Multivariate Statistical Analysis Methods Research Articles

Investigation of dynamic changes in quality of small white apricot wine during fermentation

Dynamic quality changes of small white apricot wine during fermentation were evaluated by high performance liquid chromatography and gas chromatography-mass spectrometry technique combined with multivariate statistical analysis methods. During the fermentation process, the sugar content decreased rapidly, while the alcohol gradually accumulated, reaching 11.4 %vol. The phenolic compound content and antioxidant capacity were reduced after fermentation, but still stayed at a high value. Flavor compounds of small white apricot wine are constantly changing as the fermentation progresses. After fermentation, small white apricot wine produced 3-methyl-1-butanol, isoamyl acetate, ethyl caprylate, ethyl caprate, ethyl benzoate, linalool and eugenol, which gave rich banana, lemon, pineapple, citrus and other flavors. Small white apricot wine has a soft taste, golden color and mellow body, with a typical fruit wine flavor. This study provides a theoretical basis for the further processing and flavor improvement of small white apricot.

Applied multivariate statistical analysis as a tool for assessing groundwater reactions in the Niebla-Posadas aquifer, Spain

In the current context of population growth and climate change, it is essential to effectively manage groundwater resources, to improve their quality, and to determine the behaviour of certain contaminants. Groundwater quality can be worsened most often by anthropogenic factors but can also be altered by natural factors depending on the chemical signatures of water sources (i.e., hydrochemical reactions) as a result of mixing processes. In these cases, the use of mixing calculations and multivariate statistical analysis (MSA) methods is crucial for determining the reactions that occur, the origin and fate of the detected compounds, ions or parameters, and the behaviour of the system. Thus, these methods ascertain processes that affect the chemical composition (i.e., quality) of groundwater bodies, and this information is needed for designing groundwater management strategies that exploit aquifers in a sustainable way. However, these methods are rarely employed, as few investigations that consider them focus on urban aquifers. Here, mixing calculations and other MSA methods that consider major ions and environmental isotopes are utilized in an aquifer located in a rural area associated with the Niebla-Posadas aquifer, Spain, where groundwater quality has deteriorated due to geogenic factors. This study proves the usefulness of these methods for deriving essential information that is needed (1) to properly manage the exploitation of aquifers, (2) to avoid the deterioration of groundwater bodies, and (3) to identify the reasons behind poor groundwater quality.

Serum metabolic profiling of rats infected with Clonorchis sinensis using LC-MS/MS method.

Clonorchiasis is an important foodborne parasitic disease. The omics-based-techniques could illuminate parasite biology and further make innovations in the research for parasitic diseases. However, knowledge about the serum metabolic profiles and related metabolic pathways in clonorchiasis is very limited. A untargeted ultra-high performance liquid tandem chromatography quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS) was used to profile the serum metabolites of rats at both 4 and 8 weeks post infection (wpi) with Clonorchis sinensis (C. sinensis). Additionally, multivariate statistical analysis methods were employed to identify differential metabolites. Next, serum amino acids and phosphatidylcholines (PCs) levels were determined by targeted metabolomics analysis. A total of 10530 and 6560 ions were identified in ESI+ and ESI- modes. The levels of phosphatidylcholines, glycerophosphocholine and choline were significantly changed, with the shift in lipid metabolism. Significant changes were also observed in amino acids (isoleucine, valine, leucine, threonine, glutamate and glutamine). Targeted analysis showed that BCAAs (isoleucine, valine, leucine) levels significantly increased at 4 wpi and decreased at 8 wpi; threonine was increased at 8 wpi, whereas glutamate and glutamine showed a decreasing trend at 8 wpi. Additionally, the level of 17 PCs were significantly changed in infected rats. Marked metabolic pathways were involved in clonorchiasis, including glycerophospholipid metabolism, alanine, aspartate and glutamate metabolism, histidine metabolism and pyrimidine metabolism. These results show that C. sinensis infection can cause significant changes in the rat serum metabolism, especially in amino acids and lipids. The metabolic signature together with perturbations in metabolic pathways could provide more in depth understanding of clonorchiasis and further make potential therapeutic interventions.

The use of social media by Polish and Slovak students and their digital competences

In this study, the main goal is to indicate the differences between the use of SM by Polish and Slovak students and the relationship between the use of SM and digital competences. A questionnaire survey conducted among Polish and Slovak students of technical faculties was used to obtain the data. The analysis was performed using the methods of multivariate statistical analysis. The study was conducted in October-December 2018 in parallel in Poland and Slovakia. In the course of the study, 172 questionnaires were returned from students from Poland and 171 from students from Slovakia, representing a manoeuvrability of 49% and 65% respectively. The main finding of the research on the use of SM is the more frequent use of SM by Slovak students. The results showed that Twitter and Instagram (after Facebook, which was used by over 98% of respondents) are more often (significantly) used by Slovak students, and YouTube and WhatsApp by Polish students. Our results also show that information competences demonstrate much less correlation with the use of social media compared to IT competences. The presented research results aim to fill the gap focusing on finding a relationship between the use of SM and digital competences. This approach results primarily from the universal use of SM and the need to develop competences that are the basis for effective functioning in the professional and private sphere in the era of digitization.

Аналіз ресурсних та інвестиційно-інноваційних факторів розвитку галузевих структур

The article is aimed at solving the actual problem of studying the factors of internal and external environment of functioning of sectoral structures. The authors analyzed an aggregate of various indicators for assessing the factors of development of sectoral structures using methods of multivariate statistical analysis. The completeness of the theoretical description of objects and objectivity of conclusions during the study of the problem of factor analysis of functioning of the mechanism of management of sectoral structures has been achieved. The problem of analysis of resource and investment-innovation factors of development of sectoral structures using factor analysis is examined. It is proposed to quantify the efficiency of functioning of the mechanism of enterprise management to use indicators of assessment of resource and investment-innovation potentials, which should be considered as indicators of outputs of the production system, transformed and obtained as a result of the influence of the management mechanism. The analysis was carried out in two directions: resource and investment-innovation. A large number of indicators characterizing the state of the internal and external environment of enterprises were processed, and using the methods of factor analysis, they were replaced with a certain set of uncorrelated parameters, which allowed to reduce the dimension of the studied totality of data. The state of resource and investment-innovation potentials of enterprises was considered as a result of the influence of the management mechanism, taking into account industry affiliation, that is, separately for enterprises of mechanical engineering, light or food industries. As result of the analysis, the following general factors were formed for light industry enterprises: economic direction of capital use; efficiency of use of the labor and material resources; efficiency of use of the equity and borrowed capital; efficiency of use of the borrowed funds. For food industry enterprises, the following factors are formed: the economic direction of capital use; efficiency of the financial and production activities; the ability of the management mechanism to provide a stable amount of profit; efficiency of use of fixed assets. Within the terms of investment and innovation direction for mechanical engineering and light industry enterprises, the most important factor is that which can be interpreted as the efficiency of research activities, the less important factor – the direction of using own investments for innovation; of the smallest importance is the direction of using investment resources for the production of innovative products.

Solubility determination, dissolution properties and solid transformation of resmetirom (form A) in heptane and seven alcohols.

In this work, the solubility of resmetirom (form A) was initially measured in heptane and seven alcohol solvents by gravimetric methods. Then, the transformation temperature between form A and ethanol solvate was determined at 333.76 K. Subsequently, some commonly used models were applied to fit the solubility data, and it was found that the modified Apelblat equation and the Jouyban-Acree-van't Hoff (J-A-V) model achieved the highest correlation accuracy for those in mono-solvents and heptane + propanol, respectively. And the average relative deviation (ARD) values of models were less than 0.5%, indicating a good agreement with the experimental results. Additionally, through density functional theory calculation and the analysis of solvent parameters, it was observed that hydrogen-bonding played primary roles in the dissolution process of resmetirom. The multiple factors such as the polarity of solvent, active site interaction, the molecular size and free volume all affect the solubility of resmetirom. Furthermore, by comparing the experimental and simulated infrared spectra of form A and two alcohol solvates, five characteristic bands were selected for quantification. Partial least squares regression (PLSR), a multivariate statistical analysis method, was used to extract quantitative information. The quantitative analysis model was established based on specific wavelength intervals, which were associated with inter-molecular interactions. Combined with PLSR, a new high-precision quantitative method was established to study the solid transformation process between form A and solvates. From 303.15 to 323.15 K, the rate of transformation from form A to methanol solvate or ethanol solvate was decreased with increasing temperature, revealing that the transformation process was driven by the solubility difference between form A and solvates under the studied conditions. This research will definitely afford necessary solubility data and solvent selection for the design of the crystallization process of resmetirom (form A) in industry, and provide basic data for the production of resmetirom (form A) in the pharmaceutical industry.

Chemometrics of the Environment: Hydrochemical Characterization of Groundwater in Lioua Plain (North Africa) Using Time Series and Multivariate Statistical Analysis

This study aims to analyze the chemical composition of Lioua’s groundwater in order to determine the geological processes influencing the composition and origin of its chemical elements. Therefore, chemometrics techniques, such as multivariate statistical analysis (MSA) and time series methods (TSM) are used. Indeed, MSA includes a component analysis (PCA) and a cluster analysis (CA), while autocorrelation analysis (AA), supplemented by a simple spectral density analysis (SDA), is used for the TMS. PCA displays three main factors explaining a total variance (TV) of 85.01 %. Factors 1, 2, and 3 are 68.72%, 11.96%, and 8.89 % of TV, respectively. In the CA, total dissolved solids (TDS) and electrical conductivity (EC) controlled three groups. The elements SO42−, K+, and Ca2+ are closely related to TDS, the elements Na+, Cl−, and Mg2+ are closely related to CE, while HCO3− and NO3− indicate the dissociation of other chemical elements. AA shows a linear interrelationship of EC, Mg2+, Na+, K+, Cl−, and SO42−. However, NO3− and HCO3− indicate uncorrelated characteristics with other parameters. For SDA, the correlograms of Mg2+, Na+, K+, Cl−, and SO42− have a similar trend with EC. Nonetheless, pH, Ca2+, HCO3− and NO3− exhibit multiple peaks related to the presence of several distinct cyclic mechanisms. Using these techniques, the authors were able to draw the following conclusion: the geochemical processes impacting the chemical composition are (i) dissolution of evaporated mineral deposits, (ii) water–rock interaction, and (iii) evaporation process. In addition, the groundwater exhibits two bipolar characteristics, one recorded with negative and positive charges on pH and Ca+ and another recorded only with negative charges on HCO3− and NO3−. On the other hand, SO42−, K+, Ca2+, and TDS are the major predominant elements in the groundwater’s chemical composition. Chloride presence mainly increases the electrical conductivity of water. The lithological factor is dominant in the overall mineralization of the Plio Quaternary surface aquifer waters. The origins of HCO3− and NO3− are as follows: HCO3− has a carbonate origin, whereas NO3– has an anthropogenic origin. The salinity was affected by Mg2+, SO42−, Cl−, Na+, K+, and EC. Ca2+, HCO3−, and NO3− result from human activity such as the usage of fertilizers, the carbonate facies outcrops, and domestic sewage.

Study on the Influence of Mining Activities on the Quality of Deep Karst Groundwater Based on Multivariate Statistical Analysis and Hydrochemical Analysis.

Long-term mining activities have changed the hydrogeochemical evolution process of groundwater and threatened the safe use of groundwater. By using the methods of hydrochemistry and multivariate statistical analysis, this study determined the hydrogeochemical evolution mechanism affecting the quality of karst groundwater by analyzing the conventional hydrochemistry data of the karst groundwater of the Carboniferous Taiyuan Formation in Hengyuan Coal Mine in the recent 12 years. The results show that, under the disturbance of mining, the quality of karst groundwater in Taiyuan Formation is poor, mainly because the contents of Na++K+ and SO42- are too high to allow usage as drinking water. The reason for the high content of SO42- in karst groundwater lies in the dissolution of gypsum and the oxidation of pyrite, and the high content of Na++K+ lies in the cation exchange. Influenced by the stratum grouting, the circulation of karst groundwater is improved, the cation exchange is weakened, and the desulfurization is enhanced. In the future, it is predicted that the hydrochemical type of karst groundwater in Taiyuan Formation in the study area will evolve from SO4-Ca·Mg type to HCO3-Ca Mg type.

Synchrotron Radiation FTIR Microspectroscopy Study of Biomolecular Alterations in Vincristine-Treated WRL68 Cells at the Single-Cell Level.

The toxic effect of vincristine on hepatocytes has rarely been studied. Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy is a novel technique for investigating drug-cell interaction systems. In this research, the biomolecular alterations in WRL68 cells induced by vincristine treatment were investigated by SR-FTIR microspectroscopy and were further analyzed by multivariate statistical analysis and semiquantitative methods, including principal component analysis (PCA), orthogonal partial least square-discriminant analysis (OPLS-DA), and the peak area ratios of several characteristic IR bands. In vincristine-treated WRL68 cells, alterations in lipid structures and the presence of more long-chain fatty acids were found. A decrease in protein α-helical content relative to β-sheet structures in vincristine-treated WRL68 cells was identified. The nucleic acid content was decreased relative to that of lipids and proteins in WRL68 cells treated with vincristine. These results provide important information about the toxic effect of vincristine on normal liver cells. This research also provides a new approach to reveal the biomolecular alterations in drug-treated hepatocytes by combining SR-FTIR with multivariate statistical analysis and semiquantitative methods.

Classification of three types of ginseng samples based on ginsenoside profiles: appropriate data normalization improves the efficiency of multivariate analysis

BackgroundIt is well known that ginsenosides are the main active ingredients in ginseng, and they have also been important indexes for assessing the quality of ginseng. However, the absolute contents of ginsenosides in ginseng were shown to be varied with the origin, cultivated type, cultivated year and climate. It is a great challenge to distinguish the commercial types of ginsengs according to the content of one or several ginsenosides. MethodsThe common commercial types of ginsengs are white ginseng (WG), red ginseng (RG), American ginseng (AG). To clearly illustrate the differences among WG, RG and AG at the ginsenosides level, we established a strategy for the detection and identification of ginsenosides based on an optimized LC-Q-Orbitrap MS/MS method coupled with an in-house database of ginsenosides. Before and after the normalization, the ginsenosides datasheet was analyzed and compared using several state-of-the-art multivariate statistical analysis methods. ResultsHere, 81 ginsenosides were identified in different ginseng samples. The majority of the ginsenosides (59 in 81) were all shared by WG, RG and AG. When the shared ginsenosides datasheet was normalized by the level of ginsenoside Ro, our analysis strategy clearly divided the ginseng samples into three groups (i.e., WG, RG and AG groups). We found that the ginsenoside profiles in RG and WG were significantly different from those in AG. The potential markers and multivariate diagnostic models differentiating the three types of ginsengs were also indicated. ConclusionOur novel methodology based on ginsenoside profiles is more robust than existing methods, and data normalization is required to improve the efficiency of multivariate statistical analysis.

Characteristics analysis of water pollutants in Cihu Lake, China, based on a multivariate statistical analysis method

In order to understand the sources of pollutants and the temporal and spatial distribution characteristics of the water quality in Cihu Lake, China, the monitoring data of seven water quality indicators from 12 sampling sites from 2015 to 2019 were selected, and the temporal and spatial variation laws of the water quality and pollution sources were analyzed by the use of the multivariate statistical analysis method. The results show that nitrogen and phosphorus pollution in the lake is dominant. The average concentrations of total nitrogen (TN) and total phosphorus (TP) exceed the surface water quality Class III standards by 1.6 and 2.2 times, respectively. Spatially, the results of the cluster analysis showed that the water quality in Cihu Lake can be categorized into three regions: the northern half of the lake, the southern half of the lake, and the canal entering the lake. Temporally, the water quality in these three regions can be classified into three categories: March to May (the northern half of Cihu Lake), September to November (the southern half of Cihu Lake), and September (the canal entering Cihu Lake). The discriminant analysis results showed that NH3-N, TN, CODCr, and BOD5 are the main factors that affect the uneven spatial distribution of the water quality of Cihu Lake, while TN, DO, and CODMn are the main factors that affect the temporal difference in the northern half of Cihu Lake, and NH3-N, TP, CODCr, DO, CODMn, TN, and TP are the main factors affecting the temporal difference in the southern half of Cihu Lake and the canal entering Cihu Lake. It was found that the water pollution in the study area can be mainly attributed to the incoming water and urban domestic pollution. The main pollution sources for the canal entering Cihu Lake and the southern half of Cihu Lake are the water from the sewage treatment plant and the domestic sewage that has not been intercepted, while the northern half of Cihu Lake is mainly affected by surface runoff, mixed rainwater and sewage, and internal pollution.

Methods of Multivariate Statistical Analysis in the Study of the Efficiency of Initiative Budgeting at the Regional Level

The paper presents a methodology of statistical analysis to assess efficiency of initiative budgeting at the regional level, which is one of the most urgent areas for improvement of the mechanism for managing socio-economic development of territories. The introductory part of the article explains the relevance of applying mathematical and statistical methods of analysis to improve the efficiency of initiative budgeting in the regions, and also describes theoretical and methodological provisions and information sources implemented in the author's own methodological studies designed to assess the efficiency of initiative budgeting on the example of the Stavropol Territory – pilot region for the implementation of the Local Initiatives Support Program (LISP). While analyzing structural and dynamic relationships of indicators associated with financing and results of LISP implementation, the authors applied two versions of cluster analysis:– clustering of municipal units of the studied region by the values of indicators of financing and LISP performance, ensuring the allocation of statistically homogeneous groups of administrative and territorial entities;– clustering of indicators characterizing the volume of funding and LISP performance at the municipal level, which gave an idea of the multi-level interdependence of these indicators and was taken into account when building multi-factor models of the effectiveness of initiative budgeting programs in the region.The article concludes that the indicators of infrastructure development using LISP funds are very important from the standpoint of socio-economic development of the territories, but are dependent on the living standards of the population, which provides the necessary degree of public initiative for «inclusion» of the LISP mechanism. In addition, the identified and assessed patterns, although they are common for the considered pilot region for the implementation of LISP, have specific manifestations in certain groups of municipalities, which requires finer targeted “tuning” of LISP by substantiating its projects to obtain socio-economic results, most relevant to each municipality.

Assessment of spatio-temporal patterns of water quality in a tropical river by multivariate statistical analysis and water quality index methods

Rivers are facing seasonal as well as long-term changes in their hydro-ecological regime in response to multiple stressors that have affected water quality as well as flow volumes. Ramganga is an important tributary of the Ganga River that provides freshwater to the people of Uttar Pradesh and Uttarakhand. In recent years, industrial discharges, agricultural and municipal wastes and wastewater releases have severely polluted the river throughout its stretch. The contamination index (CI) values at Moradabad in the pre-monsoon, monsoon and post-monsoon seasons were 0.78, 0.86, 0.76-not contaminated, at Bareilly 1.68, 1.65, 1.22-slightly contaminated, at Dabri 2.32, 2.34, 1.76-slightly contaminated while the pollution index (PI) values at Moradabad were 1.76, 2.23, 1.43-slightly contaminated, at Bareilly 4.29, 4.49, 3.95-severely contaminated and at Dabri 13.06, 12.21, 9.49- severely contaminated. The pollutants responsible for high pollution are NH3+, PO4-3, SO4-2, NO2-+NO3- which primarily come from agricultural run-off as the farmers use phosphate, ammonium and sulphate fertilizers and pesticides. The land use type along the banks of the Ramganga in Bareilly and Dabri is primarily dominated by agricultural lands. Hence, the increased CI and PI values at Bareilly and Dabri clearly indicate high agricultural pollution.

Assessment of Ground Instabilities’ Causative Factors Using Multivariate Statistical Analysis Methods: Case of the Coastal Region of Northwestern Rif, Morocco

An assessment of ground instabilities’ causative factors remains a topical subject. Such studies are rare, and evaluation techniques are still under development. The choice of evaluation technique should take into account the materials available and the objective sought. Statistical analysis methods are the most widely used, with multivariate analysis being the most accurate. The present work evaluates the weights of the influences of the different factors of ground instability of the coastal region between Tetouan and Jebha through multiple correspondence analysis (MCA) and principal component analysis (PCA). The application of both methods requires an accurate ground instability inventory with study sites that are well documented through modalities of causative factors and other descriptive data. The performed MCA shows that lithology has a significant influence on the type of existing instability. It also helped classify the instabilities into five distinct classes according to their modalities and specify the factors that differentiate the classes. The PCA shows that lithology is the most influential factor in landslides, contrary to rockfalls, where a variety of factors can be preponderant.

Correlation between the Quality of Talent Training and Regional Economic Development Based on Multivariate Statistical Analysis Model.

Entering the era of knowledge economy, the economic value of talent education is more and more emphasized, and it contributes more and more to the local economy and injects new vitality into local economic development. However, there are still some difficulties in the synergistic development of talent education and local economy, such as the low degree of integration of “industry, profession, and employment” and the lack of synergy between schools and enterprises, which affects the high-quality development of talent education and restricts the long-term development of regional economy. This paper incorporates machine learning algorithms to construct a multivariate statistics analysis model based on the correlation analysis model between talent training quality and regional economic development. Based on the multi-variate statistical analysis theory and method, the qualitative analysis and quantitative analysis are closely combined to scientifically pick up the main indicators affecting the comprehensive quality of talent cultivation and overcome the drawbacks of artificial subjective assignment of values. Through the judicious selection of evaluation indexes, a scientific and fair evaluation model is established, and the targets of talent cultivation are combined with regional economic development, in a comprehensive analysis, which provides a new means for the development of the regional economy.

Development of a Hybrid Biomimetic Enamel-Biocomposite Interface and a Study of Its Molecular Features Using Synchrotron Submicron ATR-FTIR Microspectroscopy and Multivariate Analysis Techniques.

Using a biomimetic strategy and bioinspired materials, our work proposed a new technological approach to create a hybrid transitional layer between enamel and dental biocomposite. For this purpose, an amino acid booster conditioner based on a set of polar amino acids (lysine, arginine, hyaluronic acid), calcium alkali, and a modified adhesive based on BisGMA and nanocrystalline carbonate-substituted hydroxyapatite are used during dental enamel restoration. The molecular properties of the hybrid interface formed using the proposed strategy were understood using methods of multivariate statistical analysis of spectral information collected using the technique of synchrotron infrared microspectroscopy. The results obtained indicate the possibility of forming a bonding that mimics the properties of natural tissue with controlled molecular properties in the hybrid layer. The diffusion of the amino acid booster conditioner component, the calcium alkali, and the modified adhesive with nanocrystalline carbonate-substituted hydroxyapatite in the hybrid interface region creates a structure that should stabilize the reconstituted crystalline enamel layer. The developed technology can form the basis for an individualized, personalized approach to dental enamel restorations.

Analysis of the pharmacodynamic difference between Xiaojin Pills taken with Chinese Baijiu and water based on serum pharmacochemistry and pharmacokinetics

Ethnopharmacological relevanceXiaojin Pills (XJPs), which has the function of dissipating knots and dispersing swelling, removing blood stasis, and relieving pain, is a classic prescription for the treatment of mammary glands hyperplasia. It is also the first choice of Chinese patent medicine for the clinical treatment of mammary glands hyperplasia in contemporary traditional Chinese medicine clinics. Previous studies have shown that the efficacy of XJPs “taken orally after soaked with Chinese Baijiu” in tradition was significantly better than that of taking it orally with water in modern in terms of activating the blood, anti-inflammation, analgesia, anti-mammary gland hyperplasia, anti-breast cancer and its metastasis in vitro and in vivo, especially under low-dose conditions. However, the material basis for the difference in efficacy between XJP&B and XJP&W is still unclear. Aim of the studyTo analyze the material basis of the significant difference in efficacy between XJP&B and XJP&W from the perspective of serum pharmacochemistry and pharmacokinetics, and clarified the scientific connotation of XJPs “taken orally after soaked with Chinese Baijiu”. Materials and methodsUltra-high performance liquid chromatography-mass spectrometry combined with a multivariate statistical analysis method were used to screen the differential components in the Chinese Baijiu extract and the water extract of XJPs and the corresponding residues, so as to clarify the differential components between XJP&B and XJP&W in vitro. The migrating components in the blood after XJP&B and XJP&W were characterized by serum pharmacochemical methods, in order to clarify the differential components in rats. The pharmacokinetic parameters of the representative components absorbed into the blood were compared between XJP&B and XJP&W by the pharmacokinetics study method, in order to determine the dynamic changes of the representative components in rats. ResultsThe identification results of different components in vitro showed that there were 34 and 12 different compounds between the Chinese Baijiu extract and water extract of XJPs, and the residues after Chinese Baijiu extraction and water extraction, respectively. The content of different components such as arachidonic acid, ursolic acid, 3-acetyl-11-keto-β-boswellic acid, 2α-hydroxyursolic acid, and oleanolic acid was higher in the Chinese Baijiu extract, which was more than twice the content in the water extract. The results of the serum pharmacochemistry study indicated that 42 prototype components were identified in the serum of rats after XJP&B and XJP&W, including organic acids, alkaloids, steroids, and terpenoids. And XJP&B increased the absorption of the prototype components of organic acids in XJPs into the blood. The pharmacokinetic study results of representative components demonstrated that the mean plasma concentration-time profile and pharmacokinetic parameters of muscone, aconitine, and 3-acetyl-11-keto-β-boswellic acid were significantly different between XJP&B and XJP&W. Compared with XJP&W, the Cmax and AUC0-t of muscone and aconitine in XJP&B were higher, and the T1/2 and MRT0-t of 3-acetyl-11-keto-β-boswellic acid in XJP&B were relatively longer. ConclusionThis research proved that “taking XJPs orally after being soaked with Chinese Baijiu” can increase the dissolution and absorption of active ingredients in XJPs, increase the plasma concentration and content of representative ingredients, and prolong its action time, thus enhancing the biological activity of XJPs in vitro and in vivo. To a certain extent, this study revealed the material basis of the significantly better efficacy of XJP&B than XJP&W and clarified the scientific connotation of XJPs “taken orally after soaked with Chinese Baijiu”, which can provide a theoretical basis for the optimization of XJPs’ clinical administration method.

Construction of Unbalanced Engineering Cost Management and Multivariate Statistics System Based on Random Matrix Weight Algorithm

The informatization of engineering cost plays an important role in the cost management and is also one of the important subjects of the informatization in the engineering field. It is the direction of the future project cost management. There is a trend of exponential increase in the information about project cost, which brings a great challenge to cost informatization. On the basis of the construction quantity and cost composition in the process of analyzing the present situation of the project cost, the overall index of the construction quantity and cost is constructed. For bridges and tunnels and to achieve a reasonable index of roadbed, on the basis of considering project at different stages and the demand for different levels of cost management personnel, adopt the method of statistical analysis and neural network, respectively, and set up the project cost fast estimation method and cost prediction model: intelligent prediction model and index system forecasting model. The error analysis and checking calculation of the two models provide the possibility for correctly estimating the engineering quantity and engineering cost in the preliminary design stage. Aiming at the low accuracy of the traditional unbalanced cost data classification algorithm, the dimension of random matrix weight was introduced into the cost data classification. On the basis of analyzing the spatial and temporal characteristics of WAMS measurement data, a high-dimensional random matrix model of WAMS measurement big data was constructed according to the high-dimensional random matrix theory, and a nonequilibrium cost data classification model based on the weighted algorithm of improved fuzzy rules was established. The model is applied to a unit project cost data classification, which verifies the operability of the model and provides a new idea for other units to carry out similar work. Finally, other common problems that should be paid attention to when using multivariate statistical analysis method are put forward.

Key phenotypes related to wheat grain yield in a two‐site multicultivar test

AbstractYield performances of wheat (Triticum aestivum L.) cultivars are decided by the combined effects of key phenotypes. However, it is unclear which phenotypes are the key phenotypes that determine yield. We conducted a two‐site multicultivar test in Henan and Anhui in China during the 2016‒2017 wheat growing season. The objectives were to assess the relationships between grain yield and other related phenotypes and reveal the key phenotypes related to grain yield. Grain yield and nineteen phenotypes were assessed in a field trial consisting of 28 wheat cultivars. Multivariate statistical analysis methods (e.g., principal component analysis, partial least squares model, structural equation model) were used to reveal the relationships between grain yield and other related phenotypes. Wheat phenotypes changed as a result of the differences in environment (site) and genotype (cultivar). Eleven phenotypes were identified as the key phenotypes closely related to grain yield: biomass, harvest index, spike density, leaf nitrate reductase, net photosynthetic rate, canopy temperature, leaf chlorophyll, leaf area index, stomatal conductance, carboxylation efficiency, and transpiration rate. For analyzing the mechanisms of yield differences, multiple related phenotypes (rather than a single phenotype) should be considered.

Cluster technologies in researching the inequality of mortgage development of Russian regions

Subject. The article discusses the uneven development of mortgage lending in Russian regions. Objectives. The aim is to cluster regional mortgage markets in the Russian Federation to identify uneven mortgage development in regions; to test the hypothesis about the possibility to base the differentiated approach to the State mortgage policy on the results of clustering of Russian regions. Methods. The study employs cluster technologies. The basic method is a hierarchical cluster analysis. The optimal number of clusters was selected by finding the ‘elbow’ point based on the study of the distance of clustering. Agglomerative clustering rests on the method of weighted pairwise comparison. Results. We performed hierarchical clustering of regional mortgage markets. Nine clusters were taken as the optimal number. The clustering results were analyzed with a search for their semantic interpretation. We revealed socio-economic reasons that determine the regional membership in the selected clusters, proved that the differentiated public policy of regional mortgage systems development to tackle the housing problems can be implemented on the basis of the results of clustering of regional mortgage markets, but not be limited to them. Conclusions. The findings can be useful for Federal authorities of the Russian Federation in the search and study of anomalies in regional mortgage development. Cluster technologies, as a tool for system classification of regions, are effective, if the cluster analysis is complemented by other methods of multivariate statistical analysis and the development of procedures for their joint constructive application.