hydroxylase deficiency.For this study,we investigated the relationship between the clinical and molecular features of congenital adrenal hyperplasia caused by 11beta-hydroxylase deficiency and reviewed the related literature,which are expected to provide assistance for the clinical diagnosis and analysis of congenital adrenal hyperplasia.Methods Clinical data for 10 Chinese patients diagnosed with congenital adrenal hyperplasia in our hospital from 2018 to 2022 were retrospectively analyzed.We examined the effects of gene mutations on protease activity and constructed threedimensional structure prediction models of proteins.Results We describe 10 patients with 11beta-hydroxylase gene mutations(n=5,46,XY;n=5,46,XX),with 10 novel mutations were reported.Female patients received treatment at an early stage,with an average age of 2.08±1.66 years,whereas male patients received treatment significantly later,at an average age of 9.77±3.62 years.The most common CYP11B1 pathogenic variant in the Chinese population was found to be c.1360C>T.All mutations lead to spatial conformational changes that affect protein stability.Conclusions Our study found that there was no significant correlation between each specific mutation and the severity of clinical manifestations.Different patients with the same gene pathogenic variant may have mild or severe clinical manifestations.The correlation between genotype and phenotype needs further study.Three-dimensional protein simulations may provide additional support for the physiopathological mechanism of genetic mutations.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the National Basic Research Program of China(Grant No.2013CB834402)the National Natural Science Foundation of China(Grant Nos.11275001,10905002,11275011,11275018,and 11035001)China Postdoctoral Science Foundation(Grant No.20100470133)
文摘Continuum discretised coupled-channels （CDCC） method with a 10Be（0＋） ＋ n two-body cluster model is applied to systematically analyze the elastic scattering of the halo nucleus alBe from the proton target at various incident energies below 100 MeV/nucleon. Using the renormalized 10Be-p potential deduced from the 10Be ＋ p elastic scattering data, the differential cross sections of 11 Be ＋ p scattering are well reproduced by the CDCC calculations without any further adjustment parameters, demonstrating the applicability of this approach for describing the scattering of exotic nuclei based on the scattering of the less exotic core nuclei.
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Background The clinical manifestations of nonclassical 11beta-hydroxylase deficiency are very similar to those of nonclassical 21-hydroxylase deficiency.For this study,we investigated the relationship between the clinical and molecular features of congenital adrenal hyperplasia caused by 11beta-hydroxylase deficiency and reviewed the related literature,which are expected to provide assistance for the clinical diagnosis and analysis of congenital adrenal hyperplasia.Methods Clinical data for 10 Chinese patients diagnosed with congenital adrenal hyperplasia in our hospital from 2018 to 2022 were retrospectively analyzed.We examined the effects of gene mutations on protease activity and constructed threedimensional structure prediction models of proteins.Results We describe 10 patients with 11beta-hydroxylase gene mutations(n=5,46,XY;n=5,46,XX),with 10 novel mutations were reported.Female patients received treatment at an early stage,with an average age of 2.08±1.66 years,whereas male patients received treatment significantly later,at an average age of 9.77±3.62 years.The most common CYP11B1 pathogenic variant in the Chinese population was found to be c.1360C>T.All mutations lead to spatial conformational changes that affect protein stability.Conclusions Our study found that there was no significant correlation between each specific mutation and the severity of clinical manifestations.Different patients with the same gene pathogenic variant may have mild or severe clinical manifestations.The correlation between genotype and phenotype needs further study.Three-dimensional protein simulations may provide additional support for the physiopathological mechanism of genetic mutations.
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。
