In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of...In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.展开更多
In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic s...In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding distortions.For this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain.However,the distortion maps can be calculated for cover and stego images and the difference between them is significant.In this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence matrix.The proposed scheme first generates the corresponding distortion maps for cover and stego images.Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images.Finally,support vector machine,based on the gaussian kernel,is used to classify the features.Compared with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.展开更多
Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation...Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.展开更多
BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the ...BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.展开更多
AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix metho...AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.展开更多
BACKGROUND The most important consideration in determining treatment strategies for undifferentiated early gastric cancer(UEGC)is the risk of lymph node metastasis(LNM).Therefore,identifying a potential biomarker that...BACKGROUND The most important consideration in determining treatment strategies for undifferentiated early gastric cancer(UEGC)is the risk of lymph node metastasis(LNM).Therefore,identifying a potential biomarker that predicts LNM is quite useful in determining treatment.AIM To develop a machine learning(ML)-based integral procedure to construct the LNM gray-level co-occurrence matrix(GLCM)prediction model.METHODS We retrospectively selected 526 cases of UEGC confirmed through pathological examination after radical gastrectomy without endoscopic treatment in four tertiary hospitals between January 2015 to December 2021.We extracted GLCM-based features from grayscale images and applied ML to the classification of candidate predictive variables.The robustness and clinical utility of each model were evaluated based on the following factors:Receiver operating characteristic curve(ROC),decision curve analysis,and clinical impact curve.RESULTS GLCM-based feature extraction significantly correlated with LNM.The top 7 GLCM-based factors included inertia value 0°(IV_0),inertia value 45°(IV_45),inverse gap 0°(IG_0),inverse gap 45°(IG_45),inverse gap full angle(IG_all),Haralick 30°(Haralick_30),Haralick full angle(Haralick_all),and Entropy.The areas under the ROC curve(AUCs)of the random forest classifier(RFC)model,support vector machine,eXtreme gradient boosting,artificial neural network,and decision tree ranged from 0.805[95%confidence interval(CI):0.258-1.352]to 0.925(95%CI:0.378-1.472)in the training set and from 0.794(95%CI:0.237-1.351)to 0.912(95%CI:0.355-1.469)in the testing set,respectively.The RFC(training set:AUC:0.925,95%CI:0.378-1.472;testing set:AUC:0.912,95%CI:0.355-1.469)model that incorporates Entropy,Haralick_all,Haralick_30,IG_all,IG_45,IG_0,and IV_45 had the highest predictive accuracy.CONCLUSION The evaluation results indicate that the method of selecting radiological and textural features becomes more effective in the LNM discrimination against UEGC patients.Additionally,the MLbased prediction model developed using the RFC can be used to derive treatment options and identify LNM,which can hence improve clinical outcomes.展开更多
Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix...Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.展开更多
A leukocyte recognition system, as part of a differential blood counter system, is very important in hematology field. In this paper, the propose system aims to automatically classify the white blood cells (leukocytes...A leukocyte recognition system, as part of a differential blood counter system, is very important in hematology field. In this paper, the propose system aims to automatically classify the white blood cells (leukocytes) on a given microscopic image. The classifications of leukocytes are performed based on the combination of color and texture features of the blood cell images. The developed system classifies the leukocytes in one of the five categories (neutrophils, eosinophils, basophils, lymphocytes, and monocytes). In the preprocessing stage, the system starts with converting the microscopic images from Red Green Blue (RGB) color space to Hue Saturation Value (HSV) color space. Next, the system splits the Hue and Saturation features from the Value feature. For both Hue and Saturation features, the system processes their color information using the Feature Selection method and the Window Cropping method;while the Value feature is processed by its texture information using the Co-occurrence matrix method. The final recognition stage is performed using the Euclidean distance method. The combination of the Feature Selection and Co-occurrence Matrix methods gives the best overall recognition accuracies for classifying leukocyte images.展开更多
The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,exist...The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,existing assessment methods are often plagued by low accuracy and poor generalization,while fragment-based design strategies commonly fail to account for synergistic effects between structural units.Therefore,based on a small-scale sample set,this study developed a more efficient global predictive model for fungicidal activity—-named APPf—by integrating multi-scale feature screening methods and machine learning algorithms,which also accounts for synergistic effects among different structural fragments.We utilized three independent external test sets for model validation:External Test Set 1 for general validation,External Test Set 2 for comparison with existing models,and External Test Set 3 for disease-specific fungicide evaluation.On External Test Set 1,the APPf model achieved a precision of 0.6454,a recall of 0.8535,and an F1 score of 0.7350,demonstrating its robust predictive performance.It also exhibited strong enrichment capability for positive samples in External Test Set 2.For External Test Set 3,APPf achieved a prediction accuracy exceeding 80%for each disease,suggesting its promising potential in practical fungicide development.Furthermore,we quantified the contribution of molecular descriptors to the model predictions using SHAP value analysis and identified nHdNH and NssssNp as strong indicative features for predicting fungicidal activity,thereby enhancing the interpretability of the model.APPf has been deployed on a public web server(http://pesticides.cau.edu.cn/APPf),providing a user-friendly online prediction service to support the discovery of novel fungicides.Meanwhile,we employed a molecular fragmentation strategy to analyze the co-occurrence relationships between fragments in fungicides and constructed a network map of fragment co-occurrence associated with fungicidal activity.This study provides both an active fragment library and a global fungicide-likeness assessment tool for AI-based de novo molecular generation aimed at discovering novel fungicidal leads,which is expected to enhance the efficiency of developing new fungicides.展开更多
Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird ...Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird species co-occurrence in Ghana's Central Region over six months. Birds were sampled in 120 points across six different habitat types (farmland, forest reserve, urban area, coastal savannah, wetland, and mangrove), using the point-centred count technique. In total, 4060 individuals belonging to 216 species were recorded across all six habitat types. We found that co-occurring species were more similar in their foraging behaviour and habitat association. About 60% of the birds were found to co-occur randomly, 15% co-occurred negatively, and 25% co-occurred positively. Carnivores like the Black Heron (Egretta ardesiaca) and Spur-winged Lapwing (Vanellus spinosus) randomly co-occurred with other guild groups and were dominant in the mangroves and wetlands. Frugivores from forest reserves had only a 25% chance of randomly co-occurring with other birds and about a 60% chance of positively co-occurring with other birds. Our findings suggest that foraging guilds and habitat type are major factors driving bird co-occurrence and community assemblages in this West African suburban region.展开更多
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes ...Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.展开更多
We read with the great interest the study by Ababneh et al in which inducedmesenchymal stem cell-derived exosomes were shown to exhibit a stronger andmore sustained anti-proliferative effect by inducing a senescence-l...We read with the great interest the study by Ababneh et al in which inducedmesenchymal stem cell-derived exosomes were shown to exhibit a stronger andmore sustained anti-proliferative effect by inducing a senescence-like state withoutapoptosis.The results obtained by the authors highlight the features of theeffects of senescent drift induction in surrounding tissues.In the light of thesefindings,the role of the properties of extracellular matrix and cellular glycocalyxin responses of human tumors to therapy remain uninvestigated.These extracellularbarriers appear to be significant obstacles to effective cancer therapy,especiallyin relation to the use of unique properties of tumor microenvironment forthe immunotherapy-resistant cancer treatment.展开更多
The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color...The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color parameters,anthocyanin content,and overall polyphenol levels in the wine samples.The copigmentation effect of malvidin-3-Oglucoside and sinapic acid was further explored in model solution and through theoretical calculations.The results indicated that the addition of hydroxycinnamic acids significantly enhanced the wine's color with sinapic acid(before the fermentation)showing the most pronounced color protection effect.Compared to control samples,the addition of hydroxycinnamic acids resulted in a 36%increase in total phenolic content and a 28% increase in total anthocyanin content.Thermodynamic analysis revealed that the interaction between sinapic acid and malvidin-3-O-glucoside was spontaneous and exothermic.Theoretical studies identified hydrogen bonding(HB)and dispersion forces as the main primary stabilizing forces,with the carboxyl group of sinapic acid playing a critical role while the anthocyanin backbone also influenced the interaction.展开更多
The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix sp...The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.展开更多
Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces ...Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.展开更多
This study investigates the anisotropic thermal conductivity of aluminum matrix composites reinforced with graphene nano-plates(GNPs)and in situ ZrB_(2) nanoparticles,while simultaneously maintaining high strength and...This study investigates the anisotropic thermal conductivity of aluminum matrix composites reinforced with graphene nano-plates(GNPs)and in situ ZrB_(2) nanoparticles,while simultaneously maintaining high strength and toughness.A discontinuous layered GNPs-ZrB_(2)/AA6111 composite was prepared using in situ melt reactions and semi-solid stirring casting technology,combined with hot rolling deformation processing.Microstructural analysis revealed that the GNPs were aligned parallel to the rolling direction-transverse direction(RD-TD)plane,whereas the ZrB_(2) nanoparticles aggregated into cluster strips,collectively forming a discontinuous layered structure.This multilayer arrangement maximized the in-plane thermal conductivity of the GNPs.The tightly bonded GNP/Al interfaces with the locking of CuAl_(2) nanoparticles ensured that the GNPs fully exploited their high thermal conductivity.Therefore,the GNPs-ZrB_(2)/AA6111 composite achieved high in-plane thermal conductivity(230 W/(m·K)),which is higher than that of the matrix(206 W/(m·K)).The improved in-plane thermal conductivity is primarily attributed to the exceptionally high intrinsic in-plane thermal conductivity of the GNPs and their two-dimensional layered structure.However,the composite exhibited pronounced thermal conductivity anisotropy in the in-plane and through-plane directions.The reduced through-plane thermal conductivity is predominantly caused by the intrinsically low through-plane thermal conductivity of the GNPs and the increased interfacial thermal resistance from the additional grain boundaries.展开更多
Polymer matrix composites with high dielectric constants and low dielectric losses are in high demand for flexible electronics.However,simultaneously satisfying these requirements poses a significant scientific challe...Polymer matrix composites with high dielectric constants and low dielectric losses are in high demand for flexible electronics.However,simultaneously satisfying these requirements poses a significant scientific challenge owing to the intrinsic trade-off relationship.Herein,we utilized the in situ controllable reduction of graphene oxide(GO)within a poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene)(P(VDF-Tr FE-CFE))matrix to regulate the dielectric properties.The as-obtained composite exhibited a high relative dielectric constant of 1415coupled with a low loss tangent of 0.380 at 100 Hz.Experimental and theoretical studies indicate that the increased degree of electron conjugation and conductivity of the reduced GO(RGO)are responsible for the high-k.The constrained reduction degree of GO,combined with its homogeneous dispersion in the polymer matrix,effectively suppresses long-range charge carrier migration,thereby minimizing dielectric loss.This novel strategy could be successfully applied to both organic and aqueous systems.Furthermore,a high-performance flexible capacitive proximity sensor was exemplified by the optimization of both the dielectric layer and electrode pattern,exhibiting excellent sensitivity and stability.The fundamental mechanisms elucidated in this study provide crucial design principles for developing dielectric PMCs with tailored properties,thereby opening new avenues for advanced flexible electronic applications.展开更多
Peripheral nerve injury causes severe neuroinflammation and has become a global medical challenge.Previous research has demonstrated that porcine decellularized nerve matrix hydrogel exhibits excellent biological prop...Peripheral nerve injury causes severe neuroinflammation and has become a global medical challenge.Previous research has demonstrated that porcine decellularized nerve matrix hydrogel exhibits excellent biological properties and tissue specificity,highlighting its potential as a biomedical material for the repair of severe peripheral nerve injury;however,its role in modulating neuroinflammation post-peripheral nerve injury remains unknown.Here,we aimed to characterize the anti-inflammatory properties of porcine decellularized nerve matrix hydrogel and their underlying molecular mechanisms.Using peripheral nerve injury model rats treated with porcine decellularized nerve matrix hydrogel,we evaluated structural and functional recovery,macrophage phenotype alteration,specific cytokine expression,and changes in related signaling molecules in vivo.Similar parameters were evaluated in vitro using monocyte/macrophage cell lines stimulated with lipopolysaccharide and cultured on porcine decellularized nerve matrix hydrogel-coated plates in complete medium.These comprehensive analyses revealed that porcine decellularized nerve matrix hydrogel attenuated the activation of excessive inflammation at the early stage of peripheral nerve injury and increased the proportion of the M2 subtype in monocytes/macrophages.Additionally,porcine decellularized nerve matrix hydrogel negatively regulated the Toll-like receptor 4/myeloid differentiation factor 88/nuclear factor-κB axis both in vivo and in vitro.Our findings suggest that the efficacious anti-inflammatory properties of porcine decellularized nerve matrix hydrogel induce M2 macrophage polarization via suppression of the Toll-like receptor 4/myeloid differentiation factor 88/nuclear factor-κB pathway,providing new insights into the therapeutic mechanism of porcine decellularized nerve matrix hydrogel in peripheral nerve injury.展开更多
基金the Yunnan Applied Basic Research Projects(No.2016FD039)the Talent Cultivation Project in Yunnan Province(No.KKSY201503063)
文摘In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix(GLCM), has been proposed and used in automatic identification systems. However,according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor(KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.
基金This work is supported by the National Natural Science Foundation of China(No.U1736118)the Natural Science Foundation of Guangdong(No.2016A030313350)+3 种基金the Special Funds for Science and Technology Development of Guangdong(No.2016KZ010103)the Key Project of Scientific Research Plan of Guangzhou(No.201804020068)the Fundamental Research Funds for the Central Universities(No.16lgjc83 and No.17lgjc45)the Science and Technology Planning Project of Guangdong Province(Grant No.2017A040405051).
文摘In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security.In most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding distortions.For this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain.However,the distortion maps can be calculated for cover and stego images and the difference between them is significant.In this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence matrix.The proposed scheme first generates the corresponding distortion maps for cover and stego images.Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images.Finally,support vector machine,based on the gaussian kernel,is used to classify the features.Compared with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.
文摘Since the efficiency of treatment of thyroid disorder depends on the risk of malignancy, indeterminate follicular neoplasm (FN) images should be classified. The diagnosis process has been done by visual interpretation of experienced pathologists. However, it is difficult to separate the favor benign from borderline types. Thus, this paper presents a classification approach based on 3D nuclei model to classify favor benign and borderline types of follicular thyroid adenoma (FTA) in cytological specimens. The proposed method utilized 3D gray level co-occurrence matrix (GLCM) and random forest classifier. It was applied to 22 data sets of FN images. Furthermore, the use of 3D GLCM was compared with 2D GLCM to evaluate the classification results. From experimental results, the proposed system achieved 95.45% of the classification. The use of 3D GLCM was better than 2D GLCM according to the accuracy of classification. Consequently, the proposed method probably helps a pathologist as a prescreening tool.
文摘BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.
基金Supported by the Priming Scientific Research Foundation for the Junior Researcher in Beijing Tongren Hospital,Capital Medical University
文摘AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.
基金Supported by the General Project-Social Development Field of Shaanxi Province Science and Technology Department,No. 2021SF-313Innovation Capability Support Plan of Shaanxi Science and Technology Department-Science and Technology Innovation Team,No. 2020TD-048
文摘BACKGROUND The most important consideration in determining treatment strategies for undifferentiated early gastric cancer(UEGC)is the risk of lymph node metastasis(LNM).Therefore,identifying a potential biomarker that predicts LNM is quite useful in determining treatment.AIM To develop a machine learning(ML)-based integral procedure to construct the LNM gray-level co-occurrence matrix(GLCM)prediction model.METHODS We retrospectively selected 526 cases of UEGC confirmed through pathological examination after radical gastrectomy without endoscopic treatment in four tertiary hospitals between January 2015 to December 2021.We extracted GLCM-based features from grayscale images and applied ML to the classification of candidate predictive variables.The robustness and clinical utility of each model were evaluated based on the following factors:Receiver operating characteristic curve(ROC),decision curve analysis,and clinical impact curve.RESULTS GLCM-based feature extraction significantly correlated with LNM.The top 7 GLCM-based factors included inertia value 0°(IV_0),inertia value 45°(IV_45),inverse gap 0°(IG_0),inverse gap 45°(IG_45),inverse gap full angle(IG_all),Haralick 30°(Haralick_30),Haralick full angle(Haralick_all),and Entropy.The areas under the ROC curve(AUCs)of the random forest classifier(RFC)model,support vector machine,eXtreme gradient boosting,artificial neural network,and decision tree ranged from 0.805[95%confidence interval(CI):0.258-1.352]to 0.925(95%CI:0.378-1.472)in the training set and from 0.794(95%CI:0.237-1.351)to 0.912(95%CI:0.355-1.469)in the testing set,respectively.The RFC(training set:AUC:0.925,95%CI:0.378-1.472;testing set:AUC:0.912,95%CI:0.355-1.469)model that incorporates Entropy,Haralick_all,Haralick_30,IG_all,IG_45,IG_0,and IV_45 had the highest predictive accuracy.CONCLUSION The evaluation results indicate that the method of selecting radiological and textural features becomes more effective in the LNM discrimination against UEGC patients.Additionally,the MLbased prediction model developed using the RFC can be used to derive treatment options and identify LNM,which can hence improve clinical outcomes.
基金Open Fund of the Key Lab of the Ministry of Education for Image Information Processing and Intelligent Control,China(No.200702)
文摘Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.
文摘A leukocyte recognition system, as part of a differential blood counter system, is very important in hematology field. In this paper, the propose system aims to automatically classify the white blood cells (leukocytes) on a given microscopic image. The classifications of leukocytes are performed based on the combination of color and texture features of the blood cell images. The developed system classifies the leukocytes in one of the five categories (neutrophils, eosinophils, basophils, lymphocytes, and monocytes). In the preprocessing stage, the system starts with converting the microscopic images from Red Green Blue (RGB) color space to Hue Saturation Value (HSV) color space. Next, the system splits the Hue and Saturation features from the Value feature. For both Hue and Saturation features, the system processes their color information using the Feature Selection method and the Window Cropping method;while the Value feature is processed by its texture information using the Co-occurrence matrix method. The final recognition stage is performed using the Euclidean distance method. The combination of the Feature Selection and Co-occurrence Matrix methods gives the best overall recognition accuracies for classifying leukocyte images.
基金Foundation： Special program for public health of traditional Chinese medicine （Fiscal agency [ 2011 ] No.76） Special program for Chinese pharmaceutical industry （No.201207002）
基金the National Key R&D Program of China(No.2022YFD1700200).
文摘The development of fungicides is time-consuming and costly.Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate.However,existing assessment methods are often plagued by low accuracy and poor generalization,while fragment-based design strategies commonly fail to account for synergistic effects between structural units.Therefore,based on a small-scale sample set,this study developed a more efficient global predictive model for fungicidal activity—-named APPf—by integrating multi-scale feature screening methods and machine learning algorithms,which also accounts for synergistic effects among different structural fragments.We utilized three independent external test sets for model validation:External Test Set 1 for general validation,External Test Set 2 for comparison with existing models,and External Test Set 3 for disease-specific fungicide evaluation.On External Test Set 1,the APPf model achieved a precision of 0.6454,a recall of 0.8535,and an F1 score of 0.7350,demonstrating its robust predictive performance.It also exhibited strong enrichment capability for positive samples in External Test Set 2.For External Test Set 3,APPf achieved a prediction accuracy exceeding 80%for each disease,suggesting its promising potential in practical fungicide development.Furthermore,we quantified the contribution of molecular descriptors to the model predictions using SHAP value analysis and identified nHdNH and NssssNp as strong indicative features for predicting fungicidal activity,thereby enhancing the interpretability of the model.APPf has been deployed on a public web server(http://pesticides.cau.edu.cn/APPf),providing a user-friendly online prediction service to support the discovery of novel fungicides.Meanwhile,we employed a molecular fragmentation strategy to analyze the co-occurrence relationships between fragments in fungicides and constructed a network map of fragment co-occurrence associated with fungicidal activity.This study provides both an active fragment library and a global fungicide-likeness assessment tool for AI-based de novo molecular generation aimed at discovering novel fungicidal leads,which is expected to enhance the efficiency of developing new fungicides.
文摘Understanding the fundamental drivers of large-scale species co-occurrence is a critical issue in ecology and conservation research. Here, we assessed foraging guilds, habitat type and disturbances as drivers of bird species co-occurrence in Ghana's Central Region over six months. Birds were sampled in 120 points across six different habitat types (farmland, forest reserve, urban area, coastal savannah, wetland, and mangrove), using the point-centred count technique. In total, 4060 individuals belonging to 216 species were recorded across all six habitat types. We found that co-occurring species were more similar in their foraging behaviour and habitat association. About 60% of the birds were found to co-occur randomly, 15% co-occurred negatively, and 25% co-occurred positively. Carnivores like the Black Heron (Egretta ardesiaca) and Spur-winged Lapwing (Vanellus spinosus) randomly co-occurred with other guild groups and were dominant in the mangroves and wetlands. Frugivores from forest reserves had only a 25% chance of randomly co-occurring with other birds and about a 60% chance of positively co-occurring with other birds. Our findings suggest that foraging guilds and habitat type are major factors driving bird co-occurrence and community assemblages in this West African suburban region.
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(62376106)The Science and Technology Development Plan of Jilin Province(20250102212JC).
文摘Driven by advancements in mobile internet technology,images have become a crucial data medium.Ensuring the security of image information during transmission has thus emerged as an urgent challenge.This study proposes a novel image encryption algorithm specifically designed for grayscale image security.This research introduces a new Cantor diagonal matrix permutation method.The proposed permutation method uses row and column index sequences to control the Cantor diagonal matrix,where the row and column index sequences are generated by a spatiotemporal chaotic system named coupled map lattice(CML).The high initial value sensitivity of the CML system makes the permutation method highly sensitive and secure.Additionally,leveraging fractal theory,this study introduces a chaotic fractal matrix and applies this matrix in the diffusion process.This chaotic fractal matrix exhibits selfsimilarity and irregularity.Using the Cantor diagonal matrix and chaotic fractal matrix,this paper introduces a fast image encryption algorithm involving two diffusion steps and one permutation step.Moreover,the algorithm achieves robust security with only a single encryption round,ensuring high operational efficiency.Experimental results show that the proposed algorithm features an expansive key space,robust security,high sensitivity,high efficiency,and superior statistical properties for the ciphered images.Thus,the proposed algorithm not only provides a practical solution for secure image transmission but also bridges fractal theory with image encryption techniques,thereby opening new research avenues in chaotic cryptography and advancing the development of information security technology.
文摘We read with the great interest the study by Ababneh et al in which inducedmesenchymal stem cell-derived exosomes were shown to exhibit a stronger andmore sustained anti-proliferative effect by inducing a senescence-like state withoutapoptosis.The results obtained by the authors highlight the features of theeffects of senescent drift induction in surrounding tissues.In the light of thesefindings,the role of the properties of extracellular matrix and cellular glycocalyxin responses of human tumors to therapy remain uninvestigated.These extracellularbarriers appear to be significant obstacles to effective cancer therapy,especiallyin relation to the use of unique properties of tumor microenvironment forthe immunotherapy-resistant cancer treatment.
基金supported by the Key R&D Program of Shaanxi Province,China(2024NC-YBXM-146)the Xi’an Agricultural Technology Research and Development Project,China(24NYGG0048)+1 种基金the Key R&D Program of Xianyang,China(L2024-ZDYF-ZDYF-NY-0028)the National Foreign Expert Project of China(G2023172002L)。
文摘The effect of adding hydroxycinnamic acids(caffeic acid,sinapic acid,p-coumaric acid and chlorogenic acid)in Cabernet Sauvignon dry red wine before and after fermentation was investigated,taking into account the color parameters,anthocyanin content,and overall polyphenol levels in the wine samples.The copigmentation effect of malvidin-3-Oglucoside and sinapic acid was further explored in model solution and through theoretical calculations.The results indicated that the addition of hydroxycinnamic acids significantly enhanced the wine's color with sinapic acid(before the fermentation)showing the most pronounced color protection effect.Compared to control samples,the addition of hydroxycinnamic acids resulted in a 36%increase in total phenolic content and a 28% increase in total anthocyanin content.Thermodynamic analysis revealed that the interaction between sinapic acid and malvidin-3-O-glucoside was spontaneous and exothermic.Theoretical studies identified hydrogen bonding(HB)and dispersion forces as the main primary stabilizing forces,with the carboxyl group of sinapic acid playing a critical role while the anthocyanin backbone also influenced the interaction.
基金The National Natural Science Foundations of China (12202219)the Natural Science Foundations of Ningxia (2024AAC02009, 2023AAC05001)the Ningxia Youth Top Talents Training Project。
文摘The fast solution of linear equations has always been one of the hot spots in scientific computing.A kind of the diagonal matrix splitting iteration methods are provided,which is different from the classical matrix splitting methods.Taking the decomposition of the diagonal elements for coefficient matrix as the key point,some new preconditioners are constructed.Taking the tri-diagonal coefficient matrix as an example,the convergence domains and optimal relaxation factor of the new method are analyzed theoretically.The presented new iteration methods are applied to solve linear algebraic equations,even 2D and 3D diffusion problems with the fully implicit discretization.The results of numerical experiments are matched with the theoretical analysis,and show that the iteration numbers are reduced greatly.The superiorities of presented iteration methods exceed some classical iteration methods dramatically.
文摘Evaluating the adversarial robustness of classification algorithms in machine learning is a crucial domain.However,current methods lack measurable and interpretable metrics.To address this issue,this paper introduces a visual evaluation index named confidence centroid skewing quadrilateral,which is based on a classification confidence-based confusion matrix,offering a quantitative and visual comparison of the adversarial robustness among different classification algorithms,and enhances intuitiveness and interpretability of attack impacts.We first conduct a validity test and sensitive analysis of the method.Then,prove its effectiveness through the experiments of five classification algorithms including artificial neural network(ANN),logistic regression(LR),support vector machine(SVM),convolutional neural network(CNN)and transformer against three adversarial attacks such as fast gradient sign method(FGSM),DeepFool,and projected gradient descent(PGD)attack.
基金supported by the National Natural Science Foundation of China(Nos.52471156,U20A20274,and 52071158)the China Postdoctoral Science Foundation(Nos.2024M751173 and 2024M752703)+1 种基金the Jiangsu Funding Program for Excellent Postdoctoral Talent,China(No.2024ZB229)the Natural Science Foundation of Jiangsu Higher Education Institutions,China(No.24KJB430012).
文摘This study investigates the anisotropic thermal conductivity of aluminum matrix composites reinforced with graphene nano-plates(GNPs)and in situ ZrB_(2) nanoparticles,while simultaneously maintaining high strength and toughness.A discontinuous layered GNPs-ZrB_(2)/AA6111 composite was prepared using in situ melt reactions and semi-solid stirring casting technology,combined with hot rolling deformation processing.Microstructural analysis revealed that the GNPs were aligned parallel to the rolling direction-transverse direction(RD-TD)plane,whereas the ZrB_(2) nanoparticles aggregated into cluster strips,collectively forming a discontinuous layered structure.This multilayer arrangement maximized the in-plane thermal conductivity of the GNPs.The tightly bonded GNP/Al interfaces with the locking of CuAl_(2) nanoparticles ensured that the GNPs fully exploited their high thermal conductivity.Therefore,the GNPs-ZrB_(2)/AA6111 composite achieved high in-plane thermal conductivity(230 W/(m·K)),which is higher than that of the matrix(206 W/(m·K)).The improved in-plane thermal conductivity is primarily attributed to the exceptionally high intrinsic in-plane thermal conductivity of the GNPs and their two-dimensional layered structure.However,the composite exhibited pronounced thermal conductivity anisotropy in the in-plane and through-plane directions.The reduced through-plane thermal conductivity is predominantly caused by the intrinsically low through-plane thermal conductivity of the GNPs and the increased interfacial thermal resistance from the additional grain boundaries.
基金financially supported by the Innovation and Technology Commission of the Hong Kong SAR Government(No.MRP/020/21)Hong Kong Polytechnic University(No.847A)+1 种基金RI-Wear Seed Fund of Poly U(1-CD8J)Start-up Fund of Poly U(1-BD49)。
文摘Polymer matrix composites with high dielectric constants and low dielectric losses are in high demand for flexible electronics.However,simultaneously satisfying these requirements poses a significant scientific challenge owing to the intrinsic trade-off relationship.Herein,we utilized the in situ controllable reduction of graphene oxide(GO)within a poly(vinylidene fluoride-trifluoroethylene-chlorofluoroethylene)(P(VDF-Tr FE-CFE))matrix to regulate the dielectric properties.The as-obtained composite exhibited a high relative dielectric constant of 1415coupled with a low loss tangent of 0.380 at 100 Hz.Experimental and theoretical studies indicate that the increased degree of electron conjugation and conductivity of the reduced GO(RGO)are responsible for the high-k.The constrained reduction degree of GO,combined with its homogeneous dispersion in the polymer matrix,effectively suppresses long-range charge carrier migration,thereby minimizing dielectric loss.This novel strategy could be successfully applied to both organic and aqueous systems.Furthermore,a high-performance flexible capacitive proximity sensor was exemplified by the optimization of both the dielectric layer and electrode pattern,exhibiting excellent sensitivity and stability.The fundamental mechanisms elucidated in this study provide crucial design principles for developing dielectric PMCs with tailored properties,thereby opening new avenues for advanced flexible electronic applications.
基金supported by the Shenzhen Hong Kong Joint Funding Project,No.SGDX20230116093645007(to LY)the Shenzhen Science and Technology Innovation Committee International Cooperation Project,No.GJHZ20200731095608025(to LY)+7 种基金Shenzhen Development and Reform Commission’s Intelligent Diagnosis,Treatment and Prevention of Adolescent Spinal Health Public Service Platform,No.S2002Q84500835(to LY)Shenzhen Medical Research Fund,No.B2303005(to LY)Team-based Medical Science Research Program,No.2024YZZ02(to LY)Zhejiang Provincial Natural Science Foundation of China,No.LWQ20H170001(to RL)Basic Research Project of Shenzhen Science and Technology from Shenzhen Science and Technology Innovation Commission,No.JCYJ20210324103010029(to BY)Shenzhen Second People’s Hospital Clinical Research Fund of Guangdong Province High-level Hospital Construction Project,Nos.2023yjlcyj029(to BY),2023yjlcyj021(to LL)Guangdong Basic and Applied Basic Research Foundation,No.2022A1515110679(to LL)China Postdoctoral Science Foundation,No.2022M722203(to GL).
文摘Peripheral nerve injury causes severe neuroinflammation and has become a global medical challenge.Previous research has demonstrated that porcine decellularized nerve matrix hydrogel exhibits excellent biological properties and tissue specificity,highlighting its potential as a biomedical material for the repair of severe peripheral nerve injury;however,its role in modulating neuroinflammation post-peripheral nerve injury remains unknown.Here,we aimed to characterize the anti-inflammatory properties of porcine decellularized nerve matrix hydrogel and their underlying molecular mechanisms.Using peripheral nerve injury model rats treated with porcine decellularized nerve matrix hydrogel,we evaluated structural and functional recovery,macrophage phenotype alteration,specific cytokine expression,and changes in related signaling molecules in vivo.Similar parameters were evaluated in vitro using monocyte/macrophage cell lines stimulated with lipopolysaccharide and cultured on porcine decellularized nerve matrix hydrogel-coated plates in complete medium.These comprehensive analyses revealed that porcine decellularized nerve matrix hydrogel attenuated the activation of excessive inflammation at the early stage of peripheral nerve injury and increased the proportion of the M2 subtype in monocytes/macrophages.Additionally,porcine decellularized nerve matrix hydrogel negatively regulated the Toll-like receptor 4/myeloid differentiation factor 88/nuclear factor-κB axis both in vivo and in vitro.Our findings suggest that the efficacious anti-inflammatory properties of porcine decellularized nerve matrix hydrogel induce M2 macrophage polarization via suppression of the Toll-like receptor 4/myeloid differentiation factor 88/nuclear factor-κB pathway,providing new insights into the therapeutic mechanism of porcine decellularized nerve matrix hydrogel in peripheral nerve injury.
