Multivariate Statistical Analysis Methods Research Articles

Comparative analysis of bile metabolic profile in patients with biliary obstruction complicated by Clonorchis sinensis infection.

Clonorchiasis is an important foodborne parasitic disease. However, eggs of Clonorchis sinensis (C. sinensis) cannot be detected in feces during biliary obstruction. Moreover, many diseases can cause biliary obstruction, such as gallstones, adenocarcinoma, cholangiocarcinoma and Ascaris lumbricoides infection. Therefore, it is of great significance to distinguish between patients of biliary obstruction and biliary obstruction with C. sinensis infection. A total of 48 biliary obstruction patients were enrolled, including 23 infected with C. sinensis (C. sinensis) (OB+C.s) and 25 non-infected subjects (OB). The bile samples were collected by endoscopic retrograde cholangiopancreatography and analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MS). Additionally, multivariate statistical analysis methods were employed to identify differential metabolites. Next, bile amino acid levels were determined by targeted metabolomics analysis. A total of 146 and 132 significant metabolites were identified in electrospray ionization (ESI)+ and ESI- modes, respectively. The levels of amino acids (asparagine, glutamate, ornithine) and polyamines (spermidine and spermine) were significantly changed. Targeted analysis showed that the levels of amino acids (such as L-arginine, L-glutamine, L-lysine, L-propionic, and L-tyrosine) were lower in OB+C.s patients compared to those in OB patients. Marked metabolic pathways were involved in "Glutathione metabolism", "Caffeine metabolism", "Alanine, aspartate and glutamate metabolism", "Arginine and proline metabolism", "Purine metabolism", "Beta-Alanine metabolism", and "D-glutamine and D-glutamate metabolism". These results show that there were significant differences between OB+C.s and OB patients, especially in amino acids. The metabolic signature and perturbations in metabolic pathways may help to better distinguish OB+C.s and OB patients.

Moderator Effect of Chronic Disease on The Relationship Between Marriage Adjustment and Satisfaction in Married Couples

Objective: The purpose of our study is to show how the relationship between marital adjustment and satisfaction will change in cases of chronic disease in either or both spouses of married couples using a multivariate statistical analysis method. Methods: Marriage adjustment ve marriage satisfaction scales were used. A structural equation modeling - multiple group analysis method was used in the study, which was designed as a relational screening model. Results: In the study, which included 898 participants, 56.6% of the participants were female and 43.4% were male. The mean age of the participants was 36.94 ± 8.72 standard deviations. First, the relationship between marital adjustment and satisfaction was analyzed using structural equation modeling, and the relationship between the scales was found to be statistically significant (p=0.001). In the model, which was significant and sufficient, the variable of chronic disease was coded on the arrow representing the regression coefficient between the scales, and multiple group analysis was applied. The relationship between marital adjustment and satisfaction was found to be weak among individuals with chronic diseases. The rate of marriage satisfaction explaining marriage adjustment was lower in individuals without chronic disease (R2=0.16) than in those without chronic disease (R2=0.10). While ego scores were not significant in individuals without chronic disease (p=0.237), they were statistically significant in individuals with chronic disease (p=0.017). Conclusion: Chronic diseases has been found to have a significant impact on the relationship between spouses. Many studies have examined the effects of chronic diseases on marriage. However, our study differs from other studies because of the analytical methods used. In the scales, it was determined whether the chronic disease showed a change in the relationship between the scales, not the scores in the chronic disease state.

PENERAPAN METODE STRUCTURAL EQUATION MODELLING-PARTIAL LEAST SQUARES (SEM-PLS) DALAM MENGEVALUASI FAKTOR-FAKTOR YANG MEMPENGARUHI PDRB DI INDONESIA

Structural Equation Modeling (SEM) is a multivariate statistical analysis method that combines regression analysis with factor analysis. SEM can be used to describe simultaneous linear relationships between observed variables (indicators) and variables that cannot be directly measured (latent variables). In the development of covariance-based SEM, there are still weaknesses based on parametric assumptions that must be met in regression analysis, and one of the classic assumptions that must be met is the assumption that the data is normally distributed. Partial Least Square (PLS) is one solution or alternative method of model estimation to manage SEM modeling with reflective or formative indicators. PLS was created to overcome the limitations of the SEM method. Structural Equation Modeling-Partial Least Square (SEM-PLS) is a powerful analysis method because it allows structural equation modeling with the assumption that the data used does not have to be normally distributed, SEM-PLS can use a relatively small sample size, and the indicators used are reflective, formative, or a combination of both. This study aims to determine the effect of latent variable indicators, namely public service expenditure, economy, health, and education on the Gross Regional Domestic Product (GRDP) in each district/city in South Sulawesi Province in 2022. The indicators used for each latent variable are Public services (employee expenditure, goods and services expenditure, capital expenditure, other expenditure), Economy (goods and services expenditure, capital expenditure), Health (employee expenditure, goods and services expenditure, capital expenditure), Education (employee expenditure, goods and services expenditure, capital expenditure, other expenditure). The results of the study show that the other expenditure indicator on the latent variable of public services and the other expenditure indicator on the latent variable of Education are excluded in the study because they do not pass the loading factor test. The model equation obtained is GRDP = 0.052 Economy - 0.087 Health + 0.321 Education + 0.706 Public Services. The R2 value obtained from the model equation is 0.982, which means that the latent variables of public service expenditure, economy, health, and education can explain the latent variable of GRDP by 98.2%. The latent variables that significantly influence the model equation are public service expenditure and education.

Study on chemical constituents and fingerprints of Phellodendron amurense Rupr. based on ultra-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry.

The chemical constituents from Phellodendron amurense Rupr. were characterized systematically by ultra-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry method for collecting mass spectrometry data, and the fingerprints method was established, providing reference for its quality control. The chromatographic column was ACQUITY UPLC BEH-C18 (100mm×2.1mm, 1.7μm). The mobile phase was acetonitrile-0.1% formic acid aqueous solution and the compounds from P. amurense Rupr. were identified by Qualitative Analysis 10.0 software, reference substance, retention time, mass spectrometry fragmentation pattern and database retrieval. Meanwhile, liquid chromatography-mass spectrometry fingerprint methods of P. amurense Rupr. and Phellodendron chinense Schneid. were established by using the similarity evaluation system of chromatographic fingerprint of traditional Chinese medicine (2012 edition), and the differences were analyzed by multivariate statistical analysis methods. A total of 105 compounds were identified, including 102 alkaloids, two phenolic acids, and one lactone compound. Liquid chromatography-mass spectrometry fingerprint method was established with ideal precision, stability and repeatability, and 12 quality differential markers were recognized between the above two herbs. Liquid chromatography-mass spectrometry method can be used for qualitative analysis of the constituents of Phellodendron amurense Rupr., providing reference for clarifying the material basis and promoting the clinical precision medication and quality evaluation of P. amurense Rupr.

Research on the Stock Investment Value of Internet Listed Enterprises

Based on the multivariate statistical analysis method, this paper studies the investment value of Chinese Internet listed companies. Firstly, the factors affecting the investment value of Internet enterprises are analyzed from macro and micro aspects, and the index evaluation system is constructed respectively.Secondly, 90 Internet enterprises are selected and empirically studied by factor analysis and cluster analysis. Finally, four companies with good performance in each field are selected to use ARIMA model to forecast stock prices in the next five days.The results show that Chinese Internet enterprises as a whole have relatively high investment value, and the solvency and future development ability of enterprises have a great impact on the investment value of Internet enterprises.

Exploring the Characteristic Aroma Components of Traditional Fermented Koumiss of Kazakh Ethnicity in Different Regions of Xinjiang by Combining Modern Instrumental Detection Technology with Multivariate Statistical Analysis Methods for Odor Activity Value and Sensory Analysis.

To investigate the characteristic aromatic compounds, present in the traditional fermented koumiss of the Kazakh ethnic group in different regions of Xinjiang, GC-IMS, and GC-MS were used to analyze the volatile compounds in koumiss from four regions. A total of 87 volatile substances were detected, and esters, acids, and alcohols were found to be the main aroma compounds in koumiss. While the types of aroma compounds in koumiss were similar across different regions, the differences in their concentrations were significant and displayed clear regional characteristics. The fingerprint spectrum of GC-IMS, combined with PLS-DA analysis, indicates that eight distinctive volatile compounds, including ethyl butyrate, can be utilized to distinguish between different origins. Additionally, we analyzed the OVA value and sensory quantification of koumiss in different regions. We found that aroma components such as ethyl caprylate and ethyl caprate, which exhibit buttery and milky characteristics, were prominent in the YL and TC regions. In contrast, aroma components such as phenylethanol, which feature a floral fragrance, were more prominent in the ALTe region. The aroma profiles of koumiss from the four regions were defined. These studies provide theoretical guidance for the industrial production of Kazakh koumiss products.

Source analysis of metals in the tea plant using linear correlation analysis combined with a lead-strontium isotope tracer

Many studies have focused on the concentrations of heavy metals (HMs) in Tieguanyin tea plants and surrounding soil; however, limited information is available on the specific sources of HMs in tea plantation trees. In this study, HMs in tea plants (roots, stems, old leaves, and young leaves) from Anxi County of the Fujian Province were determined by inductively coupled plasma mass spectrometry (ICP-MS). Additionally, correlation analysis, principal component analysis (PCA), and isotope tracers were used to analyze the sources of HMs pollution. The contribution rate of HMs in tea from different specific sources was further quantified by establishing a relative isotope mixing model. Our results showed that the highest Pb content in the roots was 23.89 μg·g−1 and was positively correlated with Sr and Ni in the roots, indicating that these three metals in the roots are from the same source, mainly from the soil. PCA analysis showed that Sr and Pb in factor 1 have higher positive charges in the young leaves, and their contribution rate was 27.16%. Sr is commonly used in the cement industry, indicating that factor 1 was mainly affected by industrial activities. The Pb and Sr isotope ratio showed that the average values of 208Pb/206Pb and 206Pb/207Pb in young leaves were 2.1124 and 1.1619, respectively, while the average values of 87Sr/86Sr in young leaves was 0.709774. The contribution rate of each potential source was quantitatively analyzed using the Pb-Sr multiple isotope mixture model, and agricultural activities were found to be the biggest contributors of HMs in tea. For example, agricultural sources of HMs found in old leaves and young leaves were 62% and 31%, respectively. Using multivariate statistical analysis methods combined with isotope tracing techniques, this study was able to identify sources of multiple heavy metals in Tieguanyin tea plants.

Hydrochemical Characteristics and Control Factors of Groundwater in Shunping County, Hebei Province

In order to study the chemical characteristics and ion source of groundwater and further serve the scientific development and management of water resources in Shunping County. A total of 33 groups of karst water and 12 groups of pore water samples were collected systematically in Shunping County, and the hydrochemical types, composition characteristics, and main controlling factors of various types of groundwater were analyzed by using Gibbs diagram, ion ratio relation, and multivariate statistical analysis methods, and the contribution rates of various sources to groundwater solutes were evaluated. The results showed that the pore water and karst water in the study area were weakly alkaline, with TDS ranging from 245.89 to 430.00 mg·L-1 and 223.54 to 1347.80 mg·L-1, respectively. The anion components of groundwater were mainly HCO3- and Ca2+. Groundwater in the study area could be grouped into PW1 and PW2 pore water and KW1, KW2, and KW3 karst water. PW1 and KW1 were HCO3-Ca·Mg type, PW2 was HCO3·Cl-Ca·Mg type, KW2 was HCO3·NO3-Ca·Mg type, and KW3 was SO4-Ca·Mg type with high salinity. The weathering of carbonate rock mainly composed of dolomite and silicate rock mainly composed of albiar and potassium feldspar were the main material sources of groundwater, and their contributions to each water body were 39.69% to 66.13% and 11.87% to 58.38%. Sewage discharge and fertilizer use in human activities had significant effects on KW2 groundwater and PW1, PW2, and KW1 groundwater, respectively. In addition, the contribution rate of atmospheric precipitation to each water body ranged from 1.09% to 7.94% on average.

Acoustic emission wavelet time-frequency characteristics of fiber reinforced mortar failure process under axial compression

Abstract In order to investigate the wavelet time-frequency characteristics of fiber reinforced mortar under axial compression, acoustic emission signals associated with steel fiber reinforced mortar and polypropylene fiber reinforced mortar with five different fiber contents (0 to 2.0 vol%) under axial compression were recorded. A new method of multivariate statistical analysis of wavelet time-frequency maps based on Morlet wavelet transform is proposed to make signal processing more accurate. Results show in the 15% to 85% loading stage, Type 3 and Type 4 signals are abundant in fiber reinforced mortar than plain mortar, while Type 7 and Type 9 signals are abundant in the 85% to 100% loading stage. According to the temporal localization characteristics of signal frequency quantified by each type of signals, it can be inferred that the fibers improve the toughness of concrete materials. It is also found the strengthening and toughening effect of steel fiber is more sensitive to fiber contents than that of polypropylene fiber. The ratio of Type 1 signals can be used as a warning against the final failure of materials while that of Type 5 signals can be used to evaluate the anti-cracking effect of fibers.

ANALYSIS OF THE TRANSFORMATION OF THE ECONOMIC BEHAVIOR OF THE KAZAKHSTAN SOCIETY

The relevance of the research topic is determined by the need to analyze trends in the economic behavior of Kazakhstanis. The high degree of dependence of modern living conditions on ongoing geopolitical, economic, natural disasters necessitates a certain psychological and financial preparedness of people for emergencies, the transformation of society's consciousness towards increasing independence in solving emerging problems, moving away from dependent behavior, moving from a wasteful nature of behavior to more rational and thrifty.The purpose of the work is to interpret the trends in the development of economic consciousness and the behavior of Kazakhstani society based on an analysis of indicators of the standard of living of the population, the direction of development of small business and expert assessments of the Global Entrepreneurship Monitor (GEM).Realization of the goal predetermined the main objectives of the study: to consider the theoretical aspects of the formation of rational economic behavior that increases the population's resilience to modern economic and political challenges; clarify and characterize the main statistical indicators of the standard of living of the population, the development of small business, characterizing the trends in the development of the economic behavior of Kazakhstanis; identify changes in the nature of people's attitudes towards entrepreneurship as a way of independent activity and a stable income.Theoretical and methodological basis of the research are structural-functional, systemic and complex approaches as directions of the methodology of cognition of social processes. The work used the method of multivariate statistical analysis, visualization methods. The initial statistical information was processed in the Excel program.The originality of the value of the study lies in the fact that, based on the analysis of statistical data and expert assessments, an explanation is given for the development of the economic behavior of Kazakhstani society.The results of the study - an analysis of the real standard of living of Kazakhstanis for the period 2010-2020, their opportunities for the formation of savings, attitudes towards the implementation of entrepreneurial activities, made it possible to determine the trends in the transformation of consciousness and the development of economic behavior of Kazakhstanis.

A novel regression method: Partial least distance square regression methodology

Partial Least Squares (PLS) is a multivariate linear statistical analysis method integrating principal component analysis (PCA), canonical correlation analysis (CCA), and multiple linear regression (MLR) analysis. It can effectively solve the problems of multiple correlation between variables and small sample size. However, PLS also has some imperfections, on the one hand, PLS extracts the latent variables based on the maximization of Pearson correlation coefficient of independent and dependent variables. Here, Pearson correlation coefficient usually cannot measure the nonlinear relationship between variables, so the latent variables cannot ensure the strongest interpretation. On the other hand, PLS applies MLR to the extracted latent variables, which cannot truly reflect the nonlinear relationship of data, so the regression function is usually under-fitting. The above two imperfect designs are the main reasons for the low regression accuracy and prediction performance of PLS for nonlinear data. According to the above problems, this paper proposes a Partial Least Distance Square (PLDS) regression method. PLDS reflects the original data information through distance variance in PCA, employs distance correlation coefficient to measure the correlation between independent and dependent variables in CCA, and performs quasilinear regression and model solution method to obtain the final regression equation in MLR. In order to verify the effectiveness of the proposed method, the UCI datasets and Chinese medicine datasets with dose-effect relationship was used to compare the new method with other classical regression methods. Finally, the experimental results show that PLSD has better prediction performance regardless of whether there is significant nonlinear relationship between variables. In addition, PLDS has high regression accuracy and low computation complexity for high-dimensional and small-sample data.

Effects of a long-term operation wetland for wastewater treatment on the spatial pattern and function of microbial communities in groundwater

Constructed wetlands have been used globally for wastewater treatment owing to low energy inputs and operating costs. However, the impact of their long-term operation on groundwater microbial communities is still unclear. This study aims to investigate the effects and further reveal the linkage between a large-scale surface flow constructed wetland (in operation for 14 years) and groundwater. Changes in the characteristics of groundwater microbial communities and their potential influencing factors were studied based on hydrochemical analysis, Illumina MiSeq sequencing, and multivariate statistical analysis methods. Results show that the long-term operation wetland significantly elevated groundwater nutrient levels and increased the risk of ammonia nitrogen pollution compared to background values. An apparent heterogeneity of microbial communities exhibited in the vertical direction and a similarity in the horizontal direction. Wetland operations substantially altered the structure of microbial communities at 3, 5, and 12 m depths, particularly a reduced abundance of denitrifying and chemoheterotrophic functional genera. The formation and evolution of groundwater microbial community structure mainly subjected to the contributions of dissolved oxygen (33.70%), total nitrogen (21.40%), dissolved organic carbon (11.09%), and pH (10.60%) variations resulted from the wetland operation and largely differed in depths. A combined effect of these factors on the groundwater should be concerned for such a long-term running wetland system. This study provides a new insight into the responses of groundwater microbial community structure driving by wetland operation and a better understanding of corresponding variation of microbial-based geochemical processes.

How far are green products from the Chinese dinner table? ——Chinese farmers’ acceptance of green planting technology

In the global practice of sustainable agricultural development, China has put forward a plan for agricultural green development, with strict control from the production side to ensure the safety and health of green products “from farming land to dinner table” through strict control from the production side, and green planting is one of the key technologies. Green planting technology (GPT) is an important initiative in China to achieve sustainable agriculture under the Sustainable Development Goals (SDGs). Farmers' acceptance of this technology not only requires national publicity and promotion, but also the adoption of agricultural entities across the country. In this study, we widely distributed questionnaires to farmers in 26 provinces and cities, and received 970 valid responses. This paper uses structural equation model (SEM), a multivariate statistical analysis method, to analyze the influencing factors of farmers' intention to adopt GPT. These results demonstrated that the farmers first consider their own behavioral competences and personal gains and losses before deciding to adopt GPT, followed by the impact of people closely related to farming, while environmental concerns and environmental values rank last. Agricultural technology extension plays an intermediary role in farmers’ behavioral intention, and government support acts as a safeguard. The findings will not only help to grasp the reasons behind ineffective promotion of GPT to improve the dissemination and adoption of GPT, but also enable government agencies and authorities to better understand the views of farmers, and subsequently formulate feasible policies and plans.

Application of multivariate statistical analysis to assess the implementation of Sustainable Development Goal 8 in European Union countries

Sustainable development should ensure a fair and balanced natural, social and economic environment. Sustainable Development Goal 8 (SDG 8) – decent work and economic growth – is of the greatest economic importance. The purpose of the study is to assess the implementation of SDG 8 in EU member states. The analysis covered the years 2002–2021 with a particular focus on two crises periods: the financial crisis of 2007–2009 and the COVID-19 pandemic in the years 2020–2021. The study uses Eurostat data and multivariate statistical analysis methods, i.e. cluster analysis – the k-means method and linear ordering – the TOPSIS method. Denmark, Finland, the Netherlands and Sweden are the countries where the fulfilment of SDG 8 was the greatest, while the lowest was observed in Greece, Italy, Romania, Slovakia and Spain. The study also shows that the countries which joined the EU in 2004 generally demonstrated a much lower degree of SDG 8 implementation compared to the well-developed Western Europe. The influence of the crisis periods was more visible in the results of the cluster analysis than in the rankings. The novelty of the research involves the application of multivariate statistical analysis methods to assess the overall situation of the studied countries in terms of their implementation of SDG 8 while taking into account both crisis periods.

Partial least squares regression with conditional orthogonal projection for variable selection

Partial least squares regression (PLS) is a popular multivariate statistical analysis method. It not only can deal with high-dimensional variables but also can effectively select variables. However, the traditional PLS variable selection approaches cannot deal with some prior important variables. In this article, we propose two filter PLS variable selection algorithms (CPLS-BETA, CPLS-VIP) that combine with the prior variables by the conditional orthogonal projection. The performance of variable selection can be improved by projecting the other variables and response orthogonally on some prior active variables. Moreover, we introduce a kind of data-driven conditional method named forward projection PLS (FPPLS), which is suitable for the situation of unknown prior information. Finally, the validity of our method is verified by simulated datasets and real datasets.

A review of data-driven fault detection and diagnostics for building HVAC systems

With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.

To the Question of the Relevance of Statistics of Information and Communication Technologies in the Context of the Digital Transformation of the Economy

Purpose of the study. The process of qualitative transformation of the economy under the influence of information and communication technologies is called digital transformation. The change of the economic paradigm in the technocratic context raises the question of whether the existing statistical methodology can still be applied objectively and effectively to the study of the subject area in the new conditions. The purpose of the article is to assess the relevance of statistics in the field of information and communication technologies as an information source for analyzing the digital transformation of the economy, taking into account its industry specifics.Materials and methods. To clarify the depth of this problem, there seems to be no better way than to carry out an analysis using statistical data collected and published by the Federal State Statistics Service. As statistical tools, descriptive statistics indexes are used to describe particular indicators, one of the methods of multivariate statistical analysis for obtaining a classification according to a number of characteristics (cluster hierarchical analysis) and, finally, a method for calculating an integral index that can rank the units of the analyzed population formed according to the type of economic activities (18 units), simultaneously for all of its constituent indicators.Results. The system of statistical indexes formed during the analysis included four thematic groups of indexes: a) IT infrastructure and access to it; b) the level and direction of use of IT services; c) availability and qualifications of IT specialists; d) information security. The calculation of descriptive statistics showed that all groups (with the exception of indexes of the presence and qualifications of IT specialists) are homogeneous; comparison of the values of the arithmetic mean and the median does not make it possible to declare any significant asymmetry in their distribution. Cluster hierarchical analysis was carried out by the Ward method using the Minkowski metric, which made it possible to obtain two approximately equal in size industry clusters and one mono-cluster, consisting of a type of activity in the field of information and communication. The resulting grouping, however, could not definite answer the question of the priority of some industries over others in the digital transformation of their constituent organizations. To solve this problem, an integral index was developed, which included the most significant indexes of each of the groups (selected by experts). As a result of calculating the values of this integral index based on the arithmetic weighted average, a ranked series was obtained, transformed into a typological grouping, the leader of which is activity in the field of information and communication, the outsider is agriculture. An analysis of this grouping made it possible to draw a very curious conclusion: in general, in the analyzed set of types of economic activity, there is a clear trend or dependence - the closer the industry is to the sphere of material production, the lower its inherent value of the integral index of digital transformation.Conclusion. The analysis carried out convincingly showed the significant possibilities of information and communication technology statistics as an information source for analyzing the process of digital transformation of the economy in the sectoral context. At the same time, the dynamism of the analyzed subject area determines the prerequisites for revising the system of statistical indexes in order to update it and increase the efficiency of publishing the results of surveys in the field of information and communication technologies

MORPHOLOGICAL CHARACTERISTICS OF THE CARP (CYPRINUS CARPIO) FROM THE WESTERN PART OF LAKE BALKASH UNDER CONDITIONS OF MODERN ANTHROPOGENIC PESSURE

Balkash Lake is one of the largest reservoirs in South-Eastern Kazakhstan, taking the 2nd place in terms of area after the Caspian Sea. The current environmental problems are damaging the ichthyofauna of the lake, where many populations of commercial fish species are significantly affected. Fish, being part of an ecosystem, are able to show adaptive properties by changing their biological (species) characteristics in response to environmental changes. With the increase in pollution of aquatic ecosystems, qualitative changes in the external and internal structure of fish occur. The chosen object of study, carp (Cyprinus carpio), is a good example of manifestations of adaptations in response to changes in the quality of the habitat. The study of the variability of morphometric traits in fish is an important direction in identifying anomalies and monitoring the general health of fish. In this regard, we studied 28 plastic features and 16 meristic (countable) features, as well as the presence of deviations in the appearance of the carp (external malformations). The study using the method of multivariate statistical analysis made it possible to determine the load and direction of changes in individual signs of carp. Based on the results of morphological studies, the phenotypic plasticity of carp correlates with the negative environmental conditions of the western part of Lake Balkash.

Comprehensive quality evaluation of Aconiti Lateralis Radix Praeparata based on pseudotargeted metabolomics and simultaneous determination of fifteen components, and development of new processed products of black slices with less toxicity

Aconiti Lateralis Radix Praeparata is one of the most famous traditional Chinese medicines possessing a variety of pharmacological activities on top of the toxicities. Due to the heterogeneity and non-standardization of the processing procedures, the subtypes and contents of the differential compounds between different processed products still remained indistinct, causing great risk in their proper use. In order to achieve the comparison and quality evaluation of different processed products of Aconiti Lateralis Radix Praeparata and develop new processed products with less toxicity, a quantification and pseudotargeted metabolomics method was developed based on the dynamic MRM mode of triple quadrupole (QqQ) mass spectrometry, and multivariate statistical analysis methods were applied to compare different processed products. Method validation results indicated good specificity, linearity, repeatability, precision, stability and recovery of the established quantification method and good linearity, precision and stability of the pseudotargeted metabolomics method. Differential compounds of different processed products were screened out and further confirmed by the quantification results. At last, the processing procedures were optimized to obtain new processed products of “Heishunpian” (black slices) with less toxicity, in which the contents of the toxic diester-type diterpenoid alkaloids were reduced from 106.98 μg/g to 0.85–12.96 μg/g. This study provided a valuable reference for the establishment of comprehensive quality evaluation methods of herbal medicines and a scientific basis for the optimization of processing procedures of Aconiti Lateralis Radix Praeparata.

Multiple Evaluations of the Spatial and Temporal Characteristics of Surface Water Quality in the Typical Area of the Yangtze River Delta of China Using the Water Quality Index and Multivariate Statistical Analysis: A Case Study in Shengzhou City.

Surface water assessments are of critical importance for balancing economic development with the ecological environment in rapidly developing regions. In this research, Shengzhou City, a typical town in the Yangtze River Delta region of China, was chosen to conduct a surface water quality study. As a region with a well-developed water system, monthly water quality monitoring data from eight sampling sites on the major tributaries and the mainstream were selected for six consecutive years from 2013 to 2018, containing seven important water quality indicators (pH, DO, CODMn, CODCr, BOD, NH4+-N, and TP). The comprehensive evaluation method based on the water quality index (WQI) and multivariate statistical analysis methods of cluster analysis (CA) and principal component analysis (PCA) were applied to explore the spatial and temporal changes of water quality in Shengzhou City. The main findings are as follows: (1) spatially, for three main tributaries, Xinchang River had the worst water quality, followed by Changle River, while Huangze River had the best. The water quality of the tributaries had higher volatility than the mainstream. (2) The sampling sites with similar locations had similar water quality characteristics. (3) Seasonally, for the four indicators of DO, CODMn, CODCr, and BOD, the water quality was better in the dry season while, for NH4+-N and TP, water quality was better in the wet season. The low WQI points were more likely to appear in the wet season. (4) The results of WQI assessment showed an improving trend in water quality. (5) Nitrogenous substances and organic matter were the key pollutants in this area. The research results prove that water quality evaluation methods and multivariate statistical methods are effective for the study of regional surface water quality.