Precipitation types primarily include rainfall,snowfall,and sleet,and the transformation of precipitation types has significant impacts on regional climate,ecosystems,and the land-atmosphere system.This study employs ...Precipitation types primarily include rainfall,snowfall,and sleet,and the transformation of precipitation types has significant impacts on regional climate,ecosystems,and the land-atmosphere system.This study employs the Ding method to separate precipitation types from three datasets(CMFD,ERA5_Land,and CN05.1).Using data from 26meteorological observation stations in the Chinese Tianshan Mountains Region(CTMR)of China as the validation dataset,the precipitation type separation accuracy of three datasets was evaluated.Additionally,the impacts of relative humidity,precipitation amount,and air temperature on the accuracy of precipitation type separation were analyzed.The results indicate that the CMFD dataset provides the highest separation accuracy,followed by CN05.1,with ERA5_Land showing the poorest performance.Spatial correlation analysis reveals that CMFD outperforms the other two datasets at both annual and monthly scales.Root Mean Square Error(RMSE)and Mean Deviation(MD)values suggest that CMFD is more consistent with the station observational data.The analysis further demonstrates that relative humidity and precipitation amount significantly affect separation accuracy.After bias correction,the correlation coefficients between CMFD,ERA5_Land,and station observational data improved to 0.85-0.94,while the RMSE was controlled within 2 mm.The study also revealed that the overestimation of precipitation was positively correlated with the overestimation of rainfall days,negatively correlated with the overestimation of snowfall days,and that underestimated air temperatures led to an increase in the misclassification of snowfall days.This research provides a basis for selecting climate change datasets and managing water resources in alpine regions.展开更多
Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climat...Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.展开更多
Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analy...Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.展开更多
Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manife...Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manifestations of Western medicine include thirst,inability to drink more,diarrhea,yellow urine,red tongue,et al.)internalized disease.Nevertheless,the mechanism of EZECD on damp-heat internalized Type 2 diabetes(T2D)remains unknown.We employed data mining,pharmacology databases and experimental verification to study how EZECD treats damp-heat internalized T2D.Methods:The main compounds or genes of EZECD and damp-heat internalized T2D were obtained from the pharmacology databases.Succeeding,the overlapped targets of EZECD and damp-heat internalized T2D were performed by the Gene Ontology,kyoto encyclopedia of genes and genomes analysis.And the compound-disease targets-pathway network were constructed to obtain the hub compound.Moreover,the hub genes and core related pathways were mined with weighted gene co-expression network analysis based on Gene Expression Omnibus database,the capability of hub compound and genes was valid in AutoDock 1.5.7.Furthermore,and violin plot and gene set enrichment analysis were performed to explore the role of hub genes in damp-heat internalized T2D.Finally,the interactions of hub compound and genes were explored using Comparative Toxicogenomics Database and quantitative polymerase chain reaction.Results:First,herb-compounds-genes-disease network illustrated that the hub compound of EZECD for damp-heat internalized T2D could be quercetin.Consistently,the hub genes were CASP8,CCL2,and AHR according to weighted gene co-expression network analysis.Molecular docking showed that quercetin could bind with the hub genes.Further,gene set enrichment analysis and Gene Ontology represented that CASP8,or CCL2,is negatively involved in insulin secretion response to the TNF or lipopolysaccharide process,and AHR or CCL2 positively regulated lipid and atherosclerosis,and/or including NOD-like receptor signaling pathway,and TNF signaling pathway.Ultimately,the quantitative polymerase chain reaction and western blotting analysis showed that quercetin could down-regulated the mRNA and protein experssion of CASP8,CCL2,and AHR.It was consistent with the results in Comparative Toxicogenomics Database databases.Conclusion:These results demonstrated quercetin could inhibit the expression of CASP8,CCL2,AHR in damp-heat internalized T2D,which improves insulin secretion and inhibits lipid and atherosclerosis,as well as/or including NOD-like receptor signaling pathway,and TNF signaling pathway,suggesting that EZECD may be more effective to treat damp-heat internalized T2D.展开更多
In this paper, we discuss some characteristic properties of partial abstract data type (PADT) and show the diffrence between PADT and abstract data type (ADT) in specification of programming language. Finally, we clar...In this paper, we discuss some characteristic properties of partial abstract data type (PADT) and show the diffrence between PADT and abstract data type (ADT) in specification of programming language. Finally, we clarify that PADT is necessary in programming language description.展开更多
Graphical abstracts(GAs)are emerging as a pivotal tool in medical literature,enhancing the dissemination and comprehension of complex clinical data through visual summaries.This editorial highlights the significant ad...Graphical abstracts(GAs)are emerging as a pivotal tool in medical literature,enhancing the dissemination and comprehension of complex clinical data through visual summaries.This editorial highlights the significant advantages of GAs,including improved clarity,increased reader engagement,and enhanced visibility of research findings.By transforming intricate scientific data into accessible visual formats,these abstracts facilitate quick and effective knowledge transfer,crucial in clinical decision-making and patient care.However,challenges such as potential data misrepresentation due to oversimplification,the skill gap in graphic design among researchers,and the lack of standardized creation guidelines pose barriers to their widespread adoption.Additionally,while software such as Adobe Illustrator,BioRender,and Canva are commonly employed to create these visuals,not all researchers may be proficient in their use.To address these issues,we recommend that academic journals establish clear guidelines and provide necessary design training to researchers.This proactive approach will ensure the creation of high-quality GAs,promote their standardization,and expand their use in clinical reporting,ultimately benefiting the medical community and improving healthcare outcomes.展开更多
Exponentiated Generalized Weibull distribution is a probability distribution which generalizes the Weibull distribution introducing two more shapes parameters to best adjust the non-monotonic shape. The parameters of ...Exponentiated Generalized Weibull distribution is a probability distribution which generalizes the Weibull distribution introducing two more shapes parameters to best adjust the non-monotonic shape. The parameters of the new probability distribution function are estimated by the maximum likelihood method under progressive type II censored data via expectation maximization algorithm.展开更多
DNA molecules are green materials with great potential for high-density and long-term data storage.However,the current data-writing process of DNA data storage via DNA synthesis suffers from high costs and the product...DNA molecules are green materials with great potential for high-density and long-term data storage.However,the current data-writing process of DNA data storage via DNA synthesis suffers from high costs and the production of hazards,limiting its practical applications.Here,we developed a DNA movable-type storage system that can utilize DNA fragments pre-produced by cell factories for data writing.In this system,these pre-generated DNA fragments,referred to herein as“DNA movable types,”are used as basic writing units in a repetitive way.The process of data writing is achieved by the rapid assembly of these DNA movable types,thereby avoiding the costly and environmentally hazardous process of de novo DNA synthesis.With this system,we successfully encoded 24 bytes of digital information in DNA and read it back accurately by means of high-throughput sequencing and decoding,thereby demonstrating the feasibility of this system.Through its repetitive usage and biological assembly of DNA movable-type fragments,this system exhibits excellent potential for writing cost reduction,opening up a novel route toward an economical and sustainable digital data-storage technology.展开更多
Restriction endonuclease analysis(REA),or restriction fragment length polymorphism(RFLP),was useful for identifying and determining the relatedness and putative identities of microbial strains(Tang et al.,1997)and for...Restriction endonuclease analysis(REA),or restriction fragment length polymorphism(RFLP),was useful for identifying and determining the relatedness and putative identities of microbial strains(Tang et al.,1997)and for characterizing and discriminating large numbers of samples inexpensively in the past。展开更多
Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial...Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.展开更多
During pre-clinical pharmacokinetic research, it is not easy to gather complete pharmacokinetic data in each animal. In some cases, an animal can only provide a single observation. Under this circumstance, it is not c...During pre-clinical pharmacokinetic research, it is not easy to gather complete pharmacokinetic data in each animal. In some cases, an animal can only provide a single observation. Under this circumstance, it is not clear how to utilize this data to estimate the pharmacokinetic parameters effectively. This study was aimed at comparing a new method to handle such single-observation-per-animal type data with the conventional method in estimating pharmacokinetic parameters. We assumed there were 15 animals within the study receiving a single dose by intravenous injection. Each animal provided one observation point. There were five time points in total, and each time point contained three measurements. The data were simulated with a one-compartment model with first-order elimination. The inter-individual variabilities （ⅡV） were set to 10%, 30% and 50% for both clearance （CL） and apparent volume of distribution （V）. A proportional model was used to describe the residual error, which was also set to 10%, 30% and 50%. Two methods （conventional method and the finite msampling method） to handle with the simulated single-observation-per-animal type data in estimating pharmacokinetic parameters were compared. The conventional method （MI） estimated pharmacokinetic parameters directly with original data, i.e., single-observation-per-animal type data. The finite resampling method （M2） was to expand original data to a new dataset by resampling original data with all kinds of combinations by time. After resampling, each individual in the new dataset contained complete pharmacokinetic data, i.e., in this study, there were 243 （C3^1×C3^1×C3^1×C3^1×C3^1） kinds of possible combinations and each of them was a virtual animal. The study was simulated 100 times by the NONMEM software. According to the results, parameter estimates of CL and V by M2 based on the simulated dataset were closer to their true values, though there was a small difference among different combinations of ⅡVs and the residual errors. In general, M2 was less advantageous over M1 when the residual error increased. It was also influenced by the levels of ⅡV as higher levels of IIV could lead to a decrease in the advantage of M2. However, M2 had no ability to estimate the ⅡV of parameters, nor did M1. The finite resampling method could provide more reliable results compared to the conventional method in estimating pharmacokinetic parameters with single-observation-per-animal type data. Compared to the inter-individual variability, the results of estimation were mainly influenced by the residual error.展开更多
We use the latest baryon acoustic oscillation and Union 2.1 type Ia supernova data to test the cosmic opacity between different redshift regions without assuming any cosmological models. It is found that the universe ...We use the latest baryon acoustic oscillation and Union 2.1 type Ia supernova data to test the cosmic opacity between different redshift regions without assuming any cosmological models. It is found that the universe may be opaque between the redshift regions 0.35 0.44, 0.44 0.57 and 0.6-0.73 since the best fit values of cosmic opacity in these regions are positive, while a transparent universe is favored in the redshift region 0.57-0.63. However, in general, a transparent universe is still consistent with observations at the lo confidence level.展开更多
To improve high quality and/or retain achieved high quality of an academic program, time to time evaluation for quality of each covered course is often an integrated aspect considered in reputed institutions, however,...To improve high quality and/or retain achieved high quality of an academic program, time to time evaluation for quality of each covered course is often an integrated aspect considered in reputed institutions, however, there has been little effort regarding humanities courses. This research article deals with analysis of evaluation data collected regarding humanities course from a College of Commerce & Economics, Mumbai, Maharashtra, India, on Likert type items. Appropriateness of one parametric measure and three non-parametric measures are discussed and used in this regard which could provide useful clues for educational policy planners. Keeping in view of the analytical results using these four measures, regardless of the threshold regarding satisfaction among students, overall performance of almost every subject has been un-satisfactory. There is a need to make a focused approach to take every course at the level of high performance. The inconsistency noticed under every threshold further revealed that under such poorly performing subjects globally, one needs to analyze merely at the global level item. Once the global level analysis reveals high performance of a course, then only item specific analysis may need to be focused to find out the items requiring further improvements.展开更多
Objective:To explore the clinical medication rule of Shao Zhengbin in the treatment of coronary heart disease with type 2 diabetes mellitus after PCI by using data mining technology.Methods:Shao Zhengbin was collected...Objective:To explore the clinical medication rule of Shao Zhengbin in the treatment of coronary heart disease with type 2 diabetes mellitus after PCI by using data mining technology.Methods:Shao Zhengbin was collected from January 2016 to may 2019 in the outpatient department of the First Affiliated Hospital of Anhui University of traditional Chinese medicine to treat patients with coronary heart disease combined with type 2 diabetes mellitus after PCI.The data base was established with Microsoft Excel 2016,SPSS statistic 24.0,SPSS modeler 18.0 computer software,and drug frequency analysis,high-frequency drug association rule analysis and clustering were carried out Analysis and factor analysis.Results:of the 133 prescriptions included in the study,86 Chinese herbs were involved,and the top 10 drugs were dangshen,Huangjing,Danshen,Gualou,chuanxiong,fried Atractylodes rhizome,Poria cocos,Fushen and Chenpi respectively;12 drug associations were generated by association analysis,including Huangjing,Huangjing,chuanxiong,dandelion,Dangshen and banpi;12 drug associations were obtained by cluster analysis,including Huangjing,huangxiong,Gualou and Huangqi There are 7 clustering formulas,such as Xia,Danshen,Fuling,Gualou,etc.Conclusion:Shao Zhengbin is good at the treatment of coronary heart disease combined with type 2 diabetes mellitus after PCI.展开更多
Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management.In order to improve the classification accuracy of forest types,Sentinel-1 and 2 data o...Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management.In order to improve the classification accuracy of forest types,Sentinel-1 and 2 data of Changbai Mountain protection development zone were selected,and combined with DEM to construct a multi-featured random forest type classification model incorporating fusing intensity,texture,spectral,vegetation index and topography information and using random forest Gini index(GI)for optimization.The overall accuracy of classification was 94.60%and the Kappa coefficient was 0.933.Comparing the classification results before and after feature optimization,it shows that feature optimization has a greater impact on the classification accuracy.Comparing the classification results of random forest,maximum likelihood method and CART decision tree under the same conditions,it shows that the random forest has a higher performance and can be applied to forestry research work such as forest resource survey and monitoring.展开更多
Type-I censoring mechanism arises when the number of units experiencing the event is random but the total duration of the study is fixed. There are a number of mathematical approaches developed to handle this type of ...Type-I censoring mechanism arises when the number of units experiencing the event is random but the total duration of the study is fixed. There are a number of mathematical approaches developed to handle this type of data. The purpose of the research was to estimate the three parameters of the Frechet distribution via the frequentist Maximum Likelihood and the Bayesian Estimators. In this paper, the maximum likelihood method (MLE) is not available of the three parameters in the closed forms;therefore, it was solved by the numerical methods. Similarly, the Bayesian estimators are implemented using Jeffreys and gamma priors with two loss functions, which are: squared error loss function and Linear Exponential Loss Function (LINEX). The parameters of the Frechet distribution via Bayesian cannot be obtained analytically and therefore Markov Chain Monte Carlo is used, where the full conditional distribution for the three parameters is obtained via Metropolis-Hastings algorithm. Comparisons of the estimators are obtained using Mean Square Errors (MSE) to determine the best estimator of the three parameters of the Frechet distribution. The results show that the Bayesian estimation under Linear Exponential Loss Function based on Type-I censored data is a better estimator for all the parameter estimates when the value of the loss parameter is positive.展开更多
Background:To systematically summarize and categorize the Chinese herbal medicine in the domestic traditional Chinese medicine(TCM)literature on type 2 diabetes mellitus(T2DM),in this paper,we mine traditional Chinese...Background:To systematically summarize and categorize the Chinese herbal medicine in the domestic traditional Chinese medicine(TCM)literature on type 2 diabetes mellitus(T2DM),in this paper,we mine traditional Chinese medicine data for relationships and provide for future practitioners and researchers.Methods:Taking randomized controlled trials on the treatment of T2DM in TCM as the research theme,we searched for full-text literature in three major clinical databases,including CNKI,Wan Fang,and VIP,published between 1990 and 2020.We then conducted frequency statistics,cluster analysis,association rules extraction,and principal component analysis based on a corpus of medical academic words extracted from 1116 research articles.Results:The most frequently used is Astragali Radix,and the most commonly used two-herb combination in T2DM treatment consisted of Coptidis Rhizoma and Moutan Cortex.Moutan Cortex,Alismatis Rhizoma,and Dioscoreae Rhizoma were the most frequently used three-herb combination.We found a“lung”and“liver”and“kidney”model and confirmed the value of classical meridian tropism theory and pattern identification.The treatment is mainly to fill deficiency and clear heat and consider water infiltration,dampness,blood circulation,and silt.Conclusion:This study provides an in-depth perspective on the TCM medication rules for T2DM and offers practitioners and researchers valuable information about the current status and frontier trends of TCM research on T2DM in terms of diagnosis and treatment.展开更多
基金financial support from the National Natural Sciences Foundation of China(42261026,and 42161025)the Open Foundation of Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone(XJYS0907-2023-01)。
文摘Precipitation types primarily include rainfall,snowfall,and sleet,and the transformation of precipitation types has significant impacts on regional climate,ecosystems,and the land-atmosphere system.This study employs the Ding method to separate precipitation types from three datasets(CMFD,ERA5_Land,and CN05.1).Using data from 26meteorological observation stations in the Chinese Tianshan Mountains Region(CTMR)of China as the validation dataset,the precipitation type separation accuracy of three datasets was evaluated.Additionally,the impacts of relative humidity,precipitation amount,and air temperature on the accuracy of precipitation type separation were analyzed.The results indicate that the CMFD dataset provides the highest separation accuracy,followed by CN05.1,with ERA5_Land showing the poorest performance.Spatial correlation analysis reveals that CMFD outperforms the other two datasets at both annual and monthly scales.Root Mean Square Error(RMSE)and Mean Deviation(MD)values suggest that CMFD is more consistent with the station observational data.The analysis further demonstrates that relative humidity and precipitation amount significantly affect separation accuracy.After bias correction,the correlation coefficients between CMFD,ERA5_Land,and station observational data improved to 0.85-0.94,while the RMSE was controlled within 2 mm.The study also revealed that the overestimation of precipitation was positively correlated with the overestimation of rainfall days,negatively correlated with the overestimation of snowfall days,and that underestimated air temperatures led to an increase in the misclassification of snowfall days.This research provides a basis for selecting climate change datasets and managing water resources in alpine regions.
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Fire season affects the dynamic changes of post-fire vegetation communities and carbon emissions.Analyzing its global patterns supports understanding of the ecological impacts of fires and responses of fires to climate change.Meteorological variables have been widely used to quantify fire season in current studies.However,their results can not be used to assess climate impacts on the seasonality of fire activities.Here we utilized satellite-based Moderate Resolution Imaging Spectroradiometer(MODIS)burned area data from 2001 to 2022 to identify global fire season types based on the number of peaks within a year.Using satellite data and innovatively processing the data to obtain a more accurate length of the fire season.We divided fire season types and examined the spatial distribution of fire season types across the Koppen-Geiger climate(KGC)zones.At a global scale,we identified three major fire season types,including unimodal(31.25%),bimodal(52.07%),and random(16.69%).The unimodal fire season primarily occurs in boreal and tropical regions lasting about 2.7 mon.In comparison,temperate ecosystems tend to have a longer fire season(3 mon)with two peaks throughout the year.The KGC zones show divergent contributions from the fire season types,indicating potential impacts of the climatic conditions on fire seasonality in these regions.
基金supported by the National Natural Science Foundation of China(No.U21B2062)the Natural Science Foundation of Hubei Province(No.2023AFB307)。
文摘Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.
基金supported by a grant from Hubei Key Laboratory of Diabetes and Angiopathy Program of Hubei University of Science and Technology(2020XZ10)Project of Education Commission of Hubei Province(B2022192).
文摘Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manifestations of Western medicine include thirst,inability to drink more,diarrhea,yellow urine,red tongue,et al.)internalized disease.Nevertheless,the mechanism of EZECD on damp-heat internalized Type 2 diabetes(T2D)remains unknown.We employed data mining,pharmacology databases and experimental verification to study how EZECD treats damp-heat internalized T2D.Methods:The main compounds or genes of EZECD and damp-heat internalized T2D were obtained from the pharmacology databases.Succeeding,the overlapped targets of EZECD and damp-heat internalized T2D were performed by the Gene Ontology,kyoto encyclopedia of genes and genomes analysis.And the compound-disease targets-pathway network were constructed to obtain the hub compound.Moreover,the hub genes and core related pathways were mined with weighted gene co-expression network analysis based on Gene Expression Omnibus database,the capability of hub compound and genes was valid in AutoDock 1.5.7.Furthermore,and violin plot and gene set enrichment analysis were performed to explore the role of hub genes in damp-heat internalized T2D.Finally,the interactions of hub compound and genes were explored using Comparative Toxicogenomics Database and quantitative polymerase chain reaction.Results:First,herb-compounds-genes-disease network illustrated that the hub compound of EZECD for damp-heat internalized T2D could be quercetin.Consistently,the hub genes were CASP8,CCL2,and AHR according to weighted gene co-expression network analysis.Molecular docking showed that quercetin could bind with the hub genes.Further,gene set enrichment analysis and Gene Ontology represented that CASP8,or CCL2,is negatively involved in insulin secretion response to the TNF or lipopolysaccharide process,and AHR or CCL2 positively regulated lipid and atherosclerosis,and/or including NOD-like receptor signaling pathway,and TNF signaling pathway.Ultimately,the quantitative polymerase chain reaction and western blotting analysis showed that quercetin could down-regulated the mRNA and protein experssion of CASP8,CCL2,and AHR.It was consistent with the results in Comparative Toxicogenomics Database databases.Conclusion:These results demonstrated quercetin could inhibit the expression of CASP8,CCL2,AHR in damp-heat internalized T2D,which improves insulin secretion and inhibits lipid and atherosclerosis,as well as/or including NOD-like receptor signaling pathway,and TNF signaling pathway,suggesting that EZECD may be more effective to treat damp-heat internalized T2D.
基金The Project Supported by National Natural Science Foundation of China
文摘In this paper, we discuss some characteristic properties of partial abstract data type (PADT) and show the diffrence between PADT and abstract data type (ADT) in specification of programming language. Finally, we clarify that PADT is necessary in programming language description.
文摘Graphical abstracts(GAs)are emerging as a pivotal tool in medical literature,enhancing the dissemination and comprehension of complex clinical data through visual summaries.This editorial highlights the significant advantages of GAs,including improved clarity,increased reader engagement,and enhanced visibility of research findings.By transforming intricate scientific data into accessible visual formats,these abstracts facilitate quick and effective knowledge transfer,crucial in clinical decision-making and patient care.However,challenges such as potential data misrepresentation due to oversimplification,the skill gap in graphic design among researchers,and the lack of standardized creation guidelines pose barriers to their widespread adoption.Additionally,while software such as Adobe Illustrator,BioRender,and Canva are commonly employed to create these visuals,not all researchers may be proficient in their use.To address these issues,we recommend that academic journals establish clear guidelines and provide necessary design training to researchers.This proactive approach will ensure the creation of high-quality GAs,promote their standardization,and expand their use in clinical reporting,ultimately benefiting the medical community and improving healthcare outcomes.
文摘Exponentiated Generalized Weibull distribution is a probability distribution which generalizes the Weibull distribution introducing two more shapes parameters to best adjust the non-monotonic shape. The parameters of the new probability distribution function are estimated by the maximum likelihood method under progressive type II censored data via expectation maximization algorithm.
基金supported by the National Key Research and Development Program of China(2018YFA0900100)the Natural Science Foundation of Tianjin,China(19JCJQJC63300)Tianjin University。
文摘DNA molecules are green materials with great potential for high-density and long-term data storage.However,the current data-writing process of DNA data storage via DNA synthesis suffers from high costs and the production of hazards,limiting its practical applications.Here,we developed a DNA movable-type storage system that can utilize DNA fragments pre-produced by cell factories for data writing.In this system,these pre-generated DNA fragments,referred to herein as“DNA movable types,”are used as basic writing units in a repetitive way.The process of data writing is achieved by the rapid assembly of these DNA movable types,thereby avoiding the costly and environmentally hazardous process of de novo DNA synthesis.With this system,we successfully encoded 24 bytes of digital information in DNA and read it back accurately by means of high-throughput sequencing and decoding,thereby demonstrating the feasibility of this system.Through its repetitive usage and biological assembly of DNA movable-type fragments,this system exhibits excellent potential for writing cost reduction,opening up a novel route toward an economical and sustainable digital data-storage technology.
基金supported by the National Natural Science Foundation of China (31570155 and 31370199)"Young Top-notch Talents" of the Guangdong Province Special Support Program (2014)+3 种基金the Excellent Young Teacher Training Plan of Guangdong Province (Yq2013039)the Guangzhou Healthcare Collaborative Innovation Major Project (201400000002)funded by the China Scholarship Council (CSC No. 201508440056) as a Visiting Scholar (2015-2016)supported by a summer research grant to D.S. from the Office of the Vice President for Research at George Mason University
文摘Restriction endonuclease analysis(REA),or restriction fragment length polymorphism(RFLP),was useful for identifying and determining the relatedness and putative identities of microbial strains(Tang et al.,1997)and for characterizing and discriminating large numbers of samples inexpensively in the past。
基金This research was supported by grants from the Polish National Science Centre(project numbers 2015/19/B/ST10/02158 and 2017/27/B/ST10/00297)The computations were partly performed in the PoznańSupercomputing and Networking Center(Grant No.331)We would like to thank the Polish Institute of Meteorology and Water Management-National Research Institute,for providing the radar-derived products.
文摘Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.
文摘During pre-clinical pharmacokinetic research, it is not easy to gather complete pharmacokinetic data in each animal. In some cases, an animal can only provide a single observation. Under this circumstance, it is not clear how to utilize this data to estimate the pharmacokinetic parameters effectively. This study was aimed at comparing a new method to handle such single-observation-per-animal type data with the conventional method in estimating pharmacokinetic parameters. We assumed there were 15 animals within the study receiving a single dose by intravenous injection. Each animal provided one observation point. There were five time points in total, and each time point contained three measurements. The data were simulated with a one-compartment model with first-order elimination. The inter-individual variabilities （ⅡV） were set to 10%, 30% and 50% for both clearance （CL） and apparent volume of distribution （V）. A proportional model was used to describe the residual error, which was also set to 10%, 30% and 50%. Two methods （conventional method and the finite msampling method） to handle with the simulated single-observation-per-animal type data in estimating pharmacokinetic parameters were compared. The conventional method （MI） estimated pharmacokinetic parameters directly with original data, i.e., single-observation-per-animal type data. The finite resampling method （M2） was to expand original data to a new dataset by resampling original data with all kinds of combinations by time. After resampling, each individual in the new dataset contained complete pharmacokinetic data, i.e., in this study, there were 243 （C3^1×C3^1×C3^1×C3^1×C3^1） kinds of possible combinations and each of them was a virtual animal. The study was simulated 100 times by the NONMEM software. According to the results, parameter estimates of CL and V by M2 based on the simulated dataset were closer to their true values, though there was a small difference among different combinations of ⅡVs and the residual errors. In general, M2 was less advantageous over M1 when the residual error increased. It was also influenced by the levels of ⅡV as higher levels of IIV could lead to a decrease in the advantage of M2. However, M2 had no ability to estimate the ⅡV of parameters, nor did M1. The finite resampling method could provide more reliable results compared to the conventional method in estimating pharmacokinetic parameters with single-observation-per-animal type data. Compared to the inter-individual variability, the results of estimation were mainly influenced by the residual error.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11175093,11222545,11435006 and 11375092the K.C.Wong Magna Fund of Ningbo University
文摘We use the latest baryon acoustic oscillation and Union 2.1 type Ia supernova data to test the cosmic opacity between different redshift regions without assuming any cosmological models. It is found that the universe may be opaque between the redshift regions 0.35 0.44, 0.44 0.57 and 0.6-0.73 since the best fit values of cosmic opacity in these regions are positive, while a transparent universe is favored in the redshift region 0.57-0.63. However, in general, a transparent universe is still consistent with observations at the lo confidence level.
文摘To improve high quality and/or retain achieved high quality of an academic program, time to time evaluation for quality of each covered course is often an integrated aspect considered in reputed institutions, however, there has been little effort regarding humanities courses. This research article deals with analysis of evaluation data collected regarding humanities course from a College of Commerce & Economics, Mumbai, Maharashtra, India, on Likert type items. Appropriateness of one parametric measure and three non-parametric measures are discussed and used in this regard which could provide useful clues for educational policy planners. Keeping in view of the analytical results using these four measures, regardless of the threshold regarding satisfaction among students, overall performance of almost every subject has been un-satisfactory. There is a need to make a focused approach to take every course at the level of high performance. The inconsistency noticed under every threshold further revealed that under such poorly performing subjects globally, one needs to analyze merely at the global level item. Once the global level analysis reveals high performance of a course, then only item specific analysis may need to be focused to find out the items requiring further improvements.
基金Clinical study on coronary artery intervention in patients with coronary heart disease complicated with type 2 diabetes mellitus by tonifying qi,nourishing Yin and eliminating phlegm and clearing collateralscirculation(No.2012ZY16).
文摘Objective:To explore the clinical medication rule of Shao Zhengbin in the treatment of coronary heart disease with type 2 diabetes mellitus after PCI by using data mining technology.Methods:Shao Zhengbin was collected from January 2016 to may 2019 in the outpatient department of the First Affiliated Hospital of Anhui University of traditional Chinese medicine to treat patients with coronary heart disease combined with type 2 diabetes mellitus after PCI.The data base was established with Microsoft Excel 2016,SPSS statistic 24.0,SPSS modeler 18.0 computer software,and drug frequency analysis,high-frequency drug association rule analysis and clustering were carried out Analysis and factor analysis.Results:of the 133 prescriptions included in the study,86 Chinese herbs were involved,and the top 10 drugs were dangshen,Huangjing,Danshen,Gualou,chuanxiong,fried Atractylodes rhizome,Poria cocos,Fushen and Chenpi respectively;12 drug associations were generated by association analysis,including Huangjing,Huangjing,chuanxiong,dandelion,Dangshen and banpi;12 drug associations were obtained by cluster analysis,including Huangjing,huangxiong,Gualou and Huangqi There are 7 clustering formulas,such as Xia,Danshen,Fuling,Gualou,etc.Conclusion:Shao Zhengbin is good at the treatment of coronary heart disease combined with type 2 diabetes mellitus after PCI.
基金Supported by projects of National Natural Science Foundation of China(Nos.42171407,42077242)Natural Science Foundation of Jilin Province(No.20210101098JC)+1 种基金Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,MNR(No.KF-2020-05-024)National Key R&D Program of China(No.2021YFD1500100).
文摘Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management.In order to improve the classification accuracy of forest types,Sentinel-1 and 2 data of Changbai Mountain protection development zone were selected,and combined with DEM to construct a multi-featured random forest type classification model incorporating fusing intensity,texture,spectral,vegetation index and topography information and using random forest Gini index(GI)for optimization.The overall accuracy of classification was 94.60%and the Kappa coefficient was 0.933.Comparing the classification results before and after feature optimization,it shows that feature optimization has a greater impact on the classification accuracy.Comparing the classification results of random forest,maximum likelihood method and CART decision tree under the same conditions,it shows that the random forest has a higher performance and can be applied to forestry research work such as forest resource survey and monitoring.
文摘Type-I censoring mechanism arises when the number of units experiencing the event is random but the total duration of the study is fixed. There are a number of mathematical approaches developed to handle this type of data. The purpose of the research was to estimate the three parameters of the Frechet distribution via the frequentist Maximum Likelihood and the Bayesian Estimators. In this paper, the maximum likelihood method (MLE) is not available of the three parameters in the closed forms;therefore, it was solved by the numerical methods. Similarly, the Bayesian estimators are implemented using Jeffreys and gamma priors with two loss functions, which are: squared error loss function and Linear Exponential Loss Function (LINEX). The parameters of the Frechet distribution via Bayesian cannot be obtained analytically and therefore Markov Chain Monte Carlo is used, where the full conditional distribution for the three parameters is obtained via Metropolis-Hastings algorithm. Comparisons of the estimators are obtained using Mean Square Errors (MSE) to determine the best estimator of the three parameters of the Frechet distribution. The results show that the Bayesian estimation under Linear Exponential Loss Function based on Type-I censored data is a better estimator for all the parameter estimates when the value of the loss parameter is positive.
基金supported by China’s National Key R&D Program,NO.2019YFC1709801.
文摘Background:To systematically summarize and categorize the Chinese herbal medicine in the domestic traditional Chinese medicine(TCM)literature on type 2 diabetes mellitus(T2DM),in this paper,we mine traditional Chinese medicine data for relationships and provide for future practitioners and researchers.Methods:Taking randomized controlled trials on the treatment of T2DM in TCM as the research theme,we searched for full-text literature in three major clinical databases,including CNKI,Wan Fang,and VIP,published between 1990 and 2020.We then conducted frequency statistics,cluster analysis,association rules extraction,and principal component analysis based on a corpus of medical academic words extracted from 1116 research articles.Results:The most frequently used is Astragali Radix,and the most commonly used two-herb combination in T2DM treatment consisted of Coptidis Rhizoma and Moutan Cortex.Moutan Cortex,Alismatis Rhizoma,and Dioscoreae Rhizoma were the most frequently used three-herb combination.We found a“lung”and“liver”and“kidney”model and confirmed the value of classical meridian tropism theory and pattern identification.The treatment is mainly to fill deficiency and clear heat and consider water infiltration,dampness,blood circulation,and silt.Conclusion:This study provides an in-depth perspective on the TCM medication rules for T2DM and offers practitioners and researchers valuable information about the current status and frontier trends of TCM research on T2DM in terms of diagnosis and treatment.
