Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of...Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.展开更多
Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this stud...Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this study,both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery.The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR-NDVI image,whereas the coarse segmentation relied on the segment anything model(SAM).Both methods successfully delineated tree crown outlines,however,only the fine segmentation accurately captured internal canopy gaps.Despite these structural differences,mean NDVI values calculated per tree crown revealed no significant differences between the two approaches,indicating that coarse segmentation is sufficient for mean vegetation index assessments.Nevertheless,the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees,underscoring its value for detailed structural and health analyses.Furthermore,the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys.For applications focused on structural integrity and spatial variation in canopy health,the fine segmentation approach is recommended.展开更多
AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigat...AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigational laser equipment.METHODS:A dataset with dual labels(point-level and pixel-level)was first established based on fundus fluorescein angiography(FFA)images of CSC and subsequently divided into training(102 images),validation(40 images),and test(40 images)datasets.An intelligent segmentation method was then developed,based on the You Only Look Once version 8 Pose Estimation(YOLOv8-Pose)model and segment anything model(SAM),to segment CSC leakage points.Next,the YOLOv8-Pose model was trained for 200 epochs,and the best-performing model was selected to form the optimal combination with SAM.Additionally,the classic five types of U-Net series models[i.e.,U-Net,recurrent residual U-Net(R2U-Net),attention U-Net(AttU-Net),recurrent residual attention U-Net(R2AttUNet),and nested U-Net(UNet^(++))]were initialized with three random seeds and trained for 200 epochs,resulting in a total of 15 baseline models for comparison.Finally,based on the metrics including Dice similarity coefficient(DICE),intersection over union(IoU),precision,recall,precisionrecall(PR)curve,and receiver operating characteristic(ROC)curve,the proposed method was compared with baseline models through quantitative and qualitative experiments for leakage point segmentation,thereby demonstrating its effectiveness.RESULTS:With the increase of training epochs,the mAP50-95,Recall,and precision of the YOLOv8-Pose model showed a significant increase and tended to stabilize,and it achieved a preliminary localization success rate of 90%(i.e.,36 images)for CSC leakage points in 40 test images.Using manually expert-annotated pixel-level labels as the ground truth,the proposed method achieved outcomes with a DICE of 57.13%,an IoU of 45.31%,a precision of 45.91%,a recall of 93.57%,an area under the PR curve(AUC-PR)of 0.78 and an area under the ROC curve(AUC-ROC)of 0.97,which enables more accurate segmentation of CSC leakage points.CONCLUSION:By combining the precise localization capability of the YOLOv8-Pose model with the robust and flexible segmentation ability of SAM,the proposed method not only demonstrates the effectiveness of the YOLOv8-Pose model in detecting keypoint coordinates of CSC leakage points from the perspective of application innovation but also establishes a novel approach for accurate segmentation of CSC leakage points through the“detect-then-segment”strategy,thereby providing a potential auxiliary means for the automatic and precise realtime localization of leakage points during traditional laser photocoagulation for CSC.展开更多
Background:Laparoscopic anatomic hepatectomy of segment 7(LAH-S7)is a challenging surgery.In this study we aimed to investigate surgical and oncological outcomes of various approaches of LAH-S7 in patients with hepato...Background:Laparoscopic anatomic hepatectomy of segment 7(LAH-S7)is a challenging surgery.In this study we aimed to investigate surgical and oncological outcomes of various approaches of LAH-S7 in patients with hepatocellular carcinoma(HCC).A particular focus was placed on identifying the Glissonean pedicle of segment 7(G7)and the intersegmental plane.Given the scarcity of comprehensive reviews or comparative studies on clinical outcomes,we also sought to analyze the experiences and advantages associated with different approaches in relation to the anatomic variations of G7.Methods:The clinical data of 124 patients who underwent LAH-S7 for HCC across seven tertiary referral medical centers in China were retrospectively analyzed.Three surgical approaches were categorized based on the procedures used for G7 identification:the indocyanine green(ICG)fluorescence positive staining approach(IFPA),the Glissonean approach(GA),and the hepatic vein-guided approach(HVGA).Subsequently,the postoperative short-term results and oncological outcomes of the three different approaches were compared.Results:The distribution of surgical approaches among the patients was as follows:IFPA in 16(12.9%),GA in 62(50.0%),and HVGA in 46(37.1%)patients.Complications were observed in 27(21.8%)patients.The 1-,3-,and 5-year overall survival(OS)rates were 99.1%,89.2%,and 84.7%,respectively.The 1-,3-,and 5-year recurrence-free survival(RFS)rates were 99.0%,84.7%,and 69.3%,respectively.The OS and RFS rates were comparable across the three approaches.Conclusions:Following a standardized surgical procedure,LAH-S7 is demonstrated to be safe and yields favorable oncological outcomes.Surgeons performing LAH-S7 should select the appropriate surgical approach based on the anatomical characteristics and variations of G7.展开更多
Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimo...Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimodal sensor fusion,often struggle with noisy data and demand high-performance GPUs,leading to sensor misalignment and performance degradation.This paper introduces an Enhanced Channel Attention BEV(ECABEV),a novel approach designed to address the challenges under insufficient GPU memory conditions.ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism,which utilizes global average and max pooling to effectively filter out noise while preserving discriminative features.Furthermore,an improved fusion approach is proposed to efficiently merge categorical data across modalities.To reduce computational overhead,a bilinear interpolation layer normalizationmethod is devised to ensure spatial feature fidelity.Moreover,a scalable crossentropy loss function is further designed to handle the imbalanced classes with less computational efficiency sacrifice.Extensive experiments on the nuScenes dataset demonstrate that ECABEV achieves state-of-the-art performance with an IoU of 39.961,using a lightweight ViT-B/14 backbone and lower resolution(224×224).Our approach highlights its cost-effectiveness and practical applicability,even on low-end devices.The code is publicly available at:https://github.com/YYF-CQU/ECABEV.git.展开更多
Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rel...Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.展开更多
The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic ...The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.展开更多
Drought is a major abiotic constraint to rice production in rainfed lowland and insufficiently irrigated areas. The improvement of drought tolerant varieties is one of the strategies to reduce the negative effects of ...Drought is a major abiotic constraint to rice production in rainfed lowland and insufficiently irrigated areas. The improvement of drought tolerant varieties is one of the strategies to reduce the negative effects of drought. Quantitative trait loci （QTLs） for primary and secondary traits related to drought tolerance （DT） on chromosomes 1, 3, 4, 8 and 9 that determined from double haploid lines derived from a cross between CT9993 and IR62266 were introgressed and dissected into small pieces in the genetic background of Khao Dawk Mall 105 （KDML105） to develop chromosome segment substitution line （CSSL） population. The CSSLs were evaluated at the reproductive stage for their agronomic performance and yield components under drought stress, and results were compared with irrigated condition. The flowering of CSSL lines was 6 to 7 d earlier than KDML105. The mean values of grain yields in the CSSLs were higher than KDML105 under drought and irrigated conditions. At irrigated condition, the grain yields of introgression lines carrying DT-QTLs from chromosomes 4 and 8 were higher than that of KDML105, whereas other traits showed little difference with KDML105. Analysis indicated that grain yield has positive correlation with plant height, tiller and panicle number per plant, and total grain weight per plant under drought stress while negatively correlated with days to flowering. As mentioned above, CSSLs showing good adaptation under drought stress can be used as genetic materials to improve drought tolerance in Thai rainfed lowland rice breeding program, and as materials to dissect genes underlying drought tolerance.展开更多
The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structur...The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structure in the upper crust and seismogenic tectonics in the Hefei segment of this fault, we collected phase velocity dispersion data of fundamental mode Rayleigh waves from ambient noise cross-correlation functions of ~400 temporal seismographs in an area of approximately 80 × 70 km along the fault zone. The period band of the dispersion data was ~0.5–10 s. We inverted for the upper crustal three-dimensional(3-D) shear velocity model with azimuthal anisotropy from the surface to 10 km depth by using a 3-D direct azimuthal anisotropy inversion method. The inversion result shows the spatial distribution characteristics of the tectonic units in the upper crust. Additionally, the deformation of the Tan-Lu Fault Zone and its conjugated fault systems could be inferred from the anisotropy model. In particular, the faults that have remained active from the early and middle Pleistocene control the anisotropic characteristics of the upper crustal structure in this area. The direction of fast axes near the fault zone area in the upper crust is consistent with the strike of the faults, whereas for the region far away from the fault zone, the direction of fast axes is consistent with the direction of the regional principal stress caused by plate movement. Combined with the azimuthal anisotropy models in the deep crust and uppermost mantle from the surface wave and Pn wave, the different anisotropic patterns caused by the Tan-Lu Fault Zone and its conjugated fault system nearby are shown in the upper and lower crust. Furthermore,by using the double-difference method, we relocated the Lujiang earthquake series, which contained 32 earthquakes with a depth shallower than 10 km. Both the Vs model and earthquake relocation results indicate that earthquakes mostly occurred in the vicinity of structural boundaries with fractured media, with high-level development of cracks and small-scale faults jammed between more rigid areas.展开更多
BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular ...BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular free wall rupture(FWR)occurs in approximately 2%of AMI patients and is notably rare in patients with non-STEMI.Types of cardiac rupture include left ventricular FWR,ventricular septal rupture,and papillary muscle rupture.The FWR usually leads to acute cardiac tamponade or electromechanical dissociation,where standard resuscitation efforts may not be effective.Ventricular septal rupture and papillary muscle rupture often result in refractory heart failure,with mortality rates over 50%,even with surgical or percutaneous repair options.CASE SUMMARY We present a rare case of an acute non-STEMI patient who suffered sudden FWR causing cardiac tamponade and loss of consciousness immediate before undergoing coronary angiography.Prompt resuscitation and emergency open-heart repair along with coronary artery bypass grafting resulted in successful patient recovery.CONCLUSION This case emphasizes the risks of AMI complications,shares a successful treatment scenario,and discusses measures to prevent such complications.展开更多
In panoramic images,the geometric distortion caused by wide-angle lenses makes traditional semantic segmentation methods difficult to accurately segment the glass areas.To address the challenges of capturing spatial f...In panoramic images,the geometric distortion caused by wide-angle lenses makes traditional semantic segmentation methods difficult to accurately segment the glass areas.To address the challenges of capturing spatial features and integrating context information,we propose the Panoramic Glass Image Segmentation Network(PGISNet).This network integrates the Matrix Decomposition Base Module(MDBM),the Transparent Perception Consistency Module(TACM),the Context and Texture Compensation Module(CTCM),and the Multi-scale Gated Context Attention Module(MGCA),constructing a progressive feature processing flow.Experimental results on the PanoGlassV2 benchmark test show that PGISNet achieved 90.03%IoU,94.76%F-score,and 94.0%PA,significantly outperforming existing methods,verifying its effectiveness and advancement in the panoramic image glass segmentation task.展开更多
基金supported by the National Key R&D Program of China(No.2022YFC2504403)the National Natural Science Foundation of China(No.62172202)+1 种基金the Experiment Project of China Manned Space Program(No.HYZHXM01019)the Fundamental Research Funds for the Central Universities from Southeast University(No.3207032101C3)。
文摘Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process.
基金This study was conducted within the project FraxVir“Detection,characterisation and analyses of the occurrence of viruses and ash dieback in special stands of Fraxinus excelsior-a supplementary study to the FraxForFuture demonstration project”and receives funding via the Waldklimafonds(WKF)funded by the German Federal Ministry of Food and Agriculture(BMEL)and Federal Ministry for the Environment,Nature Conservation,Nuclear Safety and Consumer Protection(BMUV)administrated by the Agency for Renewable Resources(FNR)under grant agreement 2220WK40A4.
文摘Detailed individual tree crown segmentation is highly relevant for the detection and monitoring of Fraxinus excelsior L.trees affected by ash dieback,a major threat to common ash populations across Europe.In this study,both fine and coarse crown segmentation methods were applied to close-range multispectral UAV imagery.The fine tree crown segmentation method utilized a novel unsupervised machine learning approach based on a blended NIR-NDVI image,whereas the coarse segmentation relied on the segment anything model(SAM).Both methods successfully delineated tree crown outlines,however,only the fine segmentation accurately captured internal canopy gaps.Despite these structural differences,mean NDVI values calculated per tree crown revealed no significant differences between the two approaches,indicating that coarse segmentation is sufficient for mean vegetation index assessments.Nevertheless,the fine segmentation revealed increased heterogeneity in NDVI values in more severely damaged trees,underscoring its value for detailed structural and health analyses.Furthermore,the fine segmentation workflow proved transferable to both individual UAV images and orthophotos from broader UAV surveys.For applications focused on structural integrity and spatial variation in canopy health,the fine segmentation approach is recommended.
基金Supported by the Shenzhen Science and Technology Program(No.JCYJ20240813152704006)the National Natural Science Foundation of China(No.62401259)+2 种基金the Fundamental Research Funds for the Central Universities(No.NZ2024036)the Postdoctoral Fellowship Program of CPSF(No.GZC20242228)High Performance Computing Platform of Nanjing University of Aeronautics and Astronautics。
文摘AIM:To construct an intelligent segmentation scheme for precise localization of central serous chorioretinopathy(CSC)leakage points,thereby enabling ophthalmologists to deliver accurate laser treatment without navigational laser equipment.METHODS:A dataset with dual labels(point-level and pixel-level)was first established based on fundus fluorescein angiography(FFA)images of CSC and subsequently divided into training(102 images),validation(40 images),and test(40 images)datasets.An intelligent segmentation method was then developed,based on the You Only Look Once version 8 Pose Estimation(YOLOv8-Pose)model and segment anything model(SAM),to segment CSC leakage points.Next,the YOLOv8-Pose model was trained for 200 epochs,and the best-performing model was selected to form the optimal combination with SAM.Additionally,the classic five types of U-Net series models[i.e.,U-Net,recurrent residual U-Net(R2U-Net),attention U-Net(AttU-Net),recurrent residual attention U-Net(R2AttUNet),and nested U-Net(UNet^(++))]were initialized with three random seeds and trained for 200 epochs,resulting in a total of 15 baseline models for comparison.Finally,based on the metrics including Dice similarity coefficient(DICE),intersection over union(IoU),precision,recall,precisionrecall(PR)curve,and receiver operating characteristic(ROC)curve,the proposed method was compared with baseline models through quantitative and qualitative experiments for leakage point segmentation,thereby demonstrating its effectiveness.RESULTS:With the increase of training epochs,the mAP50-95,Recall,and precision of the YOLOv8-Pose model showed a significant increase and tended to stabilize,and it achieved a preliminary localization success rate of 90%(i.e.,36 images)for CSC leakage points in 40 test images.Using manually expert-annotated pixel-level labels as the ground truth,the proposed method achieved outcomes with a DICE of 57.13%,an IoU of 45.31%,a precision of 45.91%,a recall of 93.57%,an area under the PR curve(AUC-PR)of 0.78 and an area under the ROC curve(AUC-ROC)of 0.97,which enables more accurate segmentation of CSC leakage points.CONCLUSION:By combining the precise localization capability of the YOLOv8-Pose model with the robust and flexible segmentation ability of SAM,the proposed method not only demonstrates the effectiveness of the YOLOv8-Pose model in detecting keypoint coordinates of CSC leakage points from the perspective of application innovation but also establishes a novel approach for accurate segmentation of CSC leakage points through the“detect-then-segment”strategy,thereby providing a potential auxiliary means for the automatic and precise realtime localization of leakage points during traditional laser photocoagulation for CSC.
基金supported by grants from the Scientific Research Fund of Education Department of Yunnan Province(2023J767)the National Natural Science Foundation of China(82272963 and 82472718)+6 种基金Health Research Project of Hunan Provincial Health Commission(W20242019)Hunan Provincial Health High-Level Talent Scientific Research Project(R2023096)Hunan Provincial Department of Science and Technology Health Industry Joint Fund(2024JJ9479)Guangdong Province Basic and Applied Basic Research Foundation Project-Guangdong Province Natural Science Foundation(2024A1515220154)"Leading Goose"Project of the Science and Technology Department of Zhejiang Province(2024C03049)Major Project of Health Science and Technology Program of Zhejiang Province(WKJ-ZJ-2407)the National Key Research and Development Program(2024YFB331170204).
文摘Background:Laparoscopic anatomic hepatectomy of segment 7(LAH-S7)is a challenging surgery.In this study we aimed to investigate surgical and oncological outcomes of various approaches of LAH-S7 in patients with hepatocellular carcinoma(HCC).A particular focus was placed on identifying the Glissonean pedicle of segment 7(G7)and the intersegmental plane.Given the scarcity of comprehensive reviews or comparative studies on clinical outcomes,we also sought to analyze the experiences and advantages associated with different approaches in relation to the anatomic variations of G7.Methods:The clinical data of 124 patients who underwent LAH-S7 for HCC across seven tertiary referral medical centers in China were retrospectively analyzed.Three surgical approaches were categorized based on the procedures used for G7 identification:the indocyanine green(ICG)fluorescence positive staining approach(IFPA),the Glissonean approach(GA),and the hepatic vein-guided approach(HVGA).Subsequently,the postoperative short-term results and oncological outcomes of the three different approaches were compared.Results:The distribution of surgical approaches among the patients was as follows:IFPA in 16(12.9%),GA in 62(50.0%),and HVGA in 46(37.1%)patients.Complications were observed in 27(21.8%)patients.The 1-,3-,and 5-year overall survival(OS)rates were 99.1%,89.2%,and 84.7%,respectively.The 1-,3-,and 5-year recurrence-free survival(RFS)rates were 99.0%,84.7%,and 69.3%,respectively.The OS and RFS rates were comparable across the three approaches.Conclusions:Following a standardized surgical procedure,LAH-S7 is demonstrated to be safe and yields favorable oncological outcomes.Surgeons performing LAH-S7 should select the appropriate surgical approach based on the anatomical characteristics and variations of G7.
基金funded by the National Natural Science Foundation of China,grant number 62262045the Fundamental Research Funds for the Central Universities,grant number 2023CDJYGRH-YB11the Open Funding of SUGON Industrial Control and Security Center,grant number CUIT-SICSC-2025-03.
文摘Autonomous vehicles rely heavily on accurate and efficient scene segmentation for safe navigation and efficient operations.Traditional Bird’s Eye View(BEV)methods on semantic scene segmentation,which leverage multimodal sensor fusion,often struggle with noisy data and demand high-performance GPUs,leading to sensor misalignment and performance degradation.This paper introduces an Enhanced Channel Attention BEV(ECABEV),a novel approach designed to address the challenges under insufficient GPU memory conditions.ECABEV integrates camera and radar data through a de-noise enhanced channel attention mechanism,which utilizes global average and max pooling to effectively filter out noise while preserving discriminative features.Furthermore,an improved fusion approach is proposed to efficiently merge categorical data across modalities.To reduce computational overhead,a bilinear interpolation layer normalizationmethod is devised to ensure spatial feature fidelity.Moreover,a scalable crossentropy loss function is further designed to handle the imbalanced classes with less computational efficiency sacrifice.Extensive experiments on the nuScenes dataset demonstrate that ECABEV achieves state-of-the-art performance with an IoU of 39.961,using a lightweight ViT-B/14 backbone and lower resolution(224×224).Our approach highlights its cost-effectiveness and practical applicability,even on low-end devices.The code is publicly available at:https://github.com/YYF-CQU/ECABEV.git.
基金funded by the Research Project:THTETN.05/24-25,VietnamAcademy of Science and Technology.
文摘Satellite image segmentation plays a crucial role in remote sensing,supporting applications such as environmental monitoring,land use analysis,and disaster management.However,traditional segmentation methods often rely on large amounts of labeled data,which are costly and time-consuming to obtain,especially in largescale or dynamic environments.To address this challenge,we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering(SS-MPFC)algorithm,which improves segmentation accuracy and robustness,particularly in complex and uncertain remote sensing scenarios.SS-MPFC unifies three paradigms:semi-supervised learning,multi-view clustering,and picture fuzzy set theory.This integration allows the model to effectively utilize a small number of labeled samples,fuse complementary information from multiple data views,and handle the ambiguity and uncertainty inherent in satellite imagery.We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data,and embeds pairwise semi-supervised constraints(must-link and cannot-link)directly into the clustering process to enhance segmentation accuracy.Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy,noise robustness,and semantic interpretability.On the Augsburg dataset,SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860,highlighting its outstanding robustness and efficiency.These results demonstrate that SSMPFC offers a scalable and effective solution for real-world satellite-based monitoring systems,particularly in scenarios where rapid annotation is infeasible,such as wildfire tracking,agricultural monitoring,and dynamic urban mapping.
基金supported by the National Key R&D Program of China(No.2022YFC3003502).
文摘The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.
基金supported by the National Centerfor Genetic Engineering and Biotechnology(BIOTEC),Thailandpartially funded by the Center of Excellence on Agricultural Biotechnology,Science and Technology Postgraduate Education and Research Development Office,Office of Higher Education Commission,Ministry of Education (AG-BIO/PERDO-CHE),Thailand
文摘Drought is a major abiotic constraint to rice production in rainfed lowland and insufficiently irrigated areas. The improvement of drought tolerant varieties is one of the strategies to reduce the negative effects of drought. Quantitative trait loci （QTLs） for primary and secondary traits related to drought tolerance （DT） on chromosomes 1, 3, 4, 8 and 9 that determined from double haploid lines derived from a cross between CT9993 and IR62266 were introgressed and dissected into small pieces in the genetic background of Khao Dawk Mall 105 （KDML105） to develop chromosome segment substitution line （CSSL） population. The CSSLs were evaluated at the reproductive stage for their agronomic performance and yield components under drought stress, and results were compared with irrigated condition. The flowering of CSSL lines was 6 to 7 d earlier than KDML105. The mean values of grain yields in the CSSLs were higher than KDML105 under drought and irrigated conditions. At irrigated condition, the grain yields of introgression lines carrying DT-QTLs from chromosomes 4 and 8 were higher than that of KDML105, whereas other traits showed little difference with KDML105. Analysis indicated that grain yield has positive correlation with plant height, tiller and panicle number per plant, and total grain weight per plant under drought stress while negatively correlated with days to flowering. As mentioned above, CSSLs showing good adaptation under drought stress can be used as genetic materials to improve drought tolerance in Thai rainfed lowland rice breeding program, and as materials to dissect genes underlying drought tolerance.
基金financially supported by the National Key Research and Development Program of China (2022YFC3005600)the Foundation of the Anhui Educational Commission (2023AH051198)+1 种基金the National Natural Science Foundation of China (42125401 and 42104063)the Joint Open Fund of Mengcheng National Geophysical Observatory (MENGO-202201)。
文摘The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structure in the upper crust and seismogenic tectonics in the Hefei segment of this fault, we collected phase velocity dispersion data of fundamental mode Rayleigh waves from ambient noise cross-correlation functions of ~400 temporal seismographs in an area of approximately 80 × 70 km along the fault zone. The period band of the dispersion data was ~0.5–10 s. We inverted for the upper crustal three-dimensional(3-D) shear velocity model with azimuthal anisotropy from the surface to 10 km depth by using a 3-D direct azimuthal anisotropy inversion method. The inversion result shows the spatial distribution characteristics of the tectonic units in the upper crust. Additionally, the deformation of the Tan-Lu Fault Zone and its conjugated fault systems could be inferred from the anisotropy model. In particular, the faults that have remained active from the early and middle Pleistocene control the anisotropic characteristics of the upper crustal structure in this area. The direction of fast axes near the fault zone area in the upper crust is consistent with the strike of the faults, whereas for the region far away from the fault zone, the direction of fast axes is consistent with the direction of the regional principal stress caused by plate movement. Combined with the azimuthal anisotropy models in the deep crust and uppermost mantle from the surface wave and Pn wave, the different anisotropic patterns caused by the Tan-Lu Fault Zone and its conjugated fault system nearby are shown in the upper and lower crust. Furthermore,by using the double-difference method, we relocated the Lujiang earthquake series, which contained 32 earthquakes with a depth shallower than 10 km. Both the Vs model and earthquake relocation results indicate that earthquakes mostly occurred in the vicinity of structural boundaries with fractured media, with high-level development of cracks and small-scale faults jammed between more rigid areas.
文摘BACKGROUND The incidence of acute myocardial infarction(AMI)is rising,with cardiac rupture accounting for approximately 2%of deaths in patients with acute ST-segment elevation myocardial infarction(STEMI).Ventricular free wall rupture(FWR)occurs in approximately 2%of AMI patients and is notably rare in patients with non-STEMI.Types of cardiac rupture include left ventricular FWR,ventricular septal rupture,and papillary muscle rupture.The FWR usually leads to acute cardiac tamponade or electromechanical dissociation,where standard resuscitation efforts may not be effective.Ventricular septal rupture and papillary muscle rupture often result in refractory heart failure,with mortality rates over 50%,even with surgical or percutaneous repair options.CASE SUMMARY We present a rare case of an acute non-STEMI patient who suffered sudden FWR causing cardiac tamponade and loss of consciousness immediate before undergoing coronary angiography.Prompt resuscitation and emergency open-heart repair along with coronary artery bypass grafting resulted in successful patient recovery.CONCLUSION This case emphasizes the risks of AMI complications,shares a successful treatment scenario,and discusses measures to prevent such complications.
基金supported by the National Key Research and Development Program of China under Grant No.2022YFA1602003,entitled"Intelligent Monitoring of Taishan Neutrino Detector".
文摘In panoramic images,the geometric distortion caused by wide-angle lenses makes traditional semantic segmentation methods difficult to accurately segment the glass areas.To address the challenges of capturing spatial features and integrating context information,we propose the Panoramic Glass Image Segmentation Network(PGISNet).This network integrates the Matrix Decomposition Base Module(MDBM),the Transparent Perception Consistency Module(TACM),the Context and Texture Compensation Module(CTCM),and the Multi-scale Gated Context Attention Module(MGCA),constructing a progressive feature processing flow.Experimental results on the PanoGlassV2 benchmark test show that PGISNet achieved 90.03%IoU,94.76%F-score,and 94.0%PA,significantly outperforming existing methods,verifying its effectiveness and advancement in the panoramic image glass segmentation task.
