Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were ...Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.展开更多
Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental...Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.展开更多
Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the t...Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.展开更多
Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most re...Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model(DEM) and the Landsat Enhanced Thematic Mapper(ETM), respectively. These factors were contrasted for 334 soil samples(depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon(SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.展开更多
In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geo...In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geochemical modeling.Cluster analysis identified three main water types based on the major ion contents,where mineralization increased from group 1 to group 3.These groups were confirmed by FA/PCA,which demonstrated that groundwater quality is influenced by geochemical processes(water-rock interaction)and human practice(irrigation).The exponential semivariogram model WQI.Groundwater chemistry has a strong spatial structure for Mg,Na,Cl,and NO3,and a moderate spatial structure for EC,Ca,K,HCO3,and SO4.Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results.All water groups are supersaturated with respect to carbonate minerals,and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area.展开更多
Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A la...Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.展开更多
Population growth and expanding urbanization have caused persistent shortages and contamination of groundwater resources in Mali,Africa.The increase in groundwater salinity makes it more difficult for residents to obt...Population growth and expanding urbanization have caused persistent shortages and contamination of groundwater resources in Mali,Africa.The increase in groundwater salinity makes it more difficult for residents to obtain drinking water,it is necessary to clarify the causes and control factors of groundwater mineralization in Gao region,northern Mali.Based on the analysis of the hydrochemical composition of groundwater in 24 boreholes,Piper and Sch?eller diagrams,principal component analysis(PCA)and hierarchical cluster analysis(HCA)are used to carry out multivariate statistical analysis on the main ions.The results show that the groundwater samples are weakly alkaline,with pH values ranging from 5.83 to 8.40,and the average values of boreholes are 7.50,respectively.The average electrical conductivity(EC)value is 354.4(μS/cm),and the extreme value is between 124.0 and 1247(μS/cm).Water is usually mineralized and presents nine types of water phase.The three principal components explain 84.42%of the total variance for 13 parameters.The factor F1(58.85%),the factor F2(16.88%)and the factor F3(8.69%)present for the majority of the total data set.In addition,multivariate statistical analysis confirmed the genetic relationship among aquifers and identified three main clusters.Clustering related to groundwater mineralization(F1),clustering related to oxide reduction and iron enrichment(F2),and clustering of groundwater pollution caused by nitrate and magnesium(F3).We found that agriculture,weathering activities and dissolution of geological materials promote the mineralization of groundwater.Groundwater quality in the Gao region is becoming less and less potable because of increasing salinity.展开更多
Jinhongtang is a traditional Chinese medicine formula composed of Rheum palmatum L.stem,Sargentodoxa cuneata stem,and Taraxacum mongolicum and is used for the treatment of sepsis.However,quality assessment method for ...Jinhongtang is a traditional Chinese medicine formula composed of Rheum palmatum L.stem,Sargentodoxa cuneata stem,and Taraxacum mongolicum and is used for the treatment of sepsis.However,quality assessment method for Jinhongtang is not available.In present study,we developed a UFLC-MS/MS method to determine 16 analytes in 20 batches of home-made and commercial Jinhongtang.Multivariate statistical analysis revealed the significant differences in the quality of home-made and commercial Jinhongtang and the difference in the quality of home-made samples was more significant.The integrated strategy based on UFLC-MS/MS and multivariate statistical analysis provided a new basis for the overall quality assessment of Jinhongtang.展开更多
Physicochemical properties of banana flour （BF） were studied in two varieties （Cavendish and Dream） and two stages of ripeness （green and ripe）. BF＇s were analyzed for pH, total soluble solids （TSS）, water ho...Physicochemical properties of banana flour （BF） were studied in two varieties （Cavendish and Dream） and two stages of ripeness （green and ripe）. BF＇s were analyzed for pH, total soluble solids （TSS）, water holding capacity （WHC） and oil holding capacity （OHC） at 40℃, 60 ℃ and 80 ℃, color values L＊, a＊ and b＊, back extrusion force and viscosity. Physicochemical data were analyzed by Multivariate Analysis of Variance, discriminant analysis and cluster analysis. All statistical analyses showed that physicochemical properties of BF prepared from different variety and stage of ripeness were different from each other. Viscosity, WHC40, WHC60 and TSS were recommended methods for discrimination between banana flour prepared from the two varieties, whilst viscosity, WHC60 and WHC80 were suggested for differentiation of banana flour prepared using green and ripe banana.展开更多
[Objective] The study aimed to study the relationship between soil and environment on the basis of multivariate statistical analysis. [ Method] Through field investigation, sampling and laboratory analysis, we discuss...[Objective] The study aimed to study the relationship between soil and environment on the basis of multivariate statistical analysis. [ Method] Through field investigation, sampling and laboratory analysis, we discussed the relationship between soil properties and environmental factors in Mizhi County, North Shaanxi by using Canoco multivariate statistical analysis. [ Result]According to the effects of various environmental factors on soil properties, the influencing order of environmental factors was land use way 〉 vegetation type 〉 vegetation restoration years 〉 vegeta- tion coverage 〉 slope aspect 〉 gradient 〉 elevation. In a word, soil properties were significantly affected by land use way and vegetation type which were the most important environmental factors of soil properties in spatial variation, while vegetation restoration years were closely related to the ac- cumulation of soil nutrients. [ Condusion]The research could provide theoretical references for the construction of ecological environment in Loess Plateau of China.展开更多
Biology is a challenging and complicated mess. Understanding this challenging complexity is the realm of the biological sciences: Trying to make sense of the massive, messy data in terms of discovering patterns and re...Biology is a challenging and complicated mess. Understanding this challenging complexity is the realm of the biological sciences: Trying to make sense of the massive, messy data in terms of discovering patterns and revealing its underlying general rules. Among the most powerful mathematical tools for organizing and helping to structure complex, heterogeneous and noisy data are the tools provided by multivariate statistical analysis (MSA) approaches. These eigenvector/eigenvalue data-compression approaches were first introduced to electron microscopy (EM) in 1980 to help sort out different views of macromolecules in a micrograph. After 35 years of continuous use and developments, new MSA applications are still being proposed regularly. The speed of computing has increased dramatically in the decades since their first use in electron microscopy. However, we have also seen a possibly even more rapid increase in the size and complexity of the EM data sets to be studied. MSA computations had thus become a very serious bottleneck limiting its general use. The parallelization of our programs—speeding up the process by orders of magnitude—has opened whole new avenues of research. The speed of the automatic classification in the compressed eigenvector space had also become a bottleneck which needed to be removed. In this paper we explain the basic principles of multivariate statistical eigenvector-eigenvalue data compression;we provide practical tips and application examples for those working in structural biology, and we provide the more experienced researcher in this and other fields with the formulas associated with these powerful MSA approaches.展开更多
To assess the quality of groundwater resources, samples were collected from 22 points for mean annual water years of 2003 and 2015 (mean minimum and maximum water table), and 19 parameters were examined and calculated...To assess the quality of groundwater resources, samples were collected from 22 points for mean annual water years of 2003 and 2015 (mean minimum and maximum water table), and 19 parameters were examined and calculated. One of the objectives of this study was to evaluate the groundwater quality of the Ghaemshahr plain which includes the study of spatial and temporal changes of groundwater quality in different sectors and factors affecting it. In this study, combining statistical methods such as Pearson correlation coefficient, factor analysis, principal component analysis, and combined diagrams with hydrochemical methods are used to assess the chemical quality of groundwater. Samples were categorized by using cluster method and then the same samples were identified. Accordingly, samples were classified in four categories which represent the quality of groundwater in different districts. Factor analysis was used to identify the factors affecting the geochemical processes of the aquifer. Statistical methods showed that they can be used to complete the conventional methods in hydro-geochemistry as well as very precise results can be achieved. Based on the obtained results, saturation index of Ghaemshahr groundwater was super-saturated;and groundwater quality control of Ghaemshahr plain is hold by processes such as dissolution of halide (salt water intrusion of Caspian Sea and brackish fossil aquifers), calcite and dolomite (dissolution of limestone, dolomite, and marl in height), weathering sodium-rich plagioclases (clay minerals), and ion exchange.展开更多
Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conform...Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conformational variability of complexes and affects most 3D structure determination methods that rely on signal averaging. Here, an approach is described that allows sorting structural states based on a 3D statistical approach, the 3D sampling and classification (3D-SC) of 3D structures derived from single particles imaged by cryo electron microscopy (cryo-EM). The method is based on jackknifing & bootstrapping of 3D sub-ensembles and 3D multivariate statistical analysis followed by 3D classification. The robustness of the statistical sorting procedure is corroborated using model data from an RNA polymerase structure and experimental data from a ribosome complex. It allows resolving multiple states within heterogeneous complexes that thus become amendable for a structural analysis despite of their highly flexible nature. The method has important implications for high-resolution structural studies and allows describing structure ensembles to provide insights into the dynamics of multi-component macromolecular assemblies.展开更多
AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analy...AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.展开更多
In the present study, an ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry(UPLC-QTOF/MS) based chemical profiling approach to rapidly evaluate chemical diversity after co...In the present study, an ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry(UPLC-QTOF/MS) based chemical profiling approach to rapidly evaluate chemical diversity after codecocting of the combination of Aconitum carmichaeli Debx.(wu-tou in Chinese, WT) and Bletilla striata(Thunb.) Reichb.f.(bai-ji in Chinese, BJ) incompatible pair. Two different kinds of decoctions, namely WT-BJ mixed decoction: mixed water extract of each individual herbs, and WT-BJ co-decoction: water extract of mixed two constituent herbs, were prepared. Batches of these two kinds of decoction samples were subjected to UPLC-QTOF/MS analysis, the datasets of tR-m/z pairs, ion intensities and sample codes were processed with supervised orthogonal partial least squared discriminant analysis(OPLS-DA) to holistically compare the difference between these two kinds of decoction samples. Once a clear classification trend was found in score plot, extended statistical analysis was performed to generate S-plot, in which the variables(tR-m/z pair) contributing most to the difference were clearly depicted as points at the two ends of "S", and the components that correlate to these ions were regarded as the most changed components during co-decocting of the incompatible pair. The identities of the changed components can be identified by comparing the retention times and mass spectra with those of reference compounds and/or tentatively assigned by matching empirical molecular formulae with those of the known compounds published in the literatures. Using the proposed approach, global chemical difference was found between mixed decoction and co-decoction, and hypaconitine, mesaconitine, deoxyaconitine, aconitine, 10-OH-mesaconitine, 10-OH-aconitine and deoxyhypaconitine were identified as the most changed toxic components of the combination of WT-BJ incompatible pair during co-decocting. It is suggested that this newly established approach could be used to practically reveal the possible toxic components changed/increased of the herbal combination taboos, e.g. the Eighteen Incompatible Medications(Shi Ba Fan), in traditional Chinese medicines.展开更多
[Objective] The study aimed to compare several statistical analysis models for estimating the sugarcane （Saccharum spp.） genotypic stability. [Method] The data of sugarcane regional trials in Guangdong, in 2009 was ...[Objective] The study aimed to compare several statistical analysis models for estimating the sugarcane （Saccharum spp.） genotypic stability. [Method] The data of sugarcane regional trials in Guangdong, in 2009 was analyzed by three models respectively： Finlay and Wilkinson model： the additive main effects and multiplicative interaction （AMMI） model and linear regression-principal components analysis （LR- PCA） model, so as to compare the models. [Result] The Finlay and Wilkinson model was easier, but the analysis of the other two models was more comprehensive, and there was a bit difference between the additive main effects and multiplicative inter- action （AMMI） model and linear regression-principal components analysis （LR-PCA） model. [Conclusion] In practice, while the proper statistical method was usually con- sidered according to the different data, it should be also considered that the same data should be analyzed with different statistical methods in order to get a more reasonable result by comparison.展开更多
Based on tidal data statistical analysis for 20 years of Tanggu Marine Environmental Monitoring Station from 1991 to 2010, we concluded that an average of nearly 10 days of 100 cm above water increase took place at Ti...Based on tidal data statistical analysis for 20 years of Tanggu Marine Environmental Monitoring Station from 1991 to 2010, we concluded that an average of nearly 10 days of 100 cm above water increase took place at Tianjin coast every year. The maximum high tide and average tide of Tianjin coast occurred in summer and autumn, and the maximum water increase also occurred in summer and autumn. Days with water increase more than 100 cm mostly occurred in spring, autumn and winter. Then we summarized the causes of coastal storm surge disaster in Tianjin based on astronomical tide factors, meteorological factors, sea level rise, land subsidence, and geographic factors, et al. Finally, we proposed storm surge disaster prevention measures.展开更多
[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shan...[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shannon-Wiener diversity index, cluster analysis of multivariate statistical analysis and MDS （Non-matric Multi- dimentional Scaling）analysis were used to analyze biological data of phytoplankton, zooplankton and Zoobenthos collected from the representative municipal polluted river in Pearl River Delta. The sediment samples were also collected to determine. Pb, Cd, Hg, Cr, As, Cu, Ni, Zn, as well as CODe, and NH3-N of porewater. Hakanson potential ecological risk index method was used to evaluate the ecological risk. [Re- suit] Shannon-Wiener diversity index analysis results can not effectively reflect the difference of pollution status of various stations in heavy polluted area; despite the presence of some problems, multivariate analysis method is superior to the Shannon-Wiener diversity index method in biological monitoring of heavy polluted river in the city. [Conclusion] The paper provided theoretical basis for biological data analysis in heavy polluted area.展开更多
基金supported by the Major State Basic Research Development Program (No. 2010CB428800)the Geological Survey Projects Foundation of Institute of Hydrogeology and Environmental Geology (No. SK201308)
文摘Understanding the controlling factor of groundwater quality can enhance promoting sustainable development of groundwater resources. To this end, multivariate statistical analysis（MA） and hydrochemical analysis were introduced in this work. The results indicate that the canonical discriminant function with 7 parameters was established using the discriminant analysis（DA） method, which can afford 100% correct assignation according to the 3 different clusters（good water（GW）, poor water（PW）, and very poor water（VPW）） obtained from cluster analysis（CA）. According to factor analysis（FA）, 8 factors were extracted from 25 hydrochemical elements and account for 80.897% of the total data variance, suggesting that groundwater with higher concentrations of sodium, calcium, magnesium, chloride, and sulfate in southeastern study area are mainly affected by the natural process; the higher level of arsenic and chromium in groundwater extracted from northwestern part of study area are derived by industrial activities; domestic and agriculture sewage have important contribution to copper, iron, iodine, and phosphate in the northern study area. Therefore, this work can help identify the main controlling factor of groundwater quality in North China plain so as to make better and more informed decisions about how to achieve groundwater resources sustainable development.
基金supposed by the Program for Science and Technology of Shandong Province (2011GHY11521)the Department of Education of Shandong Province (No. J11LB07)the Natural Science Foundation of Qingdao City (Nos. 12-1-3-52-(1)-nsh and 12-1-4-16-(7)-jch)
文摘Meretricis concha is a kind of marine traditional Chinese medicine(TCM), and has been commonly used for the treatment of asthma and scald burns. In order to investigate the relationship between the inorganic elemental fingerprint and the geographical origin identification of Meretricis concha, the elemental contents of M. concha from five sampling points in Rushan Bay have been determined by means of inductively coupled plasma optical emission spectrometry(ICP-OES). Based on the contents of 14 inorganic elements(Al, As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, and Zn), the inorganic elemental fingerprint which well reflects the elemental characteristics was constructed. All the data from the five sampling points were discriminated with accuracy through hierarchical cluster analysis(HCA) and principle component analysis(PCA), indicating that a four-factor model which could explain approximately 80% of the detection data was established, and the elements Al, As, Cd, Cu, Ni and Pb could be viewed as the characteristic elements. This investigation suggests that the inorganic elemental fingerprint combined with multivariate statistical analysis is a promising method for verifying the geographical origin of M. concha, and this strategy should be valuable for the authenticity discrimination of some marine TCM.
基金Supported by the National Natural Science Foundation of China （No.60574047） and the Doctorate Foundation of the State Education Ministry of China （No.20050335018）.
文摘Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.
基金financial support of Isfahan University of Technology (IUT) for this research
文摘Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model(DEM) and the Landsat Enhanced Thematic Mapper(ETM), respectively. These factors were contrasted for 334 soil samples(depth of 0–30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon(SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.
文摘In this study,the analytical data set of 26 groundwater samples from the alluvial aquifer of Boumerzoug-E1 khroub valley has been processed simultaneously with Multivariate analysis,geostatistical modeling,WQI,and geochemical modeling.Cluster analysis identified three main water types based on the major ion contents,where mineralization increased from group 1 to group 3.These groups were confirmed by FA/PCA,which demonstrated that groundwater quality is influenced by geochemical processes(water-rock interaction)and human practice(irrigation).The exponential semivariogram model WQI.Groundwater chemistry has a strong spatial structure for Mg,Na,Cl,and NO3,and a moderate spatial structure for EC,Ca,K,HCO3,and SO4.Water quality maps generated using ordinary Kriging are consistent with the HCA and PCA results.All water groups are supersaturated with respect to carbonate minerals,and dissolution of kaolinite and Ca-smectite is one of the processes responsible for hydrochemical evolution in the area.
基金The authors would like to thank the Laboratory of Water Engineering,Fasa University for providing the facilities to perform this research.
文摘Groundwater is considered as one of the most important sources for water supply in Iran.The Fasa Plain in Fars Province,Southern Iran is one of the major areas of wheat production using groundwater for irrigation.A large population also uses local groundwater for drinking purposes.Therefore,in this study,this plain was selected to assess the spatial variability of groundwater quality and also to identify main parameters affecting the water quality using multivariate statistical techniques such as Cluster Analysis(CA),Discriminant Analysis(DA),and Principal Component Analysis(PCA).Water quality data was monitored at 22 different wells,for five years(2009-2014)with 10 water quality parameters.By using cluster analysis,the sampling wells were grouped into two clusters with distinct water qualities at different locations.The Lasso Discriminant Analysis(LDA)technique was used to assess the spatial variability of water quality.Based on the results,all of the variables except sodium absorption ratio(SAR)are effective in the LDA model with all variables affording 92.80%correct assignation to discriminate between the clusters from the primary 10 variables.Principal component(PC)analysis and factor analysis reduced the complex data matrix into two main components,accounting for more than 95.93%of the total variance.The first PC contained the parameters of TH,Ca2+,and Mg2+.Therefore,the first dominant factor was hardness.In the second PC,Cl-,SAR,and Na+were the dominant parameters,which may indicate salinity.The originally acquired factors illustrate natural(existence of geological formations)and anthropogenic(improper disposal of domestic and agricultural wastes)factors which affect the groundwater quality.
基金funded by the China's National Natural Science Foundation(No.41440027)。
文摘Population growth and expanding urbanization have caused persistent shortages and contamination of groundwater resources in Mali,Africa.The increase in groundwater salinity makes it more difficult for residents to obtain drinking water,it is necessary to clarify the causes and control factors of groundwater mineralization in Gao region,northern Mali.Based on the analysis of the hydrochemical composition of groundwater in 24 boreholes,Piper and Sch?eller diagrams,principal component analysis(PCA)and hierarchical cluster analysis(HCA)are used to carry out multivariate statistical analysis on the main ions.The results show that the groundwater samples are weakly alkaline,with pH values ranging from 5.83 to 8.40,and the average values of boreholes are 7.50,respectively.The average electrical conductivity(EC)value is 354.4(μS/cm),and the extreme value is between 124.0 and 1247(μS/cm).Water is usually mineralized and presents nine types of water phase.The three principal components explain 84.42%of the total variance for 13 parameters.The factor F1(58.85%),the factor F2(16.88%)and the factor F3(8.69%)present for the majority of the total data set.In addition,multivariate statistical analysis confirmed the genetic relationship among aquifers and identified three main clusters.Clustering related to groundwater mineralization(F1),clustering related to oxide reduction and iron enrichment(F2),and clustering of groundwater pollution caused by nitrate and magnesium(F3).We found that agriculture,weathering activities and dissolution of geological materials promote the mineralization of groundwater.Groundwater quality in the Gao region is becoming less and less potable because of increasing salinity.
基金The authors thank National Key Research and Development Program of China(2018YFC1705900)National Natural Science Foundation of China(No.81903706)+1 种基金Distinguished professor of Liaoning Province(XLYC2002008)Science Foundation of Department of Education of Liaoning Province(LZ2020054)for financial support.
文摘Jinhongtang is a traditional Chinese medicine formula composed of Rheum palmatum L.stem,Sargentodoxa cuneata stem,and Taraxacum mongolicum and is used for the treatment of sepsis.However,quality assessment method for Jinhongtang is not available.In present study,we developed a UFLC-MS/MS method to determine 16 analytes in 20 batches of home-made and commercial Jinhongtang.Multivariate statistical analysis revealed the significant differences in the quality of home-made and commercial Jinhongtang and the difference in the quality of home-made samples was more significant.The integrated strategy based on UFLC-MS/MS and multivariate statistical analysis provided a new basis for the overall quality assessment of Jinhongtang.
文摘Physicochemical properties of banana flour （BF） were studied in two varieties （Cavendish and Dream） and two stages of ripeness （green and ripe）. BF＇s were analyzed for pH, total soluble solids （TSS）, water holding capacity （WHC） and oil holding capacity （OHC） at 40℃, 60 ℃ and 80 ℃, color values L＊, a＊ and b＊, back extrusion force and viscosity. Physicochemical data were analyzed by Multivariate Analysis of Variance, discriminant analysis and cluster analysis. All statistical analyses showed that physicochemical properties of BF prepared from different variety and stage of ripeness were different from each other. Viscosity, WHC40, WHC60 and TSS were recommended methods for discrimination between banana flour prepared from the two varieties, whilst viscosity, WHC60 and WHC80 were suggested for differentiation of banana flour prepared using green and ripe banana.
基金Supported by the Scientific Research Foundation of Xianyang Normal University for Bringing in Talents(10XSYK104)
文摘[Objective] The study aimed to study the relationship between soil and environment on the basis of multivariate statistical analysis. [ Method] Through field investigation, sampling and laboratory analysis, we discussed the relationship between soil properties and environmental factors in Mizhi County, North Shaanxi by using Canoco multivariate statistical analysis. [ Result]According to the effects of various environmental factors on soil properties, the influencing order of environmental factors was land use way 〉 vegetation type 〉 vegetation restoration years 〉 vegeta- tion coverage 〉 slope aspect 〉 gradient 〉 elevation. In a word, soil properties were significantly affected by land use way and vegetation type which were the most important environmental factors of soil properties in spatial variation, while vegetation restoration years were closely related to the ac- cumulation of soil nutrients. [ Condusion]The research could provide theoretical references for the construction of ecological environment in Loess Plateau of China.
文摘Biology is a challenging and complicated mess. Understanding this challenging complexity is the realm of the biological sciences: Trying to make sense of the massive, messy data in terms of discovering patterns and revealing its underlying general rules. Among the most powerful mathematical tools for organizing and helping to structure complex, heterogeneous and noisy data are the tools provided by multivariate statistical analysis (MSA) approaches. These eigenvector/eigenvalue data-compression approaches were first introduced to electron microscopy (EM) in 1980 to help sort out different views of macromolecules in a micrograph. After 35 years of continuous use and developments, new MSA applications are still being proposed regularly. The speed of computing has increased dramatically in the decades since their first use in electron microscopy. However, we have also seen a possibly even more rapid increase in the size and complexity of the EM data sets to be studied. MSA computations had thus become a very serious bottleneck limiting its general use. The parallelization of our programs—speeding up the process by orders of magnitude—has opened whole new avenues of research. The speed of the automatic classification in the compressed eigenvector space had also become a bottleneck which needed to be removed. In this paper we explain the basic principles of multivariate statistical eigenvector-eigenvalue data compression;we provide practical tips and application examples for those working in structural biology, and we provide the more experienced researcher in this and other fields with the formulas associated with these powerful MSA approaches.
文摘To assess the quality of groundwater resources, samples were collected from 22 points for mean annual water years of 2003 and 2015 (mean minimum and maximum water table), and 19 parameters were examined and calculated. One of the objectives of this study was to evaluate the groundwater quality of the Ghaemshahr plain which includes the study of spatial and temporal changes of groundwater quality in different sectors and factors affecting it. In this study, combining statistical methods such as Pearson correlation coefficient, factor analysis, principal component analysis, and combined diagrams with hydrochemical methods are used to assess the chemical quality of groundwater. Samples were categorized by using cluster method and then the same samples were identified. Accordingly, samples were classified in four categories which represent the quality of groundwater in different districts. Factor analysis was used to identify the factors affecting the geochemical processes of the aquifer. Statistical methods showed that they can be used to complete the conventional methods in hydro-geochemistry as well as very precise results can be achieved. Based on the obtained results, saturation index of Ghaemshahr groundwater was super-saturated;and groundwater quality control of Ghaemshahr plain is hold by processes such as dissolution of halide (salt water intrusion of Caspian Sea and brackish fossil aquifers), calcite and dolomite (dissolution of limestone, dolomite, and marl in height), weathering sodium-rich plagioclases (clay minerals), and ion exchange.
文摘Heterogeneity of biological samples is usually considered a major obstacle for three-dimensional (3D) structure determination of macromolecular complexes. Heterogeneity may occur at the level of composition or conformational variability of complexes and affects most 3D structure determination methods that rely on signal averaging. Here, an approach is described that allows sorting structural states based on a 3D statistical approach, the 3D sampling and classification (3D-SC) of 3D structures derived from single particles imaged by cryo electron microscopy (cryo-EM). The method is based on jackknifing & bootstrapping of 3D sub-ensembles and 3D multivariate statistical analysis followed by 3D classification. The robustness of the statistical sorting procedure is corroborated using model data from an RNA polymerase structure and experimental data from a ribosome complex. It allows resolving multiple states within heterogeneous complexes that thus become amendable for a structural analysis despite of their highly flexible nature. The method has important implications for high-resolution structural studies and allows describing structure ensembles to provide insights into the dynamics of multi-component macromolecular assemblies.
基金Supported by the Central High Level Hospital Clinical Research Funding(No.BJ-2024-089).
文摘AIM:To explore the causal relationship between several possible behavioral factors and high myopia(HM)using multivariable Mendelian randomization(MVMR)approach and to find the mediators among them with mediation analysis.METHODS:The causal effects of several behavioral factors,including screen time,education time,time spent outdoors,and physical activity,on the risk of HM using univariable Mendelian randomization(MR)and MVMR analyses were first assessed.Genome-wide association study summary statistics of serum metabolites were also used in mediation analysis to determine the extent to which serum metabolites mediate the effects of behavioral factors on HM.RESULTS:MR analyses indicated that both increased time spent outdoors and a higher frequency of moderate physical activity significantly reduced the risk of HM.Further MVMR analysis confirmed that moderate physical activity independently contributed to a lower risk of HM.Additionally,MR analyses identified 13 serum metabolites significantly associated with HM,of which 12 were lipids and one was an amino acid derivative.Mediation analysis revealed that six lipid metabolites mediated the protective effects of moderate physical activity on HM,with the highest mediation proportion observed for 1-(1-enyl-palmitoyl)-GPC(p-16:0;30.83%).CONCLUSION:This study suggests that in addition to outdoor time,moderate physical activity habits may have an independent protective effect against HM and pointed to lipid metabolites as priority targets for the prevention due to low physical activity.These results emphasize the importance of physical activity and metabolic health in HM and underscore the need for further study of these complex associations.
基金supported by the National Basic Research Program of China("973Program)(No2011CB505304)the Youth Scientific Research Project of Anhui Academy of Medical Science(YKY2018003)
文摘In the present study, an ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry(UPLC-QTOF/MS) based chemical profiling approach to rapidly evaluate chemical diversity after codecocting of the combination of Aconitum carmichaeli Debx.(wu-tou in Chinese, WT) and Bletilla striata(Thunb.) Reichb.f.(bai-ji in Chinese, BJ) incompatible pair. Two different kinds of decoctions, namely WT-BJ mixed decoction: mixed water extract of each individual herbs, and WT-BJ co-decoction: water extract of mixed two constituent herbs, were prepared. Batches of these two kinds of decoction samples were subjected to UPLC-QTOF/MS analysis, the datasets of tR-m/z pairs, ion intensities and sample codes were processed with supervised orthogonal partial least squared discriminant analysis(OPLS-DA) to holistically compare the difference between these two kinds of decoction samples. Once a clear classification trend was found in score plot, extended statistical analysis was performed to generate S-plot, in which the variables(tR-m/z pair) contributing most to the difference were clearly depicted as points at the two ends of "S", and the components that correlate to these ions were regarded as the most changed components during co-decocting of the incompatible pair. The identities of the changed components can be identified by comparing the retention times and mass spectra with those of reference compounds and/or tentatively assigned by matching empirical molecular formulae with those of the known compounds published in the literatures. Using the proposed approach, global chemical difference was found between mixed decoction and co-decoction, and hypaconitine, mesaconitine, deoxyaconitine, aconitine, 10-OH-mesaconitine, 10-OH-aconitine and deoxyhypaconitine were identified as the most changed toxic components of the combination of WT-BJ incompatible pair during co-decocting. It is suggested that this newly established approach could be used to practically reveal the possible toxic components changed/increased of the herbal combination taboos, e.g. the Eighteen Incompatible Medications(Shi Ba Fan), in traditional Chinese medicines.
基金Supported by the Guangdong Technological Program (2009B02001002)the Special Funds of National Agricultural Department for Commonweal Trade Research (nyhyzx07-019)the Earmarked Fund for Modern Agro-industry Technology Research System~~
文摘[Objective] The study aimed to compare several statistical analysis models for estimating the sugarcane （Saccharum spp.） genotypic stability. [Method] The data of sugarcane regional trials in Guangdong, in 2009 was analyzed by three models respectively： Finlay and Wilkinson model： the additive main effects and multiplicative interaction （AMMI） model and linear regression-principal components analysis （LR- PCA） model, so as to compare the models. [Result] The Finlay and Wilkinson model was easier, but the analysis of the other two models was more comprehensive, and there was a bit difference between the additive main effects and multiplicative inter- action （AMMI） model and linear regression-principal components analysis （LR-PCA） model. [Conclusion] In practice, while the proper statistical method was usually con- sidered according to the different data, it should be also considered that the same data should be analyzed with different statistical methods in order to get a more reasonable result by comparison.
文摘Based on tidal data statistical analysis for 20 years of Tanggu Marine Environmental Monitoring Station from 1991 to 2010, we concluded that an average of nearly 10 days of 100 cm above water increase took place at Tianjin coast every year. The maximum high tide and average tide of Tianjin coast occurred in summer and autumn, and the maximum water increase also occurred in summer and autumn. Days with water increase more than 100 cm mostly occurred in spring, autumn and winter. Then we summarized the causes of coastal storm surge disaster in Tianjin based on astronomical tide factors, meteorological factors, sea level rise, land subsidence, and geographic factors, et al. Finally, we proposed storm surge disaster prevention measures.
基金Supported by National Natural Science Foundation of China(41001341)Natural Science Foundation of Guangdong Province(9152800001000007)+1 种基金Open Fund ofState Key Laboratory of Subtropical Building Science(2011KB12)Basic Scientific Research Expenses Project of Central Universities(2012ZM0082)~~
文摘[Objective] The plankton and macrobenthos samples in municipal polluted river were analyzed by different methods, so as to explore the method suitable for biological data analysis in heavy polluted area. [Method] Shannon-Wiener diversity index, cluster analysis of multivariate statistical analysis and MDS （Non-matric Multi- dimentional Scaling）analysis were used to analyze biological data of phytoplankton, zooplankton and Zoobenthos collected from the representative municipal polluted river in Pearl River Delta. The sediment samples were also collected to determine. Pb, Cd, Hg, Cr, As, Cu, Ni, Zn, as well as CODe, and NH3-N of porewater. Hakanson potential ecological risk index method was used to evaluate the ecological risk. [Re- suit] Shannon-Wiener diversity index analysis results can not effectively reflect the difference of pollution status of various stations in heavy polluted area; despite the presence of some problems, multivariate analysis method is superior to the Shannon-Wiener diversity index method in biological monitoring of heavy polluted river in the city. [Conclusion] The paper provided theoretical basis for biological data analysis in heavy polluted area.
