Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to pred...Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.展开更多
BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation gr...BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.展开更多
Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT w...Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.展开更多
The generation method of the key stream and the structure of the algorithm determine the security of the cryptosystem.The classical chaotic map has simple dynamic behavior and few control parameters,so it is not suita...The generation method of the key stream and the structure of the algorithm determine the security of the cryptosystem.The classical chaotic map has simple dynamic behavior and few control parameters,so it is not suitable for modern cryptography.In this paper,we design a new 2D hyperchaotic system called 2D simple structure and complex dynamic behavior map(2D-SSCDB).The 2D-SSCDB has a simple structure but has complex dynamic behavior.The Lyapunov exponent verifies that the 2D-SSCDB has hyperchaotic behavior,and the parameter space in the hyperchaotic state is extensive and continuous.Trajectory analysis and some randomness tests verify that the 2D-SSCDB can generate random sequences with good performance.Next,to verify the excellent performance of the 2D-SSCDB,we use the 2D-SSCDB to generate a keystream for color image encryption.In the encryption algorithm,the encryption algorithm scrambles and diffuses simultaneously,increasing the cryptographic system’s security.The horizontal correlation,vertical correlation,and diagonal correlation of ciphertext are−0.0004,−0.0004 and 0.0007,respectively.The average information entropy of the ciphertext is 7.9993.In addition,the designed encryption algorithm reduces the correlation between the three channels of the color image.Security analysis shows that the color image encryption algorithm designed using 2DSSCDB has good security,can resist standard attack methods,and has high efficiency.展开更多
In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image gener...In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image generation,such as the loss of important information features during the encoding and decoding processes,as well as a lack of constraints during the training process.To address these issues and improve the quality of Pix2Pixgenerated images,this paper introduces two key enhancements.Firstly,to reduce information loss during encoding and decoding,we utilize the U-Net++network as the generator for the Pix2Pix model,incorporating denser skip-connection to minimize information loss.Secondly,to enhance constraints during image generation,we introduce a specialized discriminator designed to distinguish differential images,further enhancing the quality of the generated images.We conducted experiments on the facades dataset and the sketch portrait dataset from the Chinese University of Hong Kong to validate our proposed model.The experimental results demonstrate that our improved Pix2Pix model significantly enhances image quality and outperforms other models in the selected metrics.Notably,the Pix2Pix model incorporating the differential image discriminator exhibits the most substantial improvements across all metrics.An analysis of the experimental results reveals that the use of the U-Net++generator effectively reduces information feature loss,while the Pix2Pix model incorporating the differential image discriminator enhances the supervision of the generator during training.Both of these enhancements collectively improve the quality of Pix2Pix-generated images.展开更多
The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient ...The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.展开更多
Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images sti...Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images still a challenging problem.This paper develops a novel water index named as NDWI-MSI,combining a new normalized difference water index(NDWI)and a recently developed morphological shadow index(MSI),to delineate water bodies from eight-band WorldView-2 imagery.The newly available bands(e.g.coastal,yellow,red-edge,and near-infrared 2)of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations.Through our testing,a new NDWI is defined in this study.In addition,MSI,a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas.The NDWI-MSI is created by combining NDWI and MSI,which is able to highlight water bodies and simultaneously suppress shadow areas.In experiments,it is shown that the new water index can achieve better performance than traditional NDWI,and even supervised classifiers,for example,maximum likelihood classifier,and support vector machine.展开更多
目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨...目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。展开更多
新近,欧洲糖尿病预防指南与培训标准工作组(Development and Implementation of a European Guideline and Training Standards for Diabetes Prevention,IMAGE)颁布了2型糖尿病(T2DM)预防指南,其要点摘译如下:
Three-dimensional(3D)cell spheroid models combined with mass spectrometry imaging(MSI)enables innovative investigation of in vivo-like biological processes under different physiological and pathological conditions.Her...Three-dimensional(3D)cell spheroid models combined with mass spectrometry imaging(MSI)enables innovative investigation of in vivo-like biological processes under different physiological and pathological conditions.Herein,airflow-assisted desorption electrospray ionization-MSI(AFADESI-MSI)was coupled with 3D HepG2 spheroids to assess the metabolism and hepatotoxicity of amiodarone(AMI).High-coverage imaging of>1100 endogenous metabolites in hepatocyte spheroids was achieved using AFADESI-MSI.Following AMI treatment at different times,15 metabolites of AMI involved in Ndesethylation,hydroxylation,deiodination,and desaturation metabolic reactions were identified,and according to their spatiotemporal dynamics features,the metabolic pathways of AMI were proposed.Subsequently,the temporal and spatial changes in metabolic disturbance within spheroids caused by drug exposure were obtained via metabolomic analysis.The main dysregulated metabolic pathways included arachidonic acid and glycerophospholipid metabolism,providing considerable evidence for the mechanism of AMI hepatotoxicity.In addition,a biomarker group of eight fatty acids was selected that provided improved indication of cell viability and could characterize the hepatotoxicity of AMI.The combination of AFADESI-MSI and HepG2 spheroids can simultaneously obtain spatiotemporal information for drugs,drug metabolites,and endogenous metabolites after AMI treatment,providing an effective tool for in vitro drug hepatotoxicity evaluation.展开更多
Low-dimensional halide perovskites have become the most promising candidates for X-ray imaging,yet the issues of the poor chemical stability of hybrid halide perovskite,the high poisonousness of lead halides and the r...Low-dimensional halide perovskites have become the most promising candidates for X-ray imaging,yet the issues of the poor chemical stability of hybrid halide perovskite,the high poisonousness of lead halides and the relatively low detectivity of the lead-free halide perovskites which seriously restrain its commercialization.Here,we developed a solution inverse temperature crystal growth(ITCG)method to bring-up high quality Cs_(3)Cu_(2)I_(5)crystals with large size of centimeter order,in which the oleic acid(OA)is introduced as an antioxidative ligand to inhibit the oxidation of cuprous ions effieiently,as well as to decelerate the crystallization rate remarkalby.Based on these fine crystals,the vapor deposition technique is empolyed to prepare high quality Cs_(3)Cu_(2)I_(5)films for efficient X-ray imaging.Smooth surface morphology,high light yields and short decay time endow the Cs_(3)Cu_(2)I_(5)films with strong radioluminescence,high resolution(12 lp/mm),low detection limits(53 nGyair/s)and desirable stability.Subsequently,the Cs_(3)Cu_(2)I_(5)films have been applied to the practical radiography which exhibit superior X-ray imaging performance.Our work provides a paradigm to fabricate nonpoisonous and chemically stable inorganic halide perovskite for X-ray imaging.展开更多
Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,...Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,ALB has caused a large number of deaths of a local tree species Populus gansuensis.The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate.Therefore,the monitoring of the ALB infestation at the individual tree level in the landscape is necessary.Moreover,the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.Methods:Multispectral WorldView-2(WV-2)images and 5 tree physiological factors were collected as experimental materials.One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees.The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model.Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy.Finally,three machine learning algorithms,i.e.,Random Forest(RF),Support Vector Machine(SVM),Classification And Regression Tree(CART),were applied and compared to find the best classifier for predicting the damage stage of individual P.gansuensis.Results:The confusion matrix of RF achieved the highest overall classification accuracy(86.2%)and the highest Kappa index value(0.804),indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees.In addition,the canopy color was found to be positively correlated with P.gansuensis’damage stages.Conclusions:A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P.gansuensis infested with ALB.The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree.These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province,China.展开更多
基金National Key Research and Development Program of China(2022YFB3903302 and 2021YFC1809104)。
文摘Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.
基金the Fujian Province Clinical Key Specialty Construction Project,No.2022884Quanzhou Science and Technology Plan Project,No.2021N034S+1 种基金The Youth Research Project of Fujian Provincial Health Commission,No.2022QNA067Malignant Tumor Clinical Medicine Research Center,No.2020N090s.
文摘BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.
基金funded by the National Natural Science Foundation of China(42071300)the Fujian Province Natural Science(2020J01504)+4 种基金the China Postdoctoral Science Foundation(2018M630728)the Open Fund of Fujian Provincial Key Laboratory of Resources and Environment Monitoring&Sustainable Management and Utilization(ZD202102)the Program for Innovative Research Team in Science and Technology in Fujian Province University(KC190002)the Open Fund of University Key Lab of Geomatics Technology and Optimize Resources Utilization in Fujian Province(fafugeo201901)supported by the Research Project of Jinjiang Fuda Science and Education Park Development Center(2019-JJFDKY-17)。
文摘Biochemical components of Moso bamboo(Phyllostachys pubescens)are critical to physiological and ecological processes and play an important role in the material and energy cycles of the ecosystem.The coupled PROSPECT with SAIL(PROSAIL)radiative transfer model is widely used for vegetation biochemical component content inversion.However,the presence of leaf-eating pests,such as Pantana phyllostachysae Chao(PPC),weakens the performance of the model for estimating biochemical components of Moso bamboo and thus must be considered.Therefore,this study considered pest-induced stress signals associated with Sentinel-2A/B images and field data and established multiple sets of biochemical canopy reflectance look-up tables(LUTs)based on the PROSAIL framework by setting different parameter ranges according to infestation levels.Quantitative inversions of leaf area index(LAI),leaf chlorophyll content(LCC),and leaf equivalent water thickness(LEWT)were derived.The scale conversions from LCC to canopy chlorophyll content(CCC)and LEWT to canopy equivalent water thickness(CEWT)were calculated.The results showed that LAI,CCC,and CEWT were inversely related with PPC-induced stress.When applying multiple LUTs,the p-values were<0.01;the R2 values for LAI,CCC,and CEWT were 0.71,0.68,and 0.65 with root mean square error(RMSE)(normalized RMSE,NRMSE)values of 0.38(0.16),17.56μg cm-2(0.20),and 0.02 cm(0.51),respectively.Compared to the values obtained for the traditional PROSAIL model,for October,R2 values increased by 0.05 and 0.10 and NRMSE decreased by 0.09 and 0.02 for CCC and CEWT,respectively and RMSE decreased by 0.35μg cm-2 for CCC.The feasibility of the inverse strategy for integrating pest-induced stress factors into the PROSAIL model,while establishing multiple LUTs under different pest-induced damage levels,was successfully demonstrated and can potentially enhance future vegetation parameter inversion and monitoring of bamboo forest health and ecosystems.
基金Funds for New Generation Information Technology of the Industry-University-Research Innovation Foundation of China University(No.2020ITA03022).
文摘The generation method of the key stream and the structure of the algorithm determine the security of the cryptosystem.The classical chaotic map has simple dynamic behavior and few control parameters,so it is not suitable for modern cryptography.In this paper,we design a new 2D hyperchaotic system called 2D simple structure and complex dynamic behavior map(2D-SSCDB).The 2D-SSCDB has a simple structure but has complex dynamic behavior.The Lyapunov exponent verifies that the 2D-SSCDB has hyperchaotic behavior,and the parameter space in the hyperchaotic state is extensive and continuous.Trajectory analysis and some randomness tests verify that the 2D-SSCDB can generate random sequences with good performance.Next,to verify the excellent performance of the 2D-SSCDB,we use the 2D-SSCDB to generate a keystream for color image encryption.In the encryption algorithm,the encryption algorithm scrambles and diffuses simultaneously,increasing the cryptographic system’s security.The horizontal correlation,vertical correlation,and diagonal correlation of ciphertext are−0.0004,−0.0004 and 0.0007,respectively.The average information entropy of the ciphertext is 7.9993.In addition,the designed encryption algorithm reduces the correlation between the three channels of the color image.Security analysis shows that the color image encryption algorithm designed using 2DSSCDB has good security,can resist standard attack methods,and has high efficiency.
基金supported in part by the Xinjiang Natural Science Foundation of China(2021D01C078).
文摘In recent years,Pix2Pix,a model within the domain of GANs,has found widespread application in the field of image-to-image translation.However,traditional Pix2Pix models suffer from significant drawbacks in image generation,such as the loss of important information features during the encoding and decoding processes,as well as a lack of constraints during the training process.To address these issues and improve the quality of Pix2Pixgenerated images,this paper introduces two key enhancements.Firstly,to reduce information loss during encoding and decoding,we utilize the U-Net++network as the generator for the Pix2Pix model,incorporating denser skip-connection to minimize information loss.Secondly,to enhance constraints during image generation,we introduce a specialized discriminator designed to distinguish differential images,further enhancing the quality of the generated images.We conducted experiments on the facades dataset and the sketch portrait dataset from the Chinese University of Hong Kong to validate our proposed model.The experimental results demonstrate that our improved Pix2Pix model significantly enhances image quality and outperforms other models in the selected metrics.Notably,the Pix2Pix model incorporating the differential image discriminator exhibits the most substantial improvements across all metrics.An analysis of the experimental results reveals that the use of the U-Net++generator effectively reduces information feature loss,while the Pix2Pix model incorporating the differential image discriminator enhances the supervision of the generator during training.Both of these enhancements collectively improve the quality of Pix2Pix-generated images.
文摘The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.
文摘Land surface water mapping is one of the most important remote-sensing applications.However,water areas are spectrally similar and overlapped with shadow,making accurate water extraction from remote-sensing images still a challenging problem.This paper develops a novel water index named as NDWI-MSI,combining a new normalized difference water index(NDWI)and a recently developed morphological shadow index(MSI),to delineate water bodies from eight-band WorldView-2 imagery.The newly available bands(e.g.coastal,yellow,red-edge,and near-infrared 2)of WorldView-2 imagery provide more potential for constructing new NDWIs derived from various band combinations.Through our testing,a new NDWI is defined in this study.In addition,MSI,a recently developed automatic shadow extraction index from high-resolution imagery can be used to indicate shadow areas.The NDWI-MSI is created by combining NDWI and MSI,which is able to highlight water bodies and simultaneously suppress shadow areas.In experiments,it is shown that the new water index can achieve better performance than traditional NDWI,and even supervised classifiers,for example,maximum likelihood classifier,and support vector machine.
文摘目的探讨T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨损伤中的诊断价值。方法对26例关节软骨损伤患者行T_2 star mapping、T_1 images和3D DESS扫描,并将T_1 images、T_2 star mapping与3D DESS图像融合,评价患者股骨、胫骨、髌骨关节软骨损伤程度并与关节镜结果对比,计算融合伪彩图诊断软骨损伤的特异性、敏感性及与关节镜诊断结果一致性。结果 T_1 images-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为92.8%、93.0%、0.769,T_2 star mapping-3D DESS融合伪彩图诊断关节软骨损伤的敏感度、特异度及Kappa值分别为91.4%、94.2%、0.787。结论 T_2 star mapping、T_1 images与3D DESS融合伪彩图在关节软骨早期损伤评价上优于关节镜。
文摘新近,欧洲糖尿病预防指南与培训标准工作组(Development and Implementation of a European Guideline and Training Standards for Diabetes Prevention,IMAGE)颁布了2型糖尿病(T2DM)预防指南,其要点摘译如下:
基金funded by the National Natural Science Foundation of China(Grant No.:21874156)the Chinese Academy of Medical Science(CAMS)Innovation Fund for Medical Sciences(Grant No.:2021-1-I2M-028).
文摘Three-dimensional(3D)cell spheroid models combined with mass spectrometry imaging(MSI)enables innovative investigation of in vivo-like biological processes under different physiological and pathological conditions.Herein,airflow-assisted desorption electrospray ionization-MSI(AFADESI-MSI)was coupled with 3D HepG2 spheroids to assess the metabolism and hepatotoxicity of amiodarone(AMI).High-coverage imaging of>1100 endogenous metabolites in hepatocyte spheroids was achieved using AFADESI-MSI.Following AMI treatment at different times,15 metabolites of AMI involved in Ndesethylation,hydroxylation,deiodination,and desaturation metabolic reactions were identified,and according to their spatiotemporal dynamics features,the metabolic pathways of AMI were proposed.Subsequently,the temporal and spatial changes in metabolic disturbance within spheroids caused by drug exposure were obtained via metabolomic analysis.The main dysregulated metabolic pathways included arachidonic acid and glycerophospholipid metabolism,providing considerable evidence for the mechanism of AMI hepatotoxicity.In addition,a biomarker group of eight fatty acids was selected that provided improved indication of cell viability and could characterize the hepatotoxicity of AMI.The combination of AFADESI-MSI and HepG2 spheroids can simultaneously obtain spatiotemporal information for drugs,drug metabolites,and endogenous metabolites after AMI treatment,providing an effective tool for in vitro drug hepatotoxicity evaluation.
基金the financially support of the National Natural Science Foundation of China(12164051)the Joint Foundation of Provincial Science and Technology Department-Double First-class Construction of Yunnan University(2019FY003016)+4 种基金the Young Top Talent Project of Yunnan Province(YNWR-QNBJ-2018-229)the financially support by Yunnan Major Scientific and Technological Projects(202202AG050016)Advanced Analysis and Measurement Center of Yunnan University for the sample characterization service and the Postgraduate Research and Innovation Foundation of Yunnan University(2021Y036)the financially support of the National Natural Science Foundation of China(62064013)the Application Basic Research Project of Yunnan Province[2019FB130]。
文摘Low-dimensional halide perovskites have become the most promising candidates for X-ray imaging,yet the issues of the poor chemical stability of hybrid halide perovskite,the high poisonousness of lead halides and the relatively low detectivity of the lead-free halide perovskites which seriously restrain its commercialization.Here,we developed a solution inverse temperature crystal growth(ITCG)method to bring-up high quality Cs_(3)Cu_(2)I_(5)crystals with large size of centimeter order,in which the oleic acid(OA)is introduced as an antioxidative ligand to inhibit the oxidation of cuprous ions effieiently,as well as to decelerate the crystallization rate remarkalby.Based on these fine crystals,the vapor deposition technique is empolyed to prepare high quality Cs_(3)Cu_(2)I_(5)films for efficient X-ray imaging.Smooth surface morphology,high light yields and short decay time endow the Cs_(3)Cu_(2)I_(5)films with strong radioluminescence,high resolution(12 lp/mm),low detection limits(53 nGyair/s)and desirable stability.Subsequently,the Cs_(3)Cu_(2)I_(5)films have been applied to the practical radiography which exhibit superior X-ray imaging performance.Our work provides a paradigm to fabricate nonpoisonous and chemically stable inorganic halide perovskite for X-ray imaging.
基金supported by National Key Research&Development Program of China“Research on key technologies for prevention and control of major disasters in plantation”(Grant No.2018YFD0600200)Beijing’s Science and Technology Planning Project“Key technologies for prevention and control of major pests in Beijing ecological public welfare forests”(Grant Nos.Z191100008519004 and Z201100008020001).
文摘Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,ALB has caused a large number of deaths of a local tree species Populus gansuensis.The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate.Therefore,the monitoring of the ALB infestation at the individual tree level in the landscape is necessary.Moreover,the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.Methods:Multispectral WorldView-2(WV-2)images and 5 tree physiological factors were collected as experimental materials.One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees.The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model.Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy.Finally,three machine learning algorithms,i.e.,Random Forest(RF),Support Vector Machine(SVM),Classification And Regression Tree(CART),were applied and compared to find the best classifier for predicting the damage stage of individual P.gansuensis.Results:The confusion matrix of RF achieved the highest overall classification accuracy(86.2%)and the highest Kappa index value(0.804),indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees.In addition,the canopy color was found to be positively correlated with P.gansuensis’damage stages.Conclusions:A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P.gansuensis infested with ALB.The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree.These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province,China.
