Multivariate Statistical Analysis Methods Research Articles

Assessment of water quality of streams in northeast Turkey by water quality index and multiple statistical methods

A year-long study from May 2018 to April 2019 investigated the spatio-temporal changes in water quality of seven major rivers in the Giresun Province in northeastern Turkey. The results of the analysis were classified according to WHO limit values for drinking water and water quality classes were determined with respect to the Turkish Surface Water Quality Regulation (TSWQR). In addition, sodium percentage (% Na), sodium absorption ratio (SAR), magnesium hazard (MH), and residual sodium carbonate (RSC) values were calculated to evaluate the eligibility of the streams for irrigation purposes. Multivariate statistical analysis methods such as one-way ANOVA, Pearson correlation index (PCI), cluster analysis (CA), principal components analysis/factor analysis (PCA/FA) were used to determine the differences and similarities of streams, pollution sources and relations between parameters. As a result of the PCA, four factors accounted for 71.52% of all factors. These factors show that the major changes in water quality parameters were associated with point-based pollution such as wastewater from household and industrial resources, non-point sources such as agricultural activities and natural phenomena such as flooding, rock and soil erosion. The pH values in the range of 7.77 to 9.8 were slightly alkaline compared to the TSWQR range of pH 6–9. In certain months, the average NO2-N values (> 0.06 mg/L) in Batlama Stream, Aksu Stream, Yağlıdere Stream and Gelevera Stream was class III (medium) water quality and mean fluoride values in streams (1 and 1.19 mg/L) were class II water quality according to TSWQR. The levels of anionic surfactants in all streams were class III (polluted) water quality, except for Pazarsuyu Stream with class II (less polluted) water quality. The annual mean water quality index (WQI) values of the streams ranged from 25.69 (excellent) to 32.39 (good). When the water quality was evaluated for irrigation, the SAR, Na%, RSC and MH did not exceed the limit values. Based on these indices it was assessed that settlements and related anthropogenic activities along the riverbanks did not affect the water quality currently. This does not mean that it may not have an effect in the near future.

Comprehensive profiling of the chemical components and potential markers in raw and processed Cistanche tubulosa by combining ultra-high-performance liquid chromatography coupled with tandem mass spectrometry and MS/MS-based molecular networking.

Chinese materia medica processing is a distinguished and unique pharmaceutical technique in traditional Chinese medicine (TCM), which has played an important role in reducing side effects, increasing medical potencies, altering the properties and even changing the curative effects of raw herbs. The efficacy improvement in medicinal plants is mainly caused by changes in the key substances through an optimized processing procedure. Thus, the use of a rapid method for determining suitable chemical markers between raw and processed TCM is critical in order to elucidate how the bioactive compounds influence the clinical effects. In this study, ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry combined with MS/MS-based molecular networking (MN) and a multivariate statistical analysis method is proposed for the first time. This combination was used to identify the complex chemical composition and clarify the changed constituents between raw and processed Cistanche tubulosa (C. tubulosa). The chemical analysis results demonstrated that a total of 85 compounds were identified in the crude and processed C. tubulosa. Moreover, 34 compounds were detected as chemical markers. This systematic research into chemical constituents and chemical markers of crude and processed C. tubulosa lays a solid foundation for further study of the quality control of C. tubulosa. Moreover, the study provides a new and valuable technical strategy for analyzing chemical components and identifying potential chemical markers for the processing of herbal medicines.Graphical abstract.

Establishment of quality control model for high performance liquid chromatography fingerprint based on ultraviolet full-wavelength scanning spectrum: case study on different grades of Scrophulariae Radix

High performance liquid chromatography-ultraviolet(HPLC-UV) fingerprint is one of the most important methods for the quality control of Chinese medicines in Chinese Pharmacopoeia. However, certain subjectivity is present in selection of specific band of UV, and the inherent quality differences of Chinese medicine can't be well characterized by this method. Therefore, with different grades of Scrophulariae Radix were taken as the research object in this study, a new quality control model of HPLC-UV was established in this study based on the ultraviolet full-wavelength scanning spectrum. Firstly, different grades of Scrophulariae Radix samples were collected, and the full-wavelength ultraviolet absorption spectra of all the samples were established at the bands of 200-400 nm. In order to analyze the differences among samples, the analysis model was built following multivariate statistical analysis methods such as principal component analysis(PCA) and partial least squares discrimination analysis(PLS-DA) after the pretreatment of spectral data. The result showed that the ultraviolet band at 251 nm may contribute most to distinguish the quality differences among different grades of samples. Then, the HPLC fingerprints of samples were established with the band at 251 nm. The multivariate statistical analysis showed that there was a more significant classification trend in HPLC fingerprints than that in the original UV fingerprints, which could be used to distinguish different grades of samples, and could better reflect the differences among different grades. The method reported in this study can be of a great guidance and reference for the establishment of specific fingerprints of Chinese medicines as well as for the quality control of Chinese medicine.

Metabolomics Based Comparison on the Biomarkers between Panax Notoginseng and its Counterfeit Gynura Segetum in Rats

Background: Because of the similar appearance of Gynura segetum and panax notoginseng, the patients often mistakenly use Gynura segetum as Panax notoginseng, which causes serious liver damage. There is no comparative study on the metabolism of Gynura segetum and Panax notoginseng in the literature. This study was conducted to compare the difference between Panax notoginseng and its counterfeit Gynura segetum by using metabolomics method. Methods: In this paper, an ultra performance liquid chromatography coupled to quadrupole time-offlight mass spectrometric(UPLC-Q/TOF/MS) were used to detect the type of endogenous metabolites in urine and plasma of three groups (normal group, ethanol extract of panax notoginseng, decoction of Gynura segetum respectively, and different multivariate statistical analysis methods were established. Results: In this experiment, main urine biomarkers were L-glutamate, L-methionine, cytidine, and Ltyrosine in the Panax notoginseng group, which are phytosphingosine, creatine and sphinganine in the Gynura segetum group. The plasma biomarkers identified in the Panax notoginseng group were arachidonic acid, L-tyrosine, linoleic acid, alpha-linolenoyl ethanolamide and lysoPC (15:0), and in the Gynura segetum group are L-arginine, L-valine, arachidonic acid and LysoPC(18:2(9Z,12Z)). Conclusion: There are significant difference between Panax notoginseng and Gynura segetum in biomarkers from the perspective of metabolomics in the body.

Национальная безопасность России

The article is continuing the research “Modelling and Assessment of Russia’s National Power”, dedicated to calculating the index of national power, which is currently the most informative and popular indicator in the world that integrally characterizes the aggregate potential of a particular country and allows at the same time to compare the level of its power and socio-economic development with the other countries’ level. The research results were published in the “Economic Strategies” magazine No. 2 for 2020. [1]. If the national power of the state, in fact, determines its potential capabilities and mainly gross indicators are used for the calculation, then the national security indicators are calculated on the basis of normalized values (as a rule, per capita) and witness the development level of one or another component of the country’s socio-ecologic and economic system, as well as efficiency of using the existing potential. It should be reminded that in terms of the National Integral Strength Index, which includes many components, Russia ranks fourth in the world, slightly behind India. In this article, using the methods of multivariate statistical analysis, we’ll calculate the national security indicators of the UN member states, also defining the position of Russia, but in addition, we'll also estimate the threshold values for all the factors of national security considered in the work. In the last part of the paper, we'll give a medium-term forecast of national security.

RISKS ESTIMATION METHOD BY CLUSTERED EXTREME DATA OF PROCESS COVARIATES

Context. This paper presents a method for solving the problem of detecting and taking into account the influence of various (external and/or internal) factors on extreme and risky values of the multivariate observed parameters (covariates) of technological and/or diagnostic processes. Taking into account external and internal influence factors on covariates, by analogy with critical process parameters, is a significant addition to the extreme values statistics and the estimations the influence of the variability of process’s covariates on the expected losses, i.e. value at risk. Risk-oriented analysis is an actual tool for the data behavior investigation of the multivariate observations of process’s parameters. Objective. To disclose a method for detecting and taking into account the factors influence on the distribution functions parameters of the observed extreme values of process’s covariates and determine the influence of these distribution functions parameters on estimates of risks values. Method. The method consistently uses: the procedures of multivariate statistical cluster analysis, transformation the matrix of observed extreme values of process’s covariates into data frame with factor variables, estimation the extremal index and distribution functions parameters of nonclustered and clustered the observed extreme data of covariates and estimation the risk values on the calculated values of distribution functions parameters. The proposed sequence of actions is aimed at implementing the information technology of statistical causal analysis of the influence of factors on the variability of process’s covariates and their risk values due to the application of the clustering procedure for observed multivariate extreme values of covariates. The method is implementing the R-language packages software. Results. Clustering of the multivariate observed extreme values of process’s covariates allows to identifying the influence of environmental (manufacturing) factors and estimates the covariates’ risky values taking into account of this influence.Conclusions. The method is an information technology of statistical causal analysis of factors influence on the variability of process’s covariates and theirs risk values due to the application of the clustering procedure of covariates’ multivariate values. The prospect of further research is to improve the methods of causal multivariate statistical analysis of the various factors influence on the exogenous and endogenous parameters of manufacturing and other processes in order to reduce the variability of these parameters and, as a result, minimize the risks.

Study on the pharmacodynamics and metabolomics of five medicinal species in Atractylodes DC. on rats with rheumatoid arthritis

Atractylodes DC. mainly includes Atractylodis Rhizoma and Atractylodis macrocephalae Rhizoma. According to Chinese Pharmacopoeia, Atractylodis Rhizoma is the rhizome of Atractylodes lancea (Thunb.) DC. (A. lancea) and Atractylodes chinensis (DC.) Koidz. (A. chinensis), while Atractylodis macrocephalae Rhizoma is the rhizome of Atractylodes macrocephala Koidz. (A. macrocephala). Although Atractylodes japonica Koidz. ex Kitam. (A. japonica) and Atractylodes coreana (Nakai) Kitam. (A. coreana) are not included in the Pharmacopoeia, they are often used as Atractylodis Rhizoma in northern China. But in Japan, A. japonica is used as Atractylodis macrocephalae Rhizoma. In order to compare the efficacy of A. japonica and A. coreana with that of Atractylodis Rhizoma and Atractylodis macrocephalae Rhizomain in Pharmacopoeia, this paper studies the anti rheumatism of the five medicinal species in Atractylodes DC., and provides the basis for the rational application of A. japonica and A. coreana. With this purpose, the rheumatoid model of rats was established by Freund's complete adjuvant. Then, Enzyme-linked immunosorbent assay (ELISA) was used to determine the contents of interleukin-6 (IL-6), tumor necrosis factor-α (TNF - α), arthritis factor (RF), anti cyclic citrullinated peptide (anti-CCP), interleukin-1β (IL-1β), Nuclear factor-κB (NF - κB), interleukin-10 (IL-10) and Prostaglandin E2 (PGE2) in rats of each group. Ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) was used to analyze the plasma of each group. After that, multivariate statistical analysis method was used to analyze the data. The results showed that the five medicinal species in Atractylodes DC. can reduce the levels of IL-6, IL-1 β, PGE2 and NF - κB in the plasma of rheumatoid arthritis (RA) rats in varying degrees, among which the regulation of A. macrocephala is relatively weak. A. chinensis, A. lancea, A. coreana and A. japonica can significantly reduce the content of TNF - α, in which A. japonica and A. lancea have better and similar regulatory effects. A. chinensis and A. coreana can significantly reduce the content of RF in arthritis rats. A. coreana, A. lancea and A. japonica can significantly reduce the anti-CCP level, that is, the regulatory effect of A. coreana and A. chinensis is similar. The metabolic disorder of 11-deoxycortisol, taurocholate and other small molecules in the body of rats with RA directly affects the metabolic pathways of primary bile acid biosynthesis and steroid hormone biosynthesis, leading to the decline of immune function and other symptoms. Most of the metabolic pathways tend to be normal after oral administration of five medicinal species in Atractylodes DC. Among them, the regulating effect of A. coreana and A. chinensis is similar, while that of A. japonica and A. lancea are similar. A. macrocephala had little effect of intervention.

Comparative Metabolomic Analysis of Dendrobium officinale under Different Cultivation Substrates.

Dendrobium officinale, a precious herbal medicine, has been used for a long time in Chinese history. The metabolites of D. officinale, regarded as its effective components to fight diseases, are significantly affected by cultivation substrates. In this study, ultra-performance liquid chromatography mass spectrometry (UPLC-MS/MS) was conducted to analyze D. officinale stems cultured in three different substrates: pine bark (PB), coconut coir (CC), and a pine bark: coconut coir 1:1 mix (PC). A total of 529 metabolites were identified. Multivariate statistical analysis methods were employed to analyze the difference in the content of metabolites extracted from different groups. By the criteria of variable importance in projection (VIP) value ≥1 and absolute log2 (fold change) ≥1, there were a total of 68, 51, and 57 metabolites, with significant differences in content across groups being filtrated out between PB and PC, PB and CC, and PC and CC, respectively. The comparisons among the three groups revealed that flavonoids were the metabolites that fluctuated most. The results suggested the D. officinale stems from the PB group possessed a higher flavonoid content. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that the significantly regulated metabolites were mainly connected with flavonoid biosynthesis. A comprehensive profile of the metabolic differentiation of D. officinale planted in different substrates was provided, which supports the selection of an optimum cultivation substrate for a higher biomass yield of D. officinale.

Semi-Targeted Metabolomics to Validate Biomarkers of Grape Downy Mildew Infection Under Field Conditions.

Grape downy mildew is a devastating disease worldwide and new molecular phenotyping tools are required to detect metabolic changes associated to plant disease symptoms. In this purpose, we used UPLC-DAD-MS-based semi-targeted metabolomics to screen downy mildew symptomatic leaves that expressed oil spots (6 dpi, days post-infection) and necrotic lesions (15 dpi) under natural infections in the field. Leaf extract analyses enabled the identification of 47 metabolites belonging to the primary metabolism including 6 amino acids and 1 organic acid, as well as an important diversity of specialized metabolites including 9 flavonols, 11 flavan-3-ols, 3 phenolic acids, and stilbenoids with various degree of polymerization (DP) including 4 stilbenoids DP1, 8 stilbenoids DP2, and 4 stilbenoids DP3. Principal component analysis (PCA) was applied as unsupervised multivariate statistical analysis method to reveal metabolic variables that were affected by the infection status. Univariate and multivariate statistics revealed 33 and 27 metabolites as relevant infection biomarkers at 6 and 15 dpi, respectively. Correlation-based networks highlighted a general decrease of flavonoid-related metabolites, whereas stilbenoid DP1 and DP2 concentrations increased upon downy mildew infection. Stilbenoids DP3 were identified only in necrotic lesions representing late biomarkers of downy mildew infection.

Bayesian Network Based on an Adaptive Threshold Scheme for Fault Detection and Classification

Data-driven multivariate statistical analysis methods have been widely used in fault monitoring of large scale and complex industrial processes. Condition Gaussian Network (CGN) provides a way of p...

Rapid, non-invasive screening of keratitis based on Raman spectroscopy combined with multivariate statistical analysis.

This study proposes a multivariate statistical analysis method based on Raman spectroscopy and different dimensionality reduction methods combined with the support vector machine (SVM) algorithm for rapid, non-invasive, high-accuracy classification of keratitis screenings. In this experiment, tear samples from 19 subjects with keratitis and 27 healthy subjects were detected, Raman spectra of the two groups of subjects were compared and analysed, and we found that their spectral intensities were different at 1005 cm-1 and 1155 cm-1 Principal component analysis (PCA) and partial least squares (PLS) were used for feature extraction, which greatly reduced the dimensionality of the high-dimensional spectral data. Then, the above two feature extraction methods were used as input to an SVM to build the discriminant diagnosis model. The average accuracy obtained from the PCA-SVM and PLS-SVM models was 77.86 % and 100 %, respectively. Our results suggest that tear Raman spectroscopy combined with multivariate statistical analysis has great potential in screening for keratitis. We expect this technology to could lead to the development of a portable, non-invasive and highly accurate keratitis screening device.

Water quality assessment and pollution source apportionment using multi-statistic and APCS-MLR modeling techniques in Min River Basin, China.

Anthropogenic activities pose challenges on security of water quality. Identifying potential sources of pollution and quantifying their corresponding contributions are essential for water management and pollution control. In our study, 2-year (2017-2018) water quality dataset of 15 parameters from eight sampling sites in tributaries and mainstream of the Min River was analyzed with multivariate statistical analysis methods and absolute principal component score-multiple linear regression (APCS-MLR) receptor modeling technique to reveal potential sources of pollution and apportion their contributions. Temporal and spatial cluster analysis (CA) classified 12months into three periods exactly consistent with dry, wet, and normal seasons, and eight monitoring sites into two regions, lightly polluted (LP) and highly polluted (HP) regions, based on different levels of pollution caused by physicochemical properties and anthropogenic activities. The principal component analysis (PCA) identified five latent factors accounting for 75.84% and 73.46% of the total variance in the LP and HP regions, respectively. The main pollution sources in the two regions included agricultural activities, domestic sewage, and industrial wastewater discharge. APCS-MLR results showed that in the LP region, contribution of five potential pollution sources was ranked as agricultural non-point source pollution (22.13%) > seasonal effect and phytoplankton growth (19.86%) > leakage of septic tanks (15.73%) > physicochemical effect (12.86%) > industrial effluents and domestic sewage (11.59%), while in the HP region ranked as point source pollution from domestic and industrial discharges (20.81%) > municipal sewage (16.66%) > agricultural non-point source pollution (15.23%) > phytoplankton growth (14.82%) > natural and seasonal effects (12.67%). Based on the quantitative assessment of main pollution sources, the study can help policymakers to formulate strategies to improve water quality in different regions.

Source Identification and Pollution Characteristics of Heavy Metals in Suspended Particles of Urban Road Dust from Xingtai City

Fifty dust samples were collected from Xingtai City to study the source characteristics of heavy metals in suspended particles of urban road dust from industrial enterprise transformation cities. The contents of Cr, Mn, Co, Ni, Cu, Zn, As, and Pb in particles of urban road dust smaller than 100 μm were determined by X-ray fluorescence spectrometry, and their pollution levels were assessed by enrichment factor. The possible sources of heavy metals measured in the dust were identified by multivariate statistical analysis methods including correlation analysis, principal component analysis, and cluster analysis. The results indicated that in comparison to the element background values of local soil, the particles of urban road dust smaller than 100 μm from Xingtai City had higher Cr, Mn, Co, Cu, Zn, and Pb concentrations. The accumulation level of Co was the highest, followed by Zn, and Pb and Cu, whereas the other trace metals were deficient or showed minimal enrichment in the suspended road dust. The multivariate statistical analysis results showed that Cr, Mn, and Ni had significant positive correlation. These metals belong to both a principal component and a cluster, whereas Cu, Zn, and Pb belong to just a principal component. Considering the content characteristics and the results of multivariate statistical analysis of various heavy metal elements in the suspended samples, these results illustrated that the Cr, Mn, and Ni in the suspended particles of urban dust from Xingtai City roads mainly come from natural and industrial sources, whereas the Zn, Pb, and Cu originate from transportation and coal sources; As comes from natural sources, and Co is mainly affected by industrial activities.

Dynamic changes in quality of jujube wine during fermentation

Dynamic quality changes of jujube wine during 10 days (240 hr) fermentation were evaluated by electronic nose and electronic tongue combined with multivariate statistical analysis methods. Fermentation significantly reduced the relative content of aromatic compounds, but promoted the generation of alcohol, sour compounds, umami compounds, sulfur-containing organics, and broad range sensitivity compounds. The results of principal component analysis and cluster analysis indicate that, according to the overall quality similarity, the whole fermentation process of jujube wine can be categorized into three stages: early stage (0–24 hr), middle stage (36–132 hr), and late stage (144–240 hr). By Multivariate Analysis of Variance, significant differences (p < .001) were observed among jujube wine samples in the three fermentation stages. Further analysis using Redundancy Analysis found that the major changes of jujube wine quality occurred in the middle stage of fermentation, particularly 36–132 hr. Practical applications In the case of the wine production, employing of the appropriate fermentation time throughout the process can be problematic. Hence, in this paper, electronic nose and electronic tongue technology combined with multivariate statistical analysis methods were applied to reveal the dynamic changes in quality of jujube wine, which provided a promising instruction for quality control of jujube wine.

Batch process fault detection for multi-stage broad learning system

In the real industrial production process, some minor faults are difficult to be detected by multivariate statistical analysis methods with mean and variance as detection indicators due to the aging equipment and catalyst deactivation. With structural characteristics, deep neural networks can better extract data features to detect such faults. However, most deep learning models contain a large number of connection parameters between layers, which causes the training time-consuming and thus makes it difficult to achieve a fast-online response. The Broad Learning System (BLS) network structure is expanded without a retraining process and thus saves a lot of training time. Considering that different stages of the batch production process have different production characteristics, we use the Affinity Propagation (AP) algorithm to separate the different stages of the production process. This paper conducts research on a multi-stage process monitoring framework that integrates AP and the BLS. Compared with other monitoring models, the monitoring results in the penicillin fermentation process have verified the superiority of the AP-BLS model.

The application of metabolomics analysis in the research of gestational diabetes mellitus and preeclampsia.

The aim of the study was to investigate the difference of the serum metabolic profile between gestational diabetes mellitus (GDM) patients and preeclampsia (PE) patients, to establish the disease differentiation model and to find characteristic metabolites, in order to provide a new idea for the occurrence, development and treatment of the disease. Twenty-nine patients with GDM group and 29 PE group who were examined in Tianjin No. 3 Central Hospital from March 2018 to August 2018 were enrolled as case group, and 29 normal pregnant women were selected as control group. All the serum samples were analyzed by using the ultra-performance liquid chromatography and mass spectrometry. Based on the multivariate statistical analysis method of pattern recognition, we screened out and identified the differential characteristic metabolites. The serum metabolic profile model of GDM group and PE group was successfully constructed. A total of nine characteristic metabolites were screened and identified in this study, including LPC 18:0, LPC 22:6, LPC 16:0, (S)-14-methylhexadecanoic acid, behenic acid, palmitic acid, sphingosine, phytosphingosine and 1,25-dihydroxyvitamin D3-26,23-lactone. Among them, six characteristic metabolites which were LPC 18:0, LPC 22:6, behenic acid, palmitic acid, sphingosine and 1,25-dihydroxyvitamin D3-26,23-lactone all had a significant statistical difference among GDM, PE and normal pregnancy groups (P < 0.05). The construction of metabolic profile discriminant model has a strong ability to differentiate GDM patients from PE pregnant women. The screened characteristic metabolites can early reflect the disorder of lipid, calcium and phosphorus metabolism of patients, and provide reference and help for the discussion of the occurrence, development and treatment of diseases.

Methods for evaluating the effectiveness of the object management system

Evaluation of the effectiveness of systems lifecycle management the object of capital construction (further – Department) is a mechanism that allows objectively to determine the attainability of the goals and objectives of the project (hereinafter – project) as a whole and each of its stages, the efficiency of the organization and implementation of the project in the context of the rational use of resources. In accordance with the life cycle of a capital construction object is the period during which engineering surveys, design, construction (including conservation), operation (including current and major repairs), reconstruction, and demolition of a capital construction object are carried out. Performance assessment is an important element in the development of project and planning decisions that allows you to determine the level of development and progressiveness of the current management structure, projects being developed, and is carried out in order to select the most rational version of the management structure or how to improve it using the selected evaluation criteria. The set of performance evaluation parameters used for solving a specific task is determined based on the project specifics and the required technical and economic indicators. Methods of multivariate statistical analysis and multi-criteria evaluation were considered to assess the effectiveness of management systems. The essential point is the presence of a range of common parameters. This research can be used in the development of information systems in construction, as well as in investment and construction organizations to audit the quality of the management system and its segments.

Rapid identification of cellulose nitrate and cellulose acetate film in historic photograph collections

Cellulose nitrate transparent plastic film was used by photographers and movie filmmakers from its release in the 1880s to the 1950s. The storage of this material is a challenge for cultural institutions because of its instability and hazardous nature, as nitrate is highly flammable and deteriorates over time. Historically, cellulose acetate gradually began to replace cellulose nitrate as it is not flammable. Despite its non-flammable properties, leading to cellulose acetate being called ‘safety’ film, over time it became clear that it also deteriorates in hazardous ways. Identification of cellulose nitrate and cellulose acetate in collections is necessary for preservation and risk management to collections and humans. Both cellulose nitrate and cellulose acetate are found in gallery, library, archive and museum photographic collections. As a result, identification and management of this material might be the responsibility of curators, librarians, archivists, collection managers, registrars or conservators, depending on the institution staffing and structure. Currently, there is no single identification method readily available to heritage institutions that meets the requirements to identify and distinguish between cellulose nitrate and cellulose acetate reliably, non-destructively and rapidly. In this study samples from both the Sydney University Museums and Australian National Maritime Museum collections have been analysed using infrared total reflectance spectroscopy, a novel approach which demonstrates great potential for rapid and non-invasive identification particularly when combined with multivariate statistical analysis methods. Principal component analysis (PCA) allowed for non-subjective data mining and the development of a model which is hoped to be used in the future for predicting sample identification. After calculation of the PCA model, the novel application of the Parsimax orthogonal rotation method potentially provides a means of determining the level of chemical deterioration in the cellulose nitrate film.

Mechanism of Huang-lian-Jie-du decoction and its effective fraction in alleviating acute ulcerative colitis in mice: Regulating arachidonic acid metabolism and glycerophospholipid metabolism

Ethnopharmacological relevanceHuang-lian-Jie-du decoction (HLJDD) is a traditional Chinese medicine prescription for clearing away heat, purging fire and detoxifying, which can be used to treat sepsis, stroke, Alzheimer's disease and gastrointestinal diseases. Our previous studies have shown that HLJDD can effectively alleviate acute ulcerative colitis (UC) in mice, and its n-butanol fraction (HLJDD-NBA) is the effective fraction. The aim of this study is to further investigate the mechanism of HLJDD and HLJDD-NBA in relieving UC in mice from a holistic perspective. MethodsThe acute UC model of BABL/c mice was induced by 3.5% (w/v) dextran sodium sulfate drinking water. At the same time of modeling, HLJDD and HLJDD-NBA were given orally for treatment respectively. During the experiment, the clinical symptoms of mice were recorded and the physiological and biochemical indexes of mice were detected after the experiment. In addition, the plasma metabolites of mice in each group were detected and analyzed by ultra-high performance liquid chromatography quadrupole time of flight mass spectrometry and multivariate statistical analysis method. Then, the potential target metabolic pathway of drug intervention was screened through the enrichment analysis of differential metabolites. Finally, we use molecular simulation docking technology to further explore the molecular regulatory mechanism of HLJDD and HLJDD-NBA on potential target metabolic pathways. ResultsHLJDD and HLJDD-NBA intervention can significantly reduce the disease activity index of UC mice, inhibit colon length shortening and pathological damage, and relieve the abnormal changes of physiological and biochemical parameters of UC mice. Moreover, HLJDD and HLJDD-NBA can significantly inhibit the metabolic dysfunction of UC mice by reversing the abnormal changes of 24 metabolites in UC mice, and the arachidonic acid metabolic pathway and glycerophospholipid metabolic pathway are the target metabolic pathways regulated by them. Further literature review and molecular simulation docking analysis showed that HLJDD and HLJDD-NBA may inhibit the disorder of arachidonic acid metabolism pathway and glycerophospholipid metabolism pathway by inhibiting COX-2 protein expression and PLA2, 5-LOX activity. ConclusionsOur experiments revealed that HLJDD and HLJDD-NBA can alleviate UC of mice by regulating arachidonic acid metabolism and glycerophospholipid metabolism, which points out the direction for further research and development of HLJDD as a new anti-ulcer drug.

Statistical analysis of dynamics of decrease in oil production after well interventions

The paper is based on the theoretical aspects of studying the success of oil production projects. To conduct clustering based on the empirical data from the producing well stock, a classifier has been compiled describing the types of interventions in 2013-2018. The selection of indicators was carried out on the basis of theoretical qualitative analysis. The following statistical indicators were included in the study information base: well profile, well diameter, field, horizon, and oil flow rate. The dynamics of the oil flow rate is translated into a comparable form using time synchronization. Since the initial attribute space is represented in various units of measurement, in order to ensure comparability, at the stage of formation of information array, standardization of indicators was carried out to the maximum. The interventions revealed the following combinations of factors which showed the greatest effect in terms of growth and total oil production: well profile, well diameter, field, as well as critical factors that can be interpreted as factors of ineffective interventions. The obtained factors were verified by various methods of multivariate statistical data analysis. The author suggests conclusions and recommendations for the most efficient interventions in oil wells.