Objective:Using Chinese patents in force to investigate the frequency and patterns of Chinese herbal extract combinations claiming to treat heart disease.Methods:Patent documents were retrieved from the official websi...Objective:Using Chinese patents in force to investigate the frequency and patterns of Chinese herbal extract combinations claiming to treat heart disease.Methods:Patent documents were retrieved from the official website of the State Intellectual Property Office of the People’s Republic China.Cluster,frequency,and fuzzy cluster analyses were applied.Results:A high number of patents in force included high-frequency herbs such as Salvia miltiorrhiza,Panax ginseng,and Panax notoginseng,as well as high-frequency herbal families such as Araliaceae,Leguminosae,Labiatae,and Umbelliferae.Herb pairs such as P.ginsengþOphiopogon japonicus,S.miltiorrhizaþDalbergia odorifera,and P.ginsengþSchisandra chinensis are also commonly used,as well as herbal family pairs such as AraliaceaeþLiliaceae,LauraceaeþLeguminosae,and AraliaceaeþSchisandraceae.Traditional treatment principles for preventing and treating heart diseases was most-commonly based on simultaneously treating the liver and heart and treating the lung and spleen secondarily for choosing herbal combinations.Conclusion:Most of the high-frequency Chinese herbs in the patents investigated belong to the high-frequency herbal families,and herb pairs were commonly selected to coincide with the commonly-used herbal family pairs.Low-frequency Chinese herbs were also used,but generally belonged to the high-frequency herbal families,and were therefore similar to the highfrequency herbs in terms of traditional categories of taste and channel entered.The results reflect the use of traditional principles of formula composition,and suggest that these principles may indeed be an effective guide for further research and development of Chinese herbal extract combinations to prevent and treat heart diseases.展开更多
Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively r...Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively realize these advantages,a fine-grained collection and analysis of smart meter data is essential.However,the high dimensionality and volume of such time-series present significant challenges,including increased computational load,data transmission overhead,latency,and complexity in real-time analysis.This study proposes a novel,computationally efficient framework for feature extraction and selection tailored to smart meter time-series data.The approach begins with an extensive offline analysis,where features are derived from multiple domains—time,frequency,and statistical—to capture diverse signal characteristics.Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization.The bestperforming fused features set undergoes further refinement using Analysis of Variance(ANOVA)to identify the most discriminative features.The mathematical models,used to compute the selected features,are optimized to extract them with computational efficiency during online processing.Moreover,a notable dimension reduction is secured which facilitates data storage,transmission,and post processing.Onward,a specifically designed LogitBoost(LB)based ensemble of Random Forest base learners is used for an automated classification.The proposed solution demonstrates a high classification accuracy(97.93%)for the case of nine-class problem and dimension reduction(17.33-fold)with minimal front-end computational requirements,making it well-suited for real-world applications in smart grid environments.展开更多
Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern re...Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern representation is designed which includes ontological concepts, neighboring-tree structures and soft constraints. An information-(extraction) inference engine based on hypothesis-generation and conflict-resolution is implemented. The proposed technique is successfully applied to an information extraction system for Chinese-language query front-end of a job-recruitment search engine.展开更多
Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition me...Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.展开更多
Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process...Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document.In this study,a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns.One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods.In this study,attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision,continuity and consistency.To investigate the efficiency of the proposed algorithm,the results of the study were evaluated with respect to the precision and recall criteria.The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.展开更多
Low-temperature composite insulation is commonly applied in high-temperature super-conducting apparatus while partial discharge(PD)is found to be an important indicator to reveal insulation statues.In order to extract...Low-temperature composite insulation is commonly applied in high-temperature super-conducting apparatus while partial discharge(PD)is found to be an important indicator to reveal insulation statues.In order to extract feature parameters of PD signals more effectively,a method combined variational mode decomposition with multi-scale entropy and image feature is proposed.Based on the simulated test platform,original and noisy signals of three typical PD defects were obtained and decomposed.Accordingly,relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram.Afterwards,new PD feature vectors were obtained by dimension reduction.Finally,effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour.Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.展开更多
Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated b...Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated by different electrode configurations under the excitation of power frequency AC voltage were carefully collected in this paper. Furthermore, the feature vectors of the corresponding fingerprint, contained in partial discharge data, were extracted by rigorous mathematical algorithms, and the artificial neural network was employed to realize the pattern recognition of partial discharge caused by the inhomogeneity of electric field intensity with different electrode configurations. The results indicate that the J<sub>4</sub> value in the space of 7 feature quantities is 1905.6, and the recognition rate is 100% when the hidden layer neuron of the network is 19. However, the J<sub>5</sub> value of 9 feature quantities is 1589.9, and the purpose of recognition has been achieved when the number of hidden layer neurons of the network is 6. Increasing the number of hidden layer neurons will only waste computing resources. Of course, PD information collection mode, feature quantity selection, optimal feature space composition, network structure and classification algorithm are the key to realizing PD fault intelligence identification.展开更多
The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduce...The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.展开更多
Background:The purpose of this case series is to evaluate the safety and efficacy of VisuMax®Circle patterns in eyes that have undergone small incision lenticule extraction,thus creating a flap to perform an enha...Background:The purpose of this case series is to evaluate the safety and efficacy of VisuMax®Circle patterns in eyes that have undergone small incision lenticule extraction,thus creating a flap to perform an enhancement procedure or residual lenticule extraction.Methods:This prospective,single center,case study series evaluated the use of a VisuMax®Circle pattern to create a corneal flap following small incision lenticule extraction.Patients were treated and followed at TRSC International LASIK Center(Bangkok,Thailand)for 3 months to assess the efficacy and safety of the procedure.Efficacy was determined by the surgeon’s ability to lift the created corneal flap.Results:The study enrolled 28 eyes.Twenty-seven underwent the VisuMax®Circle pattern procedure for refractive enhancement,and one for residual lenticule extraction.In 100%of cases(28 eyes)the lifting of the flap was possible,as planned.In all cases of refractive enhancement(27 eyes)by laser in situ keratomileusis(LASIK),the exposure of the stromal bed was sufficient for the necessary excimer laser ablation.No eyes lost two or more Snellen lines of corrected distance visual acuity(CDVA)and no procedure or flap-related complications or serious adverse events occurred.Conclusions:This initial case series demonstrates that VisuMax®Circle pattern is efficacious and a suitable method to create a corneal flap for enhancement,following small incision lenticule extraction.展开更多
A new approach of relation extraction is described in this paper. It adopts a bootstrap- ping model with a novel iteration strategy, which generates more precise examples of specific relation. Compared with previous m...A new approach of relation extraction is described in this paper. It adopts a bootstrap- ping model with a novel iteration strategy, which generates more precise examples of specific relation. Compared with previous methods, the proposed method has three main advantages: first, it needs less manual intervention; second, more abundant and reasonable information are introduced to represent a relation pattern; third, it reduces the risk of circular dependency occurrence in bootstrapping. Scalable evaluation methodology and metrics are developed for our task with comparable techniques over TianWang 100G corpus. The experimental results show that it can get 90% precision and have excellent expansibility.展开更多
In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extractin...In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.展开更多
OBJECTIVE:To observe and explore the effect of Fuling(Poria) in alleviating the spleen deficiency symptom pattern(SDSP).METHODS:We established an animal model of SDS in Sprague-Dawley(SD) rats by treating them with de...OBJECTIVE:To observe and explore the effect of Fuling(Poria) in alleviating the spleen deficiency symptom pattern(SDSP).METHODS:We established an animal model of SDS in Sprague-Dawley(SD) rats by treating them with deficiency-inducing factors,including irregular feeding and tail clamping.Mice were administered Fuling(Poria) and its extracts(raw/cooked powder,aqueous/alcohol extract) by gavage once a day for 21 d.The body weight,rectal temperature,and spleen and thymus organ coefficients were calculated.The levels of motilin(MTL),gastrin(GAS),aquaporin 2(AQP2),interleukin 2(IL-2),IL-4,and 5-hydroxytryptamine(5-HT) in the serum and the level of AQP2 in the kidneys were evaluated by enzyme-linked immunosorbent assay.RESULTS:Fuling(Poria) and its extracts did not change the body weight,rectal temperature,and organ coefficients of the spleen and thymus.However,it reduced the levels of MTL and GAS and increased the levels of IL-2 and AQP2.In addition,the levels of IL-4 and 5-HT showed no significant alteration.CONCLUSIONS:These results suggested the crucial function of Fuling(Poria) in SDSP,especially promoting digestive function and water metabolism.展开更多
文摘Objective:Using Chinese patents in force to investigate the frequency and patterns of Chinese herbal extract combinations claiming to treat heart disease.Methods:Patent documents were retrieved from the official website of the State Intellectual Property Office of the People’s Republic China.Cluster,frequency,and fuzzy cluster analyses were applied.Results:A high number of patents in force included high-frequency herbs such as Salvia miltiorrhiza,Panax ginseng,and Panax notoginseng,as well as high-frequency herbal families such as Araliaceae,Leguminosae,Labiatae,and Umbelliferae.Herb pairs such as P.ginsengþOphiopogon japonicus,S.miltiorrhizaþDalbergia odorifera,and P.ginsengþSchisandra chinensis are also commonly used,as well as herbal family pairs such as AraliaceaeþLiliaceae,LauraceaeþLeguminosae,and AraliaceaeþSchisandraceae.Traditional treatment principles for preventing and treating heart diseases was most-commonly based on simultaneously treating the liver and heart and treating the lung and spleen secondarily for choosing herbal combinations.Conclusion:Most of the high-frequency Chinese herbs in the patents investigated belong to the high-frequency herbal families,and herb pairs were commonly selected to coincide with the commonly-used herbal family pairs.Low-frequency Chinese herbs were also used,but generally belonged to the high-frequency herbal families,and were therefore similar to the highfrequency herbs in terms of traditional categories of taste and channel entered.The results reflect the use of traditional principles of formula composition,and suggest that these principles may indeed be an effective guide for further research and development of Chinese herbal extract combinations to prevent and treat heart diseases.
文摘Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids.It offers substantial benefits across social,environmental,and economic dimensions.To effectively realize these advantages,a fine-grained collection and analysis of smart meter data is essential.However,the high dimensionality and volume of such time-series present significant challenges,including increased computational load,data transmission overhead,latency,and complexity in real-time analysis.This study proposes a novel,computationally efficient framework for feature extraction and selection tailored to smart meter time-series data.The approach begins with an extensive offline analysis,where features are derived from multiple domains—time,frequency,and statistical—to capture diverse signal characteristics.Various feature sets are fused and evaluated using robust machine learning classifiers to identify the most informative combinations for automated appliance categorization.The bestperforming fused features set undergoes further refinement using Analysis of Variance(ANOVA)to identify the most discriminative features.The mathematical models,used to compute the selected features,are optimized to extract them with computational efficiency during online processing.Moreover,a notable dimension reduction is secured which facilitates data storage,transmission,and post processing.Onward,a specifically designed LogitBoost(LB)based ensemble of Random Forest base learners is used for an automated classification.The proposed solution demonstrates a high classification accuracy(97.93%)for the case of nine-class problem and dimension reduction(17.33-fold)with minimal front-end computational requirements,making it well-suited for real-world applications in smart grid environments.
文摘Traditional pattern representation in information extraction lack in the ability of representing domain-specific concepts and are therefore devoid of flexibility. To overcome these restrictions, an enhanced pattern representation is designed which includes ontological concepts, neighboring-tree structures and soft constraints. An information-(extraction) inference engine based on hypothesis-generation and conflict-resolution is implemented. The proposed technique is successfully applied to an information extraction system for Chinese-language query front-end of a job-recruitment search engine.
基金supported by the National Natural Science Foundation of China (Project No.72301293)。
文摘Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.
文摘Taking into account the increasing volume of text documents,automatic summarization is one of the important tools for quick and optimal utilization of such sources.Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document.In this study,a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns.One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods.In this study,attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision,continuity and consistency.To investigate the efficiency of the proposed algorithm,the results of the study were evaluated with respect to the precision and recall criteria.The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.
基金Chongqing Natural Science Fund,Grant/Award Number:cstc2018jcyjAX0295Chongqing Education Commission,Grant/Award Number:KJQN202001146National Natural Science Foundation of China,Grant/Award Number:52177129。
文摘Low-temperature composite insulation is commonly applied in high-temperature super-conducting apparatus while partial discharge(PD)is found to be an important indicator to reveal insulation statues.In order to extract feature parameters of PD signals more effectively,a method combined variational mode decomposition with multi-scale entropy and image feature is proposed.Based on the simulated test platform,original and noisy signals of three typical PD defects were obtained and decomposed.Accordingly,relative moments and grayscale co-occurrence matrix were employed for feature extraction by K-modal component diagram.Afterwards,new PD feature vectors were obtained by dimension reduction.Finally,effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour.Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.
文摘Five-electrode configurations were designed to simulate the distribution inhomogeneity of electric field intensities in the air-insulating medium, and the characteristic data waveforms of partial discharge generated by different electrode configurations under the excitation of power frequency AC voltage were carefully collected in this paper. Furthermore, the feature vectors of the corresponding fingerprint, contained in partial discharge data, were extracted by rigorous mathematical algorithms, and the artificial neural network was employed to realize the pattern recognition of partial discharge caused by the inhomogeneity of electric field intensity with different electrode configurations. The results indicate that the J<sub>4</sub> value in the space of 7 feature quantities is 1905.6, and the recognition rate is 100% when the hidden layer neuron of the network is 19. However, the J<sub>5</sub> value of 9 feature quantities is 1589.9, and the purpose of recognition has been achieved when the number of hidden layer neurons of the network is 6. Increasing the number of hidden layer neurons will only waste computing resources. Of course, PD information collection mode, feature quantity selection, optimal feature space composition, network structure and classification algorithm are the key to realizing PD fault intelligence identification.
文摘The motivation for this article is to propose new damage classifiers based on a supervised learning problem for locating and quantifying damage.A new feature extraction approach using time series analysis is introduced to extract damage-sensitive features from auto-regressive models.This approach sets out to improve current feature extraction techniques in the context of time series modeling.The coefficients and residuals of the AR model obtained from the proposed approach are selected as the main features and are applied to the proposed supervised learning classifiers that are categorized as coefficient-based and residual-based classifiers.These classifiers compute the relative errors in the extracted features between the undamaged and damaged states.Eventually,the abilities of the proposed methods to localize and quantify single and multiple damage scenarios are verified by applying experimental data for a laboratory frame and a four-story steel structure.Comparative analyses are performed to validate the superiority of the proposed methods over some existing techniques.Results show that the proposed classifiers,with the aid of extracted features from the proposed feature extraction approach,are able to locate and quantify damage;however,the residual-based classifiers yield better results than the coefficient-based classifiers.Moreover,these methods are superior to some classical techniques.
文摘Background:The purpose of this case series is to evaluate the safety and efficacy of VisuMax®Circle patterns in eyes that have undergone small incision lenticule extraction,thus creating a flap to perform an enhancement procedure or residual lenticule extraction.Methods:This prospective,single center,case study series evaluated the use of a VisuMax®Circle pattern to create a corneal flap following small incision lenticule extraction.Patients were treated and followed at TRSC International LASIK Center(Bangkok,Thailand)for 3 months to assess the efficacy and safety of the procedure.Efficacy was determined by the surgeon’s ability to lift the created corneal flap.Results:The study enrolled 28 eyes.Twenty-seven underwent the VisuMax®Circle pattern procedure for refractive enhancement,and one for residual lenticule extraction.In 100%of cases(28 eyes)the lifting of the flap was possible,as planned.In all cases of refractive enhancement(27 eyes)by laser in situ keratomileusis(LASIK),the exposure of the stromal bed was sufficient for the necessary excimer laser ablation.No eyes lost two or more Snellen lines of corrected distance visual acuity(CDVA)and no procedure or flap-related complications or serious adverse events occurred.Conclusions:This initial case series demonstrates that VisuMax®Circle pattern is efficacious and a suitable method to create a corneal flap for enhancement,following small incision lenticule extraction.
基金Supported by the National Natural Science Foundation of China (No.60503072, No.60575042 and No.60435020).
文摘A new approach of relation extraction is described in this paper. It adopts a bootstrap- ping model with a novel iteration strategy, which generates more precise examples of specific relation. Compared with previous methods, the proposed method has three main advantages: first, it needs less manual intervention; second, more abundant and reasonable information are introduced to represent a relation pattern; third, it reduces the risk of circular dependency occurrence in bootstrapping. Scalable evaluation methodology and metrics are developed for our task with comparable techniques over TianWang 100G corpus. The experimental results show that it can get 90% precision and have excellent expansibility.
文摘In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.
基金Supported by National Key Research and Development Program of China:Study on the Core Efficacy Evaluation of Fuling(Poria)(No.2017YFC1703005)。
文摘OBJECTIVE:To observe and explore the effect of Fuling(Poria) in alleviating the spleen deficiency symptom pattern(SDSP).METHODS:We established an animal model of SDS in Sprague-Dawley(SD) rats by treating them with deficiency-inducing factors,including irregular feeding and tail clamping.Mice were administered Fuling(Poria) and its extracts(raw/cooked powder,aqueous/alcohol extract) by gavage once a day for 21 d.The body weight,rectal temperature,and spleen and thymus organ coefficients were calculated.The levels of motilin(MTL),gastrin(GAS),aquaporin 2(AQP2),interleukin 2(IL-2),IL-4,and 5-hydroxytryptamine(5-HT) in the serum and the level of AQP2 in the kidneys were evaluated by enzyme-linked immunosorbent assay.RESULTS:Fuling(Poria) and its extracts did not change the body weight,rectal temperature,and organ coefficients of the spleen and thymus.However,it reduced the levels of MTL and GAS and increased the levels of IL-2 and AQP2.In addition,the levels of IL-4 and 5-HT showed no significant alteration.CONCLUSIONS:These results suggested the crucial function of Fuling(Poria) in SDSP,especially promoting digestive function and water metabolism.
