Anatomical Retrofitting:Pi(脾)as Spleen and the Persistence of Ontological Ambivalence

Over the past decade,medical researchers in China have debated whether the Pi(脾)corresponds to the biomedical spleen or pancreas.This debate exemplifies a broader phenomenon of“anatomical retrofitting”,or the anachronistic imposition of contemporary categories onto living historical objects.“Anatomical retrofitting”as a means of rectifying cases of mistranslation further positions the biomedical spleen and pancreas as representing ahistorical,universal truths.This framework gives rise to a conceptual binary:while the biomedical spleen is universalized as what philosophers may describe as“logical”ontology,the Pi connects to a different nature of reality,or“metaphysical”ontology.Far from being an object of imprecision,the Pi was a dynamic vessel that also shared characteristics with the humoral spleen.Given that scholars in China have already subjected Pi to historical scrutiny,this paper urges scholars to do the same with biomedical anatomy.For instance,historically situating the humoral spleen demonstrates that it became less known and less articulated as it transformed into the biomedical spleen.Meanwhile,the pancreas remained an unstable epistemic object that took on the dynamic functions of the humoral spleen in nineteenth-century organotherapy.Through primary source analysis and literature review,this paper contends that the apparent ontological incommensurability between Pi and spleen is neither mutually exclusive nor irreconcilable.Instead,the dynamic nature of internal viscera,their many functions,and their participation in epistemic practices contribute to an ongoing ontological ambivalence that persists despite the forced certainty of anatomical retrofitting.

Optimised CNN Architectures for Handwritten Arabic Character Recognition

Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.

Insights into the dwarfing mechanism of pear(Pyrus betulaefolia) based on anatomical and structural analysis using X-ray scanning

The lack of a suitable rootstock to control scion growth has limited the development of high-density plantations in pear production, which is partly attributed to poor understanding of the dwarfing mechanism. In the present study, the rootstock of the dwarf-type pear (Pyrus betulaefolia)PY-9’ was identified and used as the material for anatomical analysis.PY-9’ grew to half the tree height of the normal cultivar Zhengdu’, along with fewer internodes and shorter length. Significant differences in growth rate betweenPY-9’ andZhengdu’ were detected at approximately 30 days after full bloom, which corresponded with the time of the greatest difference in water potential between the dwarf and normal cultivar.PY-9’ showed a higher photosynthetic rate thanZhengdu’. Anatomical analysis showed thatPY-9’ had higher area ratios of both phloem and xylem and more developed vascular tissues thanZhengdu’. The three-dimensional reconstructed skeleton of the xylem from X-ray computed tomography scanning revealed greater intervessel connectivity inZhengdu’ than inPY-9’, which could contribute to the more vigorous growth ofZhengdu’. This study thus provides the first comparison of the microstructural properties of xylem elements between a dwarfing-type and vigorous-type pear rootstock, providing new insights into the dwarfing mechanism in pear and facilitating breeding of dwarf pear rootstocks to increase crop productivity.

Effect of Co on Solidification Characteristics and Microstructural Transformation of Non-equilibrium Solidified Cu-Ni Alloys

Non-equilibrium solidification structures of Cu55Ni45 and Cu55Ni43Co2 alloys were prepared by the molten glass purification cycle superheating method.The variation of the recalescence phenomenon with the degree of undercooling in the rapid solidification process was investigated using an infrared thermometer.The addition of the Co element affected the evolution of the recalescence phenomenon in Cu-Ni alloys.The images of the solid-liquid interface migration during the rapid solidification of supercooled melts were captured by using a high-speed camera.The solidification rate of Cu-Ni alloys,with the addition of Co elements,was explored.Finally,the grain refinement structure with low supercooling was characterised using electron backscatter diffraction(EBSD).The effect of Co on the microstructural evolution during nonequilibrium solidification of Cu-Ni alloys under conditions of small supercooling is investigated by comparing the microstructures of Cu55Ni45 and Cu55Ni43Co2 alloys.The experimental results show that the addition of a small amount of Co weakens the recalescence behaviour of the Cu55Ni45 alloy and significantly reduces the thermal strain in the rapid solidification phase.In the rapid solidification phase,the thermal strain is greatly reduced,and there is a significant increase in the characteristic undercooling degree.Furthermore,the addition of Co and the reduction of Cu not only result in a lower solidification rate of the alloy,but also contribute to the homogenisation of the grain size.

High Prevalence of Anatomical Variations and Anomalies of the Coronary Arteries Detected by CT Angiography in Symptomatic Patients

Objective:Coronary artery anatomical variations and anomalies are an important topic due to their potential clinical manifestations.This study aims to investigate the prevalence of coronary artery anatomical variations and anomalies in symptomatic patients with coronary computed tomography angiography(CCTA).Methods:This is a retrospective study that included all symptomatic patients who had CCTA in a tertiary care hospital in Saudi Arabia during a period of seven years.Results:The total number of included patients was 507(60%males)with a mean age of 57.4 years.Approximately 41%had luminal stenoses,averaging 49.7%.The total num-ber of patients with coronary anatomical variations(CAV)and coronary artery anomalies(CAA)was 217(43%).CAV prevalence was 26%,which included 14%non-right coronary dominance,5%short left main coronary artery(LMCA),and 7%division variations(trifurcation and quadrifurcarion)of the LMCA.The prevalence of CAA was 29%,which included 5%origin anomalies,22%myocardial bridge,and 2%course anomalies.Conclusions:A high prevalence of coronary artery anatomic variations and anomalies in symptomatic patients is reported in this study.Systematic reviews,meta-analyses,reporting guidelines,and unified definitions and classifications of cor-onary variations and anomalies are lacking in the literature,presenting potential opportunities for future research and publications.

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

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.

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

Evaluation of Functional and Radiological Outcome of Arthroscopic-Assisted Anatomical Coracoclavicular (CC) and Acromioclavicular (AC) Ligament Reconstruction in Chronic AC Joint Dislocation

Introduction: Acromioclavicular (AC) joint dislocation is a common shoulder injury, comprising 9% - 12% of shoulder girdle injuries. Optimal management remains challenging, with treatment decisions guided by the Rockwood classification system. Controversies surround grade III injuries, necessitating further classification. Non-operative treatment has shown favorable outcomes, while surgical interventions vary. Anatomical coracoclavicular reconstruction (ACCR) has demonstrated biomechanical advantages over traditional methods. Arthroscopic techniques offer advantages, minimizing deltoid detachment and allowing concurrent pathology identification. This study evaluates the outcomes of arthroscopic-assisted ACCR in chronic AC joint dislocation. Surgical Technique: Arthroscopic-assisted ACCR involves meticulous portal placement, tendon graft harvesting, diagnostic arthroscopy, and coracoid exposure. The clavicle tunnels were made to mimic the conoid and trapezoid ligament positions, using FibreTape#2 loop and Dog Bone Button for correct placement against the coracoid base, and passing the semitendinosus graft through to reconstruct the conoid ligament, reduction done and graft follow through for anatomical reconstruction. Methods: A retrospective cohort study at Hospital Kuala Lumpur analyzed 35 patients undergoing arthroscopic-assisted ACCR for Rockwood grade III - V AC joint dislocations. Inclusion criteria encompassed trauma ≥ 3 weeks prior, no prior shoulder injuries, and ≥12-month follow-up. Functional and radiological assessments utilized ASES scores and coracoclavicular distances, respectively. Statistical analysis employed descriptive statistics and logistic regression. Results: The mean age was 38.9 years (SD 11.26), and 34 of 35 patients were male. Grade IV injuries were predominant (37.1%). Waiting time for surgery averaged 234.9 days. Functional improvement was substantial postoperatively (ASES: 55.5 to 88.9). Radiological outcomes demonstrated reduced coracoclavicular distances and maintained reduction. No significant correlation was observed between injury grade and outcomes. Conclusion: Arthroscopic-assisted ACCR for chronic AC joint dislocation yields significant functional and radiological improvement, irrespective of injury grade. Waiting time for surgery exhibits minor impact on outcomes, emphasizing the procedure’s efficacy. Concomitant injuries do not impede success, highlighting the versatility of this approach in managing shoulder instability. The study contributes valuable insights into the nuanced management of chronic AC joint dislocations and supports the adoption of arthroscopic-assisted ACCR as a viable treatment option.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

Audio2AB:Audio-driven collaborative generation of virtual character animation

Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success.However,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient attention.Methods Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body animations.This parameterization method can be used to drive full-body animations of virtual characters and improve their portability.In the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial data.The Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio features.Finally,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function.Results By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape weights.Therefore,different 3D virtual character animations could be created through parameterization.Conclusions The experimental results showed that the proposed method could generate significant gestures and facial animations.

A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.

High rate of clinically relevant improvement following anatomical total shoulder arthroplasty for glenohumeral osteoarthritis

BACKGROUND The minimal clinically important difference(MCID)is defined as the smallest meaningful change in a health domain that a patient would identify as important.Thus,an improvement that exceeds the MCID can be used to define a successful treatment for the individual patient.AIM To quantify the rate of clinical improvement following anatomical total shoulder arthroplasty for glenohumeral osteoarthritis.METHODS Patients were treated with the Global Unite total shoulder platform arthroplasty between March 2017 and February 2019 at Herlev and Gentofte Hospital,Denmark.The patients were evaluated preoperatively and 3 months,6 months,12 months,and 24 months postoperatively using the Western Ontario Osteoarthritis of the Shoulder index(WOOS),Oxford Shoulder Score(OSS)and Constant-Murley Score(CMS).The rate of clinically relevant improvement was defined as the proportion of patients who had an improvement 24 months postoperatively that exceeded the MCID.Based on previous literature,MCID for WOOS,OSS,and CMS were defined as 12.3,4.3,and 12.8 respectively.RESULTS Forty-nine patients with a Global Unite total shoulder platform arthroplasty were included for the final analysis.Mean age at the time of surgery was 66 years(range 49.0-79.0,SD:8.3)and 65%were women.One patient was revised within the two years follow-up.The mean improvement from the preoperative assessment to the two-year follow-up was 46.1 points[95%confidence interval(95%CI):39.7-53.3,P<0.005]for WOOS,18.2 points(95%CI:15.5-21.0,P<0.005)for OSS and 37.8 points(95%CI:31.5-44.0,P<0.005)for CMS.Two years postoperatively,41 patients(87%)had an improvement in WOOS that exceeded the MCID,45 patients(94%)had an improvement in OSS that exceeded the MCID,and 42 patients(88%)had an improvement in CMS that exceeded the MCID.CONCLUSION Based on three shoulder-specific outcome measures we find that approximately 90%of patients has a clinically relevant improvement.This is a clear message when informing patients about their prognosis.

Frontier Progress of Landscape Character Assessment in China

The role of Landscape Character Assessment(LCA)at the level of territorial landscape governance spans both natural and social sciences.By analyzing the development history,research distribution,methods and applications of cutting-edge cases of LCA in China,the following conclusions are drawn:①the LCA research in China originated earlier than that in Europe,but has not yet been systematically applied to the implementation of urban and rural planning at all levels;②the fundamental theory of LCA in China has been well constructed,with three main research directions:technologyled,assessment-led,and assessment combined with other theories;③the development of LCA in rural areas is more mature than in urban areas,but the progress of research is uneven across regions;④the current research presents significant“bottom-up”academic characteristics,and there is an urgent need for government decision-making authorities and academia to jointly promote a“top-down”standardized governance mechanism to comprehensively promote the modernization of territorial landscape governance.

Progress in the evaluation tool of Character Strengths and its application in nursing staff

Character Strengths is a group of positive personality traits reflected through cognition,behavior,and emotion,which play a positive role in improving happiness,alleviating negative emotions,and maintaining physical and mental health.This article reviews the concept,content,methods,evaluation tools,and application progress of Character Strengths in nurses,so as to provide a reference for clinical managers and improve the quality of life,mental health,and professional satisfaction of n urses.

Chip Surface Character Recognition Based on OpenCV

Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing.By recognizing the character information on the chip,automated production,quality control,and data collection and analysis can be achieved.This article studies a chip surface character recognition method based on the OpenCV vision library.Firstly,the obtained chip images are preprocessed.Secondly,the template matching method is used to locate the chip position.In addition,the surface characters on the chip are individually segmented,and each character image is extracted separately.Finally,a Support Vector Machine(SVM)is used to classify and recognize characters.The results show that this method can accurately recognize the surface characters of chips and meet the requirements of chip quality inspection.

A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion

This paper analyzes the progress of handwritten Chinese character recognition technology,from two perspectives:traditional recognition methods and deep learning-based recognition methods.Firstly,the complexity of Chinese character recognition is pointed out,including its numerous categories,complex structure,and the problem of similar characters,especially the variability of handwritten Chinese characters.Subsequently,recognition methods based on feature optimization,model optimization,and fusion techniques are highlighted.The fusion studies between feature optimization and model improvement are further explored,and these studies further enhance the recognition effect through complementary advantages.Finally,the article summarizes the current challenges of Chinese character recognition technology,including accuracy improvement,model complexity,and real-time problems,and looks forward to future research directions.

Anatomical and chemical characteristics associated with lodging resistance in wheat

Anatomical and chemical characteristics of stems affect lodging in wheat(Triticum aestivum L.) cultivars. Traits associated with lodging resistance, such as plant height, stem strength, culm wall thickness, pith diameter, and stem diameter, were extensively investigated in earlier studies. However, the solid stem trait was rarely considered. In this study, we measured a range of anatomical and chemical characteristics on solid and hollow stemmed wheat cultivars. Significant correlations were detected between resistance to lodging and several anatomical features, including width of mechanical tissue, weight of low internodes, and width of stem walls. Morphological features that gave the best indication of improved lodging resistance were increased stem width, width of mechanical tissue layer, and stem density. Multiple linear regression analysis showed that 99% of the variation in lodging resistance could be explained by the width of the mechanical tissue layer, suggesting that solid stemmed wheat has several anatomical features for increasing resistance to lodging. In addition, microsatellite markers GWM247 and GWM340 were linked to a single solid stem QTL on chromosome 3BL in a population derived from the cross Xinongshixin(solid stem)/Line 3159(hollow stem). These markers should be valuable in breeding wheat for solid stem.

Anatomical berry characteristics during the development of grape berries with different shapes

The aim of this study was to characterize the changes in berry anatomy during the development of grape(Vitis vinifera L.,and Vitis vinifera×Vitis labrusca)with different shapes.Paraffin sectioning was used to examine the structural parameters of the cells.The results revealed that,with the development of berries,the transverse and longitudinal diameters of the flesh cells gradually increased,revealing certain regularity.However,the transverse and longitudinal diameters of the epidermal and sub-epidermal cells were different between varieties,reflecting the specificity of the varieties.The growth of the transverse and longitudinal diameters of the berries was found to be completed in the early stage of development.A combination of correlation analysis and size analysis for each cell layer revealed that,due to the small crosssectional area of the epidermal and sub-epidermal cells,the influence of these cells on the transverse and the longitudinal diameters of the berries would be small.In conclusion,the longitudinal and transverse diameters of the grape berries were mainly determined by the longitudinal and transverse diameters of the flesh cells.The different shapes of the grape berries could mainly be attributed to the different growth rates of the flesh cells in the longitudinal and transverse directions.These different rates of growth led to different lengths and widths of the berry.

Anatomical and Chemical Characteristics of a Rolling Leaf Mutant of Rice and Its Ecophysiological Properties

The anatomical and chemical characteristics of a rolling leaf mutant （rlm） of rice （Oryza sativa L.） and its ecophysiological properties in photosynthesis and apoplastic transport were investigated. Compared with the wild type （WT）, the areas of whole vascular bundles and xylem as well as the ratios of xylem area/whole vascular bundles area and xylem area/phloem area were higher in rim, whereas the area and the width of foliar bulliform cell were lower. The Fourier transform infrared （FTIR） microspectroscopy spectra of foliar cell walls differed greatly between rim and WT. The rim exhibited lower protein and polysaccharide contents of foliar cell walls. An obvious reduction of pectin content was also found in rim by biochemical measurements. Moreover, the rate of photosynthesis was depressed while the conductance of stoma and the intercellular CO2 concentration were enhanced in rim. The PTS fluorescence, which represents the ability of apoplastic transport, was 11% higher in rim than in WT. These results suggest that the changes in anatomical and chemical characteristics of foliar vascular bundles, such as the reduction of proteins, pectins, and other polysaccharides of foliar cell walls, participate in the leaf rolling mutation, and consequently lead to the reduced photosynthetic dynamics and apoplastic transport ability in the mutant.

Growth changes and tissues anatomical characteristics of giant reed(Arundo donax L.) in soil contaminated with arsenic,cadmium and lead

A greenhouse experiment was conducted to elucidate the growth changes and tissues anatomical characteristics of giant reed(Arundo donax L.),a perennial rhizomatous grass,which was cultivated for 70 d in soils contaminated with As,Cd and Pb.The results show that giant reed rapidly grows with big biomass of shoots in contaminated soil,possessing strong metal-tolerance with limited metal translocation from roots to shoots.When As,Cd and Pb concentrations in the soil are less than 254,76.1 and 1 552 mg/kg,respectively,plant height and dried biomass are slightly reduced,the accumulation of As,Cd and Pb in shoots of giant reed is low while metal concentration in roots is high,and the anatomical characteristics of stem tissues are thick and homogeneous according to SEM images.However,plant height and dried biomass are significantly reduced and metal concentration in plant shoots and roots are significantly increased(P<0.05),the stems images become heterogeneous and the secretion in vascular bundles increases significantly when As,Cd and Pb concentrations in the soil exceed 334,101 and 2 052 mg/kg,respectively.The giant reed is a promising,naturally occurring plant with strong metal-tolerance,which can be cultivated in soils contaminated with multiple metals for ecoremediation purposes.