Panax Ginseng(2n=48)represents a quintessential resource in traditional Chinese medicine,renowned for its outstanding medicinal and economic benefits(Choi,2008).But the late start in analyzing the ginseng genome and t...Panax Ginseng(2n=48)represents a quintessential resource in traditional Chinese medicine,renowned for its outstanding medicinal and economic benefits(Choi,2008).But the late start in analyzing the ginseng genome and the poorly developed genetic transformation system still impede the study of ginseng gene function and the application of molecular breeding.Transient transformation has the advantages of high efficiency,low cost,and short cycle while laying the foundation for stable genetic transformation(Chen et al.,2021).In the plant transformation process,the cell wall prevents exogenous DNA or protein entry,significantly reducing the efficiency of the transformation.Protoplasts,as exposed cells wrapped by the plasma membrane,are more likely to absorb exogenous DNA,RNA,and protein.Transgenic systems of protoplasts have been established in several species and applied in many fields,such as gene function research(Gou et al.,2020),gene editing(Yang et al.,2023),and physiological or molecular mechanism research(Aoyagi,2011).For instance,Oryza sativa protoplasts were employed to screen genes involved in rice defense signaling pathways through fluorescent reporter systems,with BiFC employed to verified inter-protein interactions(He et al.,2016).A study transformed Cannabis sativa L.protoplasts with the plasmids carrying GFP and RFP genes,evaluated the efficiency under different transformation conditions by flow cytometry,and verified the induction of synthetic DR5 promoter by IAA based on the constructed system(Beard et al.,2021).展开更多
Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,...Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.展开更多
Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facili...Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facilitating safe and flexible navigation in dense forests.However,their limited onboard computational power makes real-time,image-based tree trunk segmentation challenging,emphasizing the urgent need for lightweight and efficient segmentation models.In this study,we present RT-Trunk,a model specifically designed for real-time tree trunk instance segmentation in complex forest environments.To ensure real-time performance,we selected SparseInst as the base framework.We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks,thereby improving segmentation accuracy.We further integrate the lightweight convolutional block attention module(CBAM),enabling the model to focus on tree trunk features while suppressing irrelevant information,which leads to additional gains in segmentation accuracy.To enable RT-Trunk to operate effectively under diverse complex forest environments,we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations,seasons,weather conditions,tree species,and levels of forest clutter.Com-pared with the other tree trunk segmentation methods,the RT-Trunk method achieved an average precision of 91.4%and the fastest inference speed of 32.9 frames per second.Overall,the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accu-racy,making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes.The code for this work is available at https://github.com/NEFU CVRG/RT Trunk.展开更多
The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly researched.However,due to the complex structure,low contrast,and complex background of teeth in panoramic X-ray images,the task o...The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly researched.However,due to the complex structure,low contrast,and complex background of teeth in panoramic X-ray images,the task of instance segmentation is technically tricky.In this study,the contrast between impacted Teeth and periodontal tissues such as gingiva,periodontalmembrane,and alveolar bone is low,resulting in fuzzy boundaries of impacted teeth.Amodel based on Teeth YOLACT is proposed to provide amore efficient and accurate solution for the segmentation of impacted teeth in oral panoramic X-ray films.Firstly,a Multi-scale Res-Transformer Module(MRTM)is designed.In the module,depthwise separable convolutions with different receptive fields are used to enhance the sensitivity of the model to lesion size.Additionally,the Vision Transformer is integrated to improve the model’s ability to perceive global features.Secondly,the Context Interaction-awareness Module(CIaM)is designed to fuse deep and shallow features.The deep semantic features guide the shallow spatial features.Then,the shallow spatial features are embedded into the deep semantic features,and the cross-weighted attention mechanism is used to aggregate the deep and shallow features efficiently,and richer context information is obtained.Thirdly,the Edge-preserving perceptionModule(E2PM)is designed to enhance the teeth edge features.The first-order differential operator is used to get the tooth edge weight,and the perception ability of tooth edge features is improved.The shallow spatial feature is fused by linear mapping,weight concatenation,and matrix multiplication operations to preserve the tooth edge information.Finally,comparison experiments and ablation experiments are conducted on the oral panoramic X-ray image datasets.The results show that the APdet,APseg,ARdet,ARseg,mAPdet,and mAPseg indicators of the proposed model are 89.9%,91.9%,77.4%,77.6%,72.8%,and 73.5%,respectively.This study further verifies the application potential of the method combining multi-scale feature extraction,multi-scale feature fusion,and edge perception enhancement in medical image segmentation,which provides a valuable reference for future related research.展开更多
The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-r...The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.展开更多
In actual traffic scenarios,precise recognition of traffic participants,such as vehicles and pedestrians,is crucial for intelligent transportation.This study proposes an improved algorithm built on Mask-RCNN to enhanc...In actual traffic scenarios,precise recognition of traffic participants,such as vehicles and pedestrians,is crucial for intelligent transportation.This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants.The algorithmincorporates long and shortterm memory networks and the fused attention module(GSAM,GCT,and Spatial Attention Module)to enhance the algorithm’s capability to process both global and local information.Additionally,to increase the network’s initial operation stability,the original network activation function was replaced with Gaussian error linear unit.Experiments were conducted using the publicly available Cityscapes dataset.Comparing the test results,it was observed that the revised algorithmoutperformed the original algorithmin terms of AP_(50),AP_(75),and othermetrics by 8.7%and 9.6%for target detection and 12.5%and 13.3%for segmentation.展开更多
Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often...Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.展开更多
Dear readers,In the field of stroke research and treatment,cutting-edge technologies and studies continue to emerge,providing new perspectives and strategies for exploring the mechanisms and treatment methods of strok...Dear readers,In the field of stroke research and treatment,cutting-edge technologies and studies continue to emerge,providing new perspectives and strategies for exploring the mechanisms and treatment methods of stroke.According to the latest advancements,neuroimaging techniques play a crucial role in stroke research.MRI technology,for instance,is essential for evaluating stroke patients.展开更多
This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The go...This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.展开更多
Chronic heart failure(CHF)is a clinical syndrome manifested by reduced pumping ability of the heart,increased pressure in heart chambers in both physical activity and at rest.The symptoms of this syndrome are dyspnea,...Chronic heart failure(CHF)is a clinical syndrome manifested by reduced pumping ability of the heart,increased pressure in heart chambers in both physical activity and at rest.The symptoms of this syndrome are dyspnea,undue fatigability,peripheral edema,which follow structural and functional changes of the myocardium.[1]The growing incidence of CHF,especially among elderly people,is an urgent problem for medicine in the vast majority of industrialized countries.For instance,in Russian Federation,CHF is diagnosed in about 7%of cardiovascular patients.At the same time,this indicator varies from 0.3%in young people(20-29 years old)to 70%in the older age group.[2,3].展开更多
We read the article“How to manage the malposition of deep vein catheterization into the artery”[1]with keen interest.However,we have several concerns with the proposed algorithm.First,the site of catheter misplaceme...We read the article“How to manage the malposition of deep vein catheterization into the artery”[1]with keen interest.However,we have several concerns with the proposed algorithm.First,the site of catheter misplacement is assumed to be the subclavian artery,the most frequent site of misplacement during internal jugular vein catheterization.[2]However,catheter misplacement can occur in the common carotid and vertebral arteries during internal jugular vein catheterization.[2,3]If a catheter is misplaced in one of these arteries,preventing cerebral ischemia is a priority.[2,4,5]For example,if a thrombus forms around the catheter,a method is chosen to resolve it while preventing dispersion and closing the perforation.[2,6]Therefore,open surgical closure must be selected.Second,the algorithm may not handle instances of realistic catheter misplacement in the arteries.We assume a case where an internal jugular venous catheter(5Fr double-lumen catheter)is inserted but accidentally penetrates the subclavian artery and is placed in the thoracic cavity.Suppose that the injured site is about 5 mm from the confluence of the right common carotid or vertebral arteries.展开更多
Dear Editor,I mplantable collamer lens(ICL)surgery demonstrates longterm stability and favorable refractive outcome[1-2].An increasing number of individuals across all age groups opt for refraction removal through ICL...Dear Editor,I mplantable collamer lens(ICL)surgery demonstrates longterm stability and favorable refractive outcome[1-2].An increasing number of individuals across all age groups opt for refraction removal through ICL surgery.Currently,instances of ICL displacement resulting from trauma remain rare,and there are no documented cases of ICL damage due to blunt trauma.Postoperative ICL dislocations were found in 7 eyes(9775 total,equating to 0.072%of ICL implants),averaging 28.6mo(11-82mo)[3].展开更多
The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are ...The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.展开更多
Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,...Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.展开更多
Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance...Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial contour.To solve these problems,a real-time instance segmentation algorithm based on contour learning was proposed.Firstly,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution kernel.Secondly,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more comprehensively.Thirdly,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real contour.Finally,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour.The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s^(−1) when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU.The proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.展开更多
Copper sulfate is a frequently used copper compound in laboratory settings,with instances of poisoning being uncommon.A study conducted by the American Association of Poison Control Centers’National Poison Data Syste...Copper sulfate is a frequently used copper compound in laboratory settings,with instances of poisoning being uncommon.A study conducted by the American Association of Poison Control Centers’National Poison Data System found that only 140 individuals were exposed to copper compounds over the course of a year,with five cases being intentional(Gummin et al.,2023).Severe poisoning from copper sulfate can result in isolated gastrointestinal injury(Galust et al.,2023),intravascular hemolysis(Adline et al.,2024),rhabdomyolysis(Richards et al.,2020),and other symptoms documented in the literature.展开更多
The Third Plenum holds a unique place in the Communist Party of China(CPC)’s political calendar because the Party often introduces significant reforms and outlines strategic priorities during the event.For instance,t...The Third Plenum holds a unique place in the Communist Party of China(CPC)’s political calendar because the Party often introduces significant reforms and outlines strategic priorities during the event.For instance,the Third Plenary Session of the 11th Central Committee of CPC in 1978 marked the beginning of China’s reform and opening-up drive under Deng Xiaoping,after which the country underwent historic economic development.展开更多
Traditional Chinese villages serve as crucial repositories of traditional culture.However,In China,the urgent task of preserving information about traditional village architecture has arisen due to the degradation of ...Traditional Chinese villages serve as crucial repositories of traditional culture.However,In China,the urgent task of preserving information about traditional village architecture has arisen due to the degradation of these villages’appearance caused by rapid urbanization in recent years.This paper proposes a method for preserving information about traditional village rooftops based on high spatial resolution remote sensing imagery.Leveraging an improved Mask R-CNN model,the method conducts target recognition on the rooftops of traditional village buildings and generates vectorized representations of these rooftops.The precision rate,recall rate,and F1-score achieved in the experimental results are 93.26%,86.33%,and 92.02%,respectively.These findings indicate the effectiveness of the proposed method in preserving information about traditional village architecture and providing a viable approach to support the sustainable development of traditional villages in China.展开更多
Correction.The corresponding author of“A Multi-Center Randomized Controlled Study UsingΔP_(CO2)/Ca-v_(O2) as the Target to Guide Early Tissue Hypoperfusion in Sepsis in Plateau Areas,”published in Volume 8 Issue 1(...Correction.The corresponding author of“A Multi-Center Randomized Controlled Study UsingΔP_(CO2)/Ca-v_(O2) as the Target to Guide Early Tissue Hypoperfusion in Sepsis in Plateau Areas,”published in Volume 8 Issue 1(2024)of Journal of Clinical and Nursing Research(https://www.doi.org/10.26689/jcnr.v8i1.5944),wrote to the editors about a typographical error in the article.All instances of“>1.84”(found in the abstract and conclusion)should be corrected to“<1.84.”展开更多
Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrat...Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.展开更多
基金supported by the Genetic analysis of important quality and traits of ginseng and basic research on molecular design breeding(Grant No.U21A20405)。
文摘Panax Ginseng(2n=48)represents a quintessential resource in traditional Chinese medicine,renowned for its outstanding medicinal and economic benefits(Choi,2008).But the late start in analyzing the ginseng genome and the poorly developed genetic transformation system still impede the study of ginseng gene function and the application of molecular breeding.Transient transformation has the advantages of high efficiency,low cost,and short cycle while laying the foundation for stable genetic transformation(Chen et al.,2021).In the plant transformation process,the cell wall prevents exogenous DNA or protein entry,significantly reducing the efficiency of the transformation.Protoplasts,as exposed cells wrapped by the plasma membrane,are more likely to absorb exogenous DNA,RNA,and protein.Transgenic systems of protoplasts have been established in several species and applied in many fields,such as gene function research(Gou et al.,2020),gene editing(Yang et al.,2023),and physiological or molecular mechanism research(Aoyagi,2011).For instance,Oryza sativa protoplasts were employed to screen genes involved in rice defense signaling pathways through fluorescent reporter systems,with BiFC employed to verified inter-protein interactions(He et al.,2016).A study transformed Cannabis sativa L.protoplasts with the plasmids carrying GFP and RFP genes,evaluated the efficiency under different transformation conditions by flow cytometry,and verified the induction of synthetic DR5 promoter by IAA based on the constructed system(Beard et al.,2021).
基金supported by Science and Technology Research Youth Project of Chongqing Municipal Education Commission(No.KJQN202301104)Cooperative Project between universities in Chongqing and Affiliated Institutes of Chinese Academy of Sciences(No.HZ2021011)+1 种基金Chongqing Municipal Science and Technology Commission Technology Innovation and Application Development Special Project(No.2022TIAD-KPX0040)Action Plan for Quality Development of Chongqing University of Technology Graduate Education(Grant No.gzlcx20242014).
文摘Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.
基金supported in part by the National Natural Science Foundation of China(No.31470714 and 61701105).
文摘Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facilitating safe and flexible navigation in dense forests.However,their limited onboard computational power makes real-time,image-based tree trunk segmentation challenging,emphasizing the urgent need for lightweight and efficient segmentation models.In this study,we present RT-Trunk,a model specifically designed for real-time tree trunk instance segmentation in complex forest environments.To ensure real-time performance,we selected SparseInst as the base framework.We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks,thereby improving segmentation accuracy.We further integrate the lightweight convolutional block attention module(CBAM),enabling the model to focus on tree trunk features while suppressing irrelevant information,which leads to additional gains in segmentation accuracy.To enable RT-Trunk to operate effectively under diverse complex forest environments,we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations,seasons,weather conditions,tree species,and levels of forest clutter.Com-pared with the other tree trunk segmentation methods,the RT-Trunk method achieved an average precision of 91.4%and the fastest inference speed of 32.9 frames per second.Overall,the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accu-racy,making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes.The code for this work is available at https://github.com/NEFU CVRG/RT Trunk.
基金supported in part by the National Natural Science Foundation of China(Grant No.62062003)Natural Science Foundation of Ningxia(Grant No.2023AAC03293).
文摘The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly researched.However,due to the complex structure,low contrast,and complex background of teeth in panoramic X-ray images,the task of instance segmentation is technically tricky.In this study,the contrast between impacted Teeth and periodontal tissues such as gingiva,periodontalmembrane,and alveolar bone is low,resulting in fuzzy boundaries of impacted teeth.Amodel based on Teeth YOLACT is proposed to provide amore efficient and accurate solution for the segmentation of impacted teeth in oral panoramic X-ray films.Firstly,a Multi-scale Res-Transformer Module(MRTM)is designed.In the module,depthwise separable convolutions with different receptive fields are used to enhance the sensitivity of the model to lesion size.Additionally,the Vision Transformer is integrated to improve the model’s ability to perceive global features.Secondly,the Context Interaction-awareness Module(CIaM)is designed to fuse deep and shallow features.The deep semantic features guide the shallow spatial features.Then,the shallow spatial features are embedded into the deep semantic features,and the cross-weighted attention mechanism is used to aggregate the deep and shallow features efficiently,and richer context information is obtained.Thirdly,the Edge-preserving perceptionModule(E2PM)is designed to enhance the teeth edge features.The first-order differential operator is used to get the tooth edge weight,and the perception ability of tooth edge features is improved.The shallow spatial feature is fused by linear mapping,weight concatenation,and matrix multiplication operations to preserve the tooth edge information.Finally,comparison experiments and ablation experiments are conducted on the oral panoramic X-ray image datasets.The results show that the APdet,APseg,ARdet,ARseg,mAPdet,and mAPseg indicators of the proposed model are 89.9%,91.9%,77.4%,77.6%,72.8%,and 73.5%,respectively.This study further verifies the application potential of the method combining multi-scale feature extraction,multi-scale feature fusion,and edge perception enhancement in medical image segmentation,which provides a valuable reference for future related research.
基金funded by Anhui Provincial Natural Science Foundation(No.2208085ME128)the Anhui University-Level Special Project of Anhui University of Science and Technology(No.XCZX2021-01)+1 种基金the Research and the Development Fund of the Institute of Environmental Friendly Materials and Occupational Health,Anhui University of Science and Technology(No.ALW2022YF06)Anhui Province New Era Education Quality Project(Graduate Education)(No.2022xscx073).
文摘The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.
基金the National Natural Science Foundation of China(52175236)Qingdao People’s Livelihood Science and Technology Plan(19-6-1-88-nsh).
文摘In actual traffic scenarios,precise recognition of traffic participants,such as vehicles and pedestrians,is crucial for intelligent transportation.This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants.The algorithmincorporates long and shortterm memory networks and the fused attention module(GSAM,GCT,and Spatial Attention Module)to enhance the algorithm’s capability to process both global and local information.Additionally,to increase the network’s initial operation stability,the original network activation function was replaced with Gaussian error linear unit.Experiments were conducted using the publicly available Cityscapes dataset.Comparing the test results,it was observed that the revised algorithmoutperformed the original algorithmin terms of AP_(50),AP_(75),and othermetrics by 8.7%and 9.6%for target detection and 12.5%and 13.3%for segmentation.
基金This research was supported by the National Natural Science Foundation of China No.62276086the National Key R&D Program of China No.2022YFD2000100Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGN23D010002.
文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.
文摘Dear readers,In the field of stroke research and treatment,cutting-edge technologies and studies continue to emerge,providing new perspectives and strategies for exploring the mechanisms and treatment methods of stroke.According to the latest advancements,neuroimaging techniques play a crucial role in stroke research.MRI technology,for instance,is essential for evaluating stroke patients.
基金The results and knowledge included herein have been obtained owing to support from the following institutional grant.Internal grant agency of the Faculty of Economics and Management,Czech University of Life Sciences Prague,Grant No.2023A0004-“Text Segmentation Methods of Historical Alphabets in OCR Development”.https://iga.pef.czu.cz/.Funds were granted to T.Novák,A.Hamplová,O.Svojše,and A.Veselýfrom the author team.
文摘This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.
文摘Chronic heart failure(CHF)is a clinical syndrome manifested by reduced pumping ability of the heart,increased pressure in heart chambers in both physical activity and at rest.The symptoms of this syndrome are dyspnea,undue fatigability,peripheral edema,which follow structural and functional changes of the myocardium.[1]The growing incidence of CHF,especially among elderly people,is an urgent problem for medicine in the vast majority of industrialized countries.For instance,in Russian Federation,CHF is diagnosed in about 7%of cardiovascular patients.At the same time,this indicator varies from 0.3%in young people(20-29 years old)to 70%in the older age group.[2,3].
文摘We read the article“How to manage the malposition of deep vein catheterization into the artery”[1]with keen interest.However,we have several concerns with the proposed algorithm.First,the site of catheter misplacement is assumed to be the subclavian artery,the most frequent site of misplacement during internal jugular vein catheterization.[2]However,catheter misplacement can occur in the common carotid and vertebral arteries during internal jugular vein catheterization.[2,3]If a catheter is misplaced in one of these arteries,preventing cerebral ischemia is a priority.[2,4,5]For example,if a thrombus forms around the catheter,a method is chosen to resolve it while preventing dispersion and closing the perforation.[2,6]Therefore,open surgical closure must be selected.Second,the algorithm may not handle instances of realistic catheter misplacement in the arteries.We assume a case where an internal jugular venous catheter(5Fr double-lumen catheter)is inserted but accidentally penetrates the subclavian artery and is placed in the thoracic cavity.Suppose that the injured site is about 5 mm from the confluence of the right common carotid or vertebral arteries.
基金Supported by the Guangdong Medical Research Foundation(No.B2023206).
文摘Dear Editor,I mplantable collamer lens(ICL)surgery demonstrates longterm stability and favorable refractive outcome[1-2].An increasing number of individuals across all age groups opt for refraction removal through ICL surgery.Currently,instances of ICL displacement resulting from trauma remain rare,and there are no documented cases of ICL damage due to blunt trauma.Postoperative ICL dislocations were found in 7 eyes(9775 total,equating to 0.072%of ICL implants),averaging 28.6mo(11-82mo)[3].
基金funded by National Natural Science Foundation of China No.62062003Ningxia Natural Science Foundation Project No.2023AAC03293.
文摘The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.
基金the National Natural Science Foundation of China(No.62063006)the Natural Science Foundation of Guangxi Province(No.2023GXNS-FAA026025)+3 种基金the Innovation Fund of Chinese Universities Industry-University-Research(ID:2021RYC06005)the Research Project for Young andMiddle-Aged Teachers in Guangxi Universi-ties(ID:2020KY15013)the Special Research Project of Hechi University(ID:2021GCC028)financially supported by the Project of Outstanding Thousand Young Teachers’Training in Higher Education Institutions of Guangxi,Guangxi Colleges and Universities Key Laboratory of AI and Information Processing(Hechi University),Education Department of Guangxi Zhuang Autonomous Region.
文摘Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.
基金supported by National Key Research and Development Program(No.2022YFE0112400)National Natural Science Foundation of China(No.21706096)Natural Science Foundation of Jiangsu Province(No.BK20160162).
文摘Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial contour.To solve these problems,a real-time instance segmentation algorithm based on contour learning was proposed.Firstly,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution kernel.Secondly,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more comprehensively.Thirdly,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real contour.Finally,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour.The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s^(−1) when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU.The proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.
基金supported by the National Key Research and Development Program of China(Nos.2023YFC3603100 and 2023YFC3603105)the“Leading Goose”R&D Program of Zhejiang(No.2022C03076-4),China。
文摘Copper sulfate is a frequently used copper compound in laboratory settings,with instances of poisoning being uncommon.A study conducted by the American Association of Poison Control Centers’National Poison Data System found that only 140 individuals were exposed to copper compounds over the course of a year,with five cases being intentional(Gummin et al.,2023).Severe poisoning from copper sulfate can result in isolated gastrointestinal injury(Galust et al.,2023),intravascular hemolysis(Adline et al.,2024),rhabdomyolysis(Richards et al.,2020),and other symptoms documented in the literature.
文摘The Third Plenum holds a unique place in the Communist Party of China(CPC)’s political calendar because the Party often introduces significant reforms and outlines strategic priorities during the event.For instance,the Third Plenary Session of the 11th Central Committee of CPC in 1978 marked the beginning of China’s reform and opening-up drive under Deng Xiaoping,after which the country underwent historic economic development.
文摘Traditional Chinese villages serve as crucial repositories of traditional culture.However,In China,the urgent task of preserving information about traditional village architecture has arisen due to the degradation of these villages’appearance caused by rapid urbanization in recent years.This paper proposes a method for preserving information about traditional village rooftops based on high spatial resolution remote sensing imagery.Leveraging an improved Mask R-CNN model,the method conducts target recognition on the rooftops of traditional village buildings and generates vectorized representations of these rooftops.The precision rate,recall rate,and F1-score achieved in the experimental results are 93.26%,86.33%,and 92.02%,respectively.These findings indicate the effectiveness of the proposed method in preserving information about traditional village architecture and providing a viable approach to support the sustainable development of traditional villages in China.
文摘Correction.The corresponding author of“A Multi-Center Randomized Controlled Study UsingΔP_(CO2)/Ca-v_(O2) as the Target to Guide Early Tissue Hypoperfusion in Sepsis in Plateau Areas,”published in Volume 8 Issue 1(2024)of Journal of Clinical and Nursing Research(https://www.doi.org/10.26689/jcnr.v8i1.5944),wrote to the editors about a typographical error in the article.All instances of“>1.84”(found in the abstract and conclusion)should be corrected to“<1.84.”
文摘Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.
