Statistical Principal Component Analysis Research Articles

EFFECT OF DIFFERENT SPATIAL ARRANGEMENTS OF Jatropha curcas L. ON FOOD CROPS

The objective of this study was to assess, during three years, the spatial arrangement that will enhance the morpho-productive development of Jatropha curcas L. in association systems with staple crops. It was hypothesized that the association of crops is a factor that determines the behavior of the species and the development of agroenergetic farms, considering rainy and low rainfall periods. A quasi-experimental design was used, and four treatments were tested: 1) J. curcas (Jc) in monoculture; 2) 50 % of the area with Jc and 50 % planted with food crops in rotation (CA); 3) Jc intercropped with CA; and 4) area planted with CA. A principal component statistical analysis was performed with the variables fruit yield and associated crop yield using 95 % confidence intervals from a Student’s t-test. The number of bunches and total fruit and seeds harvested from Jc were shown to be the most important and had a positive correlation for the first component in each period. As for fruit yield, the highest values were achieved in monoculture. Although high yields were not attained in CA, the relationship is regarded favorable since the use of polycultures is viable, fundamentally in the arrangement of 50 % Jc + 50 % CA, being positive the biological efficiency given by the diversification and the best equivalent land use.

Statistical analysis of principal components (PCA) in the study of the vulnerability of Heritage Churches

Built heritage is constantly at risk due to anthropogenic or natural threats. Its vulnerability involves factors related to construction form, state of conservation and urban pressure, for this reason the assessment of vulnerability depends largely on its conservation and restoration. Therefore, the identification of these factors and the analysis of the correlation between them can be an effective tool in the fulfilment of integrated plans focused on the preventive conservation of these buildings. This study focuses on churches built in America (Colombia and Guatemala) between the 16th and 18th centuries, which have been analysed by calculating a global vulnerability index. Principal Component Analysis (PCA) was employed to obtain a better observation of the set of data and vulnerability indices to evaluate the behaviour of the factors that affect the model. This first approach by PCA allows to identify similarities and differences in the factors that affect the global vulnerability of each church and facilitate the prediction of typical future behaviours to establish prevention measures applicable to various buildings, moreover this study allowed to analysing the common characteristics and threats to help cultural heritage managers to propose preventive conservation measures applied to the maintenance of heritage buildings and the generation of urban policies focused on articulated plans in risk management, that included criteria based on sustainability and resilience.

(Invited) Plasma-Synthesized Coupled Nanoplasmonic Gold for DNA Sersing

The facilitated electrochemical properties of plasma play a critical role in enhancing the synthesis and design of functional nanomaterials for the development of novel sensors. The targeted challenge is to customize and tailor functionality at multiple scales to improve detection device performance. In the realm of optoelectronics, plasmonic sensing is considered an efficient tool for investigating molecular properties, often accompanied by Raman, FTIR or Fluorescent spectroscopy. Specifically, Raman scattering, also known as SERS (surface-enhanced Raman scattering), is commonly used to probe analytes at a reduced scale using plasmonic enhancers. This technique is especially useful for rapidly and accurately investigating biologically relevant samples, such as DNA and RNA. DNA/RNA-related investigations are crucial in modern nanomedicine due to the progress made in bionanotechnology, DNA vaccines, and functional gene engineering. DNA, in particular, is highly valued by microbiologists because it contains information about the nature of bio-species. Currently, PCR-related methods dominate the field for molecular-sensitive DNA-level studies, but it is challenging to investigate using optical techniques like SERS. Despite its promise, SERS still requires technological and analytical improvements. A sophisticated plasmonic nanomaterial provides considerably improved photon scattering due to the strongly localized field confinement effect, resulting in better performance. In this report, a nanoplasmonic sensor was designed using a plasma-driven electrochemical reduction mechanism, achieved through an atmospheric pressure He-plasma jet setup operating at kHz frequency. The reactive oxidative species donated electrons via liquid-gas electrochemistry towards the Ar-delivered Au(3+)-containing microdroplets, resulting in microscaled aggregates composed of AuNPs obtained from the nebulized ionic gold liquid precursor. Due to the strong coupling between nanoparticles, a high analytical enhancement factor of about 107 was achieved, and the substrate enabled obtaining Raman fingerprints of bacterial DNA fragments (M. luteus and S. aureus, E. coli, J. lividum) at nanovolume sample quantities. Additionally, the main DNA molecular vibrational signatures associated with nucleobase motions were extracted from the collected spectra and used to reliably classify bacterial strains using statistical principal component analysis.[1] References[1] Shvalya, V., Vasudevan, A., Modic, M., Abutoama, M., Skubic, C., Nadižar, N., ... & Cvelbar, U. (2022). Bacterial DNA Recognition by SERS Active Plasma-Coupled Nanogold. Nano Letters.

Authentication of Roasted Coffee Beans via LIBS: Statistical Principal Component Analysis

Great efforts are constantly being made by industry-specific coffee agencies to standardize the certification of coffee quality. In consequence, international trade requires quick and reliable analyses because of their high cost, the risk of misclassification, the difficulty of large-scale analysis, and, most importantly, the subjectivity generated by tasters. A powerful analytical method that can be used to accurately evaluate and identify coffee varieties is Laser-Induced Breakdown Spectroscopy (LIBS). In this study, it provided a quick, cost-effective, and residue-free method commonly used in laboratories for direct analysis, determining multi-elemental composition, and exploring the organic composition of roasted coffee. The mineral composition of eight varieties of pure roasted coffee was determined using a pulsed nanosecond laser produced from a Nd:YAG laser at 1064 nm. The most important spectral variables for coffee variety identification were sequestered using LIBS coupled with a chemometric-tool-based principal component analysis (PCA). The nine main wavelengths chosen corresponded to the elements of C(I), Mg(II, I), Ca(II), Fe(I), K(I), H(I), and O(I), in addition to the CN group. The overall findings indicated that using LIBS to identify coffee varieties is feasible based on a simple, quick, and eco-friendly strategy without the requirement for complex preparation or wasting time in preparation. Such studies can help to protect the coffee market and businesses by certifying product quality. Using LIBS and full statistical illustrations with PCA, the prevention of unfair competition, protection of consumers, and determination of coffee quality can be achieved.

Повышение эффективности текстурной сегментации лесного полога по изображениям сверхвысокого пространственного разрешения

The issue of increasing the accuracy of tree canopy segmentation structures in the processing of very high spatial resolution satellite images by joint use of textural features extracted by different methods is considered. The TTSPCA method (Total Texture Statistics Principal Component Analysis) is proposed and its effectiveness is shown for a number of test cases. Estimates of TTSPCA effectiveness are obtained for the forest stands segmentation and growth types. The panchromatic Worldview-2 image of the test area (Bronnitskoye forestry, Moscow region) obtained in summer was used as remote data. To perform texture segmentation, in addition to TTSPCA, several standard second-order statistical methods and a spectral (energy) method based on the wavelet transform were also used. It is shown that almost all considered statistical and spectral methods provide forest stand segmentation with errors not exceeding 3–4 %. It has been established that the TTSPCA method makes it possible to reduce the probability of errors in the forest inventory zone, as well as to identify sections with a predominance of natural and artificial plantations with an accuracy of over 85 %. The results obtained can be further recommended for use in order to improve the system we are developing for joint spectral-textural processing of satellite images with different spatial resolutions.

Plasma-Made Nanogold for DNA Sensing with SERS

Facilitated electrochemical properties of plasma are crucial for improving functional nanomaterials synthesis and design for novel sensor development. Controlled customization and functionality tailoring at multiscale is the targeted challenge for better detection devices performance. In the modern era of optoelectronics, among various probing approaches, plasmonic sensing accompanied by Raman, FTIR or Fluorescent spectroscopy is considered a tool for the efficient investigation of molecular properties. In particular, Raman scattering is often called SERS (surface-enhanced Raman scattering) due to plasmonic enhancers, which are used to improve the probing of analytes at a reduced scale. This feature is extremely useful for biologically relevant analytical tasks when samples alike DNA and similar need to be investigated fast and accurately. DNA/RNA-related investigations are highly important in modern nanomedicine due to the solid progress of bionanotechnology, DNA vaccines, and functional gene engineering. DNA is the most valued substance for microbiologists because it contains information about bio-species' nature.[1] Currently, the number of molecular-sensitive methods for DNA-level study is quite limited, among which PCR (polymer chain reaction) related methods dominate the field. However, it is challenging to investigate it with optical techniques like SERS. Despite being promising, SERS is still under technological and analytical improvement. Better performance comes with sophisticated plasmonic nanomaterials, which provide considerably improved photon scattering due to the strongly localized field confinement effect.[2] In the current report, a well-performing nanoplasmonic sensor was designed using a plasma-driven electrochemical reduction mechanism. Exploiting atmospheric pressure He-plasma jet setup operating kHz frequency, the reactive oxidative species donated electrons via liquid-gas electrochemistry towards the Ar-delivered Au(3+)-containing microdroplets. Through this method, microscaled aggregates composed of AuNPs were obtained from the nebulized ionic gold liquid precursor. Due to the strong coupling between nanoparticles, a high analytical enhancement factor of about 107 was achieved and the substrate allowed to obtain Raman fingerprints of bacterial DNA fragments (M. luteus and S. aureus, E. coli, J. lividum) at nanovolume sample quantities. In addition, main DNA molecular vibrational signatures associated with nucleobase motions were extracted from spectra collected and used to classify the bacterial strains reliably by employing statistical principal component analysis.

Intelligent prediction of lower extremity loadings during badminton lunge footwork in a lab-simulated court.

Introduction: Playing badminton has been reported with extensive health benefits, while main injuries were documented in the lower extremity. This study was aimed to investigate and predict the knee- and ankle-joint loadings of athletes who play badminton, with "gold standard" facilities. The axial impact acceleration from wearables would be used to predict joint moments and contact forces during sub-maximal and maximal lunge footwork. Methods: A total of 25 badminton athletes participated in this study, following a previously established protocol of motion capture and musculoskeletal modelling techniques with the integration of a wearable inertial magnetic unit (IMU). We developed a principal component analysis (PCA) statistical model to extract features in the loading parameters and a multivariate partial least square regression (PLSR) machine learning model to correlate easily collected variables, such as the stance time, approaching velocity, and peak accelerations, with knee and ankle loading parameters (moments and contact forces). Results: The key variances of joint loadings were observed from statistical principal component analysis modelling. The promising accuracy of the partial least square regression model using input parameters was observed with a prediction accuracy of 94.52%, while further sensitivity analysis found a single variable from the ankle inertial magnetic unit that could predict an acceptable range (93%) of patterns and magnitudes of the knee and ankle loadings. Conclusion: The attachment of this single inertial magnetic unit sensor could be used to record and predict loading accumulation and distribution, and placement would exhibit less influence on the motions of the lower extremity. The intelligent prediction of loading patterns and accumulation could be integrated to design training and competition schemes in badminton or other court sports in a scientific manner, thus preventing fatigue, reducing loading-accumulation-related injury, and maximizing athletic performance.

Caracterización fisicoquímica de maltas de cebada disponibles para la industria de la cerveza artesanal Argentina

According to market statistics, the consumption and production of craft beer in the world and in Argentina have exponentially grown in recent years. However, there is no complete characterisation of the malts available for these small producers, even though they are one of the fundamental raw materials in beer production. The aim of this work was to characterise the base malts used in craft production, given that their physicochemical properties are a fundamental parameter for conditioning the final product. Moisture, ash, protein, lipids, total fibre and carbohydrates were determined according to EBC and AOAC methods, obtaining the following results, respectively: 5.15 g/100g ± 0.85; 1.91 g/100g ± 0.12; 10.44 g/100g ± 0.21; 1.76g/100g ± 0.17; 4.46 ± 0.62; 76.28g/100g ± 1.59. Protein profiles of the malt extracts were also characterised by vertical SDS-PAGE electrophoresis according to the Laemmli method, which have a direct impact on beer quality. Finally, a principal component statistical analysis was performed on the collected data. The values obtained are in agreement with those reported by other authors for malts of other varieties and geographical origin.

Environmental risk assessment of potentially toxic elements in Doce River watershed after mining sludge dam breakdown in Mariana, MG, Brazil.

Faced with a potential risk of a colossal amount of sludge released into the Doce River basin in the most shocking Brazilian mining disaster, we proposed to assess the environmental risk from a new perspective: Understanding the mobilization of potentially toxic elements (PTE) with the geochemical fractions. Soil and sediment samples were taken in nine sites throughout the basin and characterized. The environmental risk was assessed from the PTE sequential extraction in three fractions: soluble, reducible, and oxidizable, in addition to the pseudo-total concentration. The potential mobile fraction (PMF) showed a considerable PTE mobilization from the soil and sediment samples. Principal component statistical analysis indicated the sludge as the single source of PTE. The risk assessment depended on the fractional distribution and the PTE enrichment degree in the affected samples. The fractional distribution contributed mainly to Mn, Sb, and Pb mobility, with PMF of 96%, 81%, and 100%, respectively. The mobilization of Cd, Co, Ag, Ni, Pb, Zn, and Cu was predominantly related to the degree of enrichment. The risk assessment from the geochemical fractions pointed to the magnitude of the disaster and the dispersion of PTE with severe effects on the affected populations. Therefore, more strongly enforced regulations in the basin are needed, in addition to the urgent use of more secure containment dams. It is also essential to emphasize the transferability of the design of this study to other environmental units in mining disaster conditions.

Environmental and Health Risk Assessment Due to Potentially Toxic Elements in Soil near Former Antimony Mine in Western Serbia

Background: Anthropogenic activities have clearly affected the environment, with irreversible and destructive consequences. Mining activities have a significant negative impact, primarily on soil, and then on human health. The negative impact of the first mining activities is represented even today in the soils of those localities. Research shows that, for different types of mines, the concentrations of potentially toxic elements (PTEs) are high, especially in antimony, multi-metal and lead–zinc mines, which have adverse effects on the environment and then on human health and the economy. A large flood in 2014 in Western Serbia resulted in the breaking of the dam of the processed antimony ore dump of the former antimony mine, causing toxic tailings to spill and pollute the downstream area. Due to this accident, tailings material flooded the area downstream of the dump, and severely affected the local agriculture and population. Methods: Potentially toxic elements content, pollution indices and health indices were determined in soil samples from the flooded area, using referenced methodologies. The sources and routes of pollutants and risks were determined and quantified using statistical principal component analysis, positive matrix factorisation, and a Monte Carlo simulation. Results: The main source of As, Cd, Hg, Pb, Sb and Zn in the upper part of the study area was the tailing material. Based on the pollution indices, about 72% of the studied samples show a high risk of contamination and are mainly distributed immediately downstream of the tailings dump that was spilled due to heavy rainfall. Conclusions: Although the content of the PTEs is high, there is no non-carcinogenic risk for any PTEs except As, for which a threshold risk was determined. There is no carcinogenic risk in the study area.

Simultaneous Sensing of Multiplex Volatile Organic Compounds by Adsorption and Plasmon Dual-Induced Raman Enhancement Technique.

Developing highly efficient gas sensors with excellent performance for rapid and sensitive detection of volatile organic compounds (VOCs) is of critical importance for the protection of human health, ecological environment, and other factors. Here, a robust gas sensor based on Raman technology was constructed by an in situ grown 2D covalent organic framework (COF) on Au nanoparticles' surface in the microchannel. Dual enhancement effects are included for the as-prepared microfluidic sensor. First, acting as a gas confinement chamber, the 2D COF could effectively capture gas molecules with high adsorption capacity and fast adsorption kinetics, resulting in VOCs' preconcentration at a high level in the COF layer. At the same time, after being stacked in the microchannel, abundant hot spots were generated among the nanogaps of Au@COF NPs. The local surface plasmon resonance effect could effectively enhance the Raman intensity. Both factors contribute to the improved detection sensitivity of VOCs. As a demonstration, several representative VOCs with different functional groups were tested. The resultant Raman spectra were subjected to the statistical principal component analysis. Varied VOCs can be successfully detected with a detection limit as low as ppb level and distinguished with 95% confidence interval. The present microfluidic platform provides a simple, sensitive, and fast method for VOCs' sensing and distinguishing, which is expected to hold potential applications in the fields of health, agricultural, and environmental research.

Multivariate Statistical Analysis Based on Random Matrix to Explore the Promotion Effect of Network Resource Sharing Mode on Physical Education

Multivariate statistical analysis is a new statistical method emerging in recent years. Due to the continuous update and continuous development of Internet technology, statistical analysis of data from all walks of life is required. The method of multivariate statistics was soon applied to all walks of life, bringing great convenience to people’s work and industry. The method of multivariate statistics is used to process and evaluate the data, guide the school’s physical education work, and make suggestions for various functional units. This paper focuses on the multivariate statistical analysis of principal components and factors, uses factor analysis to comprehensively evaluate college students, and uses the variance method to make statistics on the four-year physical education performance of 141 undergraduates in grades 13 and four subjects. And based on this, the effect of different majors on their academic achievement is discussed. This paper believes that in college physical education, this method should be applied to daily physical education, and students should be rationally analyzed and scientifically evaluated. This model can make a comprehensive investigation of the education work so that their education work can be improved more effectively, and sports talents in colleges and universities can get better development.

Evaluation of the physiological potential of Panicum maximum seeds by multivariate analysis

The aim of this work was to identify efficient vigor tests for differentiating the seed lots, forecasting seedling emergence in the field and assessing the physiological quality of Panicum maximum seeds. 12 seed lots from the cultivar Tanzania and 11 seed lots from the cultivar Massai were evaluated for water content, germination, first count and germination speed index, emergence and first emergence count of seedlings in sand, root length and shoot length, analysis of SVIS® images (seedling length, vigor and uniformity index) and seedling emergence in the field. The work was conducted in a completely randomized design for tests performed in the laboratory and in randomized blocks for tests in the field. The data were subjected to analysis of variance and the means compared by Scott Knott's test at 5% probability and statistical multivariate clustering analysis and principal components analysis. The shoot and root length tests are efficient for the evaluation of the physiological potential of P. maximum cv. Massai, while the seedling length, vigor index and growth uniformity index tests using image analysis, seedling emergence in sand and first seedling emergence count in sand are efficient in assessing the physiological potential of seeds of P. maximum cv. Tanzania, and providing information similar to that of seedling emergence in the field.

Synthesis and Critical View on the Structure-Activity Relationships of N-(Substituted phenyl)-/N-Diphenylmethyl-piperazine-Based Conjugates as Antimycobacterial Agents

This research focused on a three-step synthesis, analytical, physicochemical, and biological evaluation of hybrid molecules 6a–g, containing a lipophilic 3-trifluoromethylphenyl moiety, polar carbamoyloxy bridge, 2-hydroxypropan-1,3-diyl chain and 4-(substituted phenyl)-/4-diphenylmethylpiperazin-1-ium-1-yl fragment. The estimation of analytical and physicochemical descriptors (m/zmeasured via HPLC-UV/HR-MS, log ε2 (Ch–T) from UV/Vis spectrophotometry and log kw via RP-HPLC) as well as in vitro antimycobacterial and cytotoxic screening of given compounds were carried out (i.e., determination of MIC and IC50 values). These highly lipophilic molecules (log kw = 4.1170–5.2184) were tested against Mycobacterium tuberculosis H37Ra ATCC 25177 (Mtb H37Ra), M. kansasii DSM 44162 (MK), M. smegmatis ATCC 700084 (MS), and M. marinum CAMP 5644 (MM). The impact of the 6a–g set on the viability of human liver hepatocellular carcinoma (HepG2) cells was also investigated. 1-[2-Hydroxypropyl-{(3-trifluoromethyl)- phenyl}carbamoyloxy]-4-(3,4-dichlorophenyl)piperazin-1-ium chloride (6e) and 1-[2-hydroxy- propyl-{(3-trifluoromethyl)phenyl}carbamoyloxy]-4-(4-diphenylmethyl)piperazin-1-ium chloride (6g) most effectively inhibited the growth of Mtb H37Ra (MIC < 3.80 μM). The substance 6g also showed interesting activity against MM (MIC = 8.09 μM). All obtained data served as input values for structure-activity relationship evaluations using statistical principal component analysis. In fact, the toxicity of both 6e (IC50 = 29.39 μM) and 6g (IC50 = 22.18 μM) in HepG2 cells as well as selectivity index (SI) values (SI < 10.00) prevented to consider these promising antimycobacterials safe.

Highly sensitive label-free detection of analytes at different scales using uniform graphene-nanopyramids hybrid SERS system

Surface-enhanced Raman spectroscopy (SERS) has attracted enormous interests as a biological sensing technique. However, the wide application of SERS technique suffers from the limitation of sensitivity, uniformity and universality for the analytes. Herein, a graphene-nanopyramids hybrid SERS system with high uniformity and sensitivity was used to label-freely detect analytes at different scales. Colloidal lithography and modern semiconductor nanofabrication process were employed for the fabrication of wafer-scale highly-ordered array of Au nanopyramids, which ensures the uniformity of the system. The graphene layer not only plays a role of biology-compatible supporting layer for analytes but also further enhances the Raman signals and facilitates the quantitative analysis capability of the hybrid system as an intensity gauge. For nanoscale chemical and protein molecules including Rhodamine 6G (R6G), methylene blue (MB), catalase and lysozyme with concentrations lower than 10−10 M, clear SERS signals can be obtained with analytical enhancement factors (EFs) of ~109 for R6G and MB. For micron-scale analytes, Raman fingerprints of cells, i.e. erythrocyte and leukocyte have been obtained. Moreover, using statistical principal component analysis method, one can easily distinguish different analytes based on their Raman fingerprints. The experiment results of EF are in good agreement with the theoretical values predicted by finite-difference time-domain simulation. Such a wafer-scale, highly uniform and universal system will certainly enable the wider application of SERS in multiscale label-free detection of chemicals, proteins and cells, etc. with high sensitivity and specificity.

Variation in Plasma Levels of TRAF2 Protein During Development of Squamous Cell Carcinoma of the Oral Tongue.

As early detection is crucial for improvement of cancer prognosis, we searched for biomarkers in plasma from individuals who later developed squamous cell carcinoma of the oral tongue (SCCOT) as well as in patients with an already established SCCOT. Levels of 261 proteins related to inflammation and/or tumor processes were measured using the proximity extension assay (PEA) in 179 plasma samples (42 collected before diagnosis of SCCOT with 81 matched controls; 28 collected at diagnosis of SCCOT with 28 matched controls). Statistical modeling tools principal component analysis (PCA) and orthogonal partial least square - discriminant analysis (OPLS-DA) were applied to provide insights into separations between groups. PCA models failed to achieve group separation of SCCOT patients from controls based on protein levels in samples taken prior to diagnosis or at the time of diagnosis. For pre-diagnostic samples and their controls, no significant OPLS-DA model was identified. Potentials for separating pre-diagnostic samples collected up to five years before diagnosis (n = 15) from matched controls (n = 28) were seen in four proteins. For diagnostic samples and controls, the OPLS-DA model indicated that 21 proteins were important for group separation. TNF receptor associated factor 2 (TRAF2), decreased in pre-diagnostic plasma (< 5 years) but increased at diagnosis, was the only protein showing altered levels before and at diagnosis of SCCOT (p-value < 0.05). Taken together, changes in plasma protein profiles at diagnosis were evident, but not reliably detectable in pre-diagnostic samples taken before clinical signs of tumor development. Variation in protein levels during cancer development poses a challenge for the identification of biomarkers that could predict SCCOT development.

A practical study of CITES wood species identification by untargeted DART/QTOF, GC/QTOF and LC/QTOF together with machine learning processes and statistical analysis

Illegal logging and trafficking of endangered timber species has attracted the world's major organized crime groups, with associated deforestation and serious social damage. The inability of traditional methodologies and DNA analysis to readily perform wood identification to the species level for monitoring has stimulated research on chemotyping techniques. In this study, simple wood extraction of endangered rosewoods (Dalbergia spp), amenable to use in the field, produced colorful hues that were suggestive of wood species. A more definitive study was conducted to develop wood species identification procedures using high-resolution quadrupole time-of-flight (QTOF) mass spectrometers interfaced with liquid chromatography (LC), gas chromatography (GC), and Direct Analysis in Real Time (DART). The time consuming process of extracting “identifying” mass spectral ions for species identification, contentious due to their ubiquitous nature, was supplanted by application of machine learning processes. The unbiased software mining of raw data from multiple analytical batches, followed by statistical Random Forest analysis, enabled discrimination between both anatomically and chemotypically similar Dalbergia species. Statistical Principal Component Analysis (PCA) scatterplots with 95% confidence ellipses were visually compelling in showing a differential clustering of Dalbergia from other commonly traded and lookalike wood species. The information rich raw data from GC or LC analyses offered a corroborative, legally defensible, and widely available confirmatory tool in the identification of timber species.

Metabolomics Changes of Serum and Tissues in Mice Died of Acute Tetracaine Poisoning.

Objective To study the changes of metabolites in serum and tissues （kidney, liver and heart） of mice died of acute tetracaine poisoning by metabolomics, to search for potential biomarkers and related metabolic pathways, and to provide new ideas for the identification of cause of death and research on toxicological mechanism of acute tetracaine poisoning. Methods Forty ICR mice were randomly divided into control group and acute tetracaine poisoning death group. The model of death from acute poisoning was established by intraperitoneal injection of tetracaine, and the metabolic profile of serum and tissues of mice was obtained by ultra-high performance liquid chromatography-electrostatic field orbitrap high resolution mass spectrometry （UPLC-Orbitrap HRMS）. Multivariate statistical principal component analysis （PCA） and orthogonal partial least square-discriminant analysis （OPLS-DA） were used, combined with t-test and fold change to identify the differential metabolites associated with death from acute tetracaine poisoning. Results Compared with the control group, the metabolic profiles of serum and tissues in the mice from acute tetracaine poisoning death group were significantly different. Eleven differential metabolites were identified in serum, including xanthine, spermine, 3-hydroxybutylamine, etc.; twenty-five differential metabolites were identified in liver, including adenylate, adenosine, citric acid, etc.; twelve differential metabolites were identified in heart, including hypoxanthine, guanine, guanosine, etc; four differential metabolites were identified in kidney, including taurochenodeoxycholic acid, 11, 12-epoxyeicosatrienoic acid, dimethylethanolamine and indole. Acute tetracaine poisoning mainly affected purine metabolism, tricarboxylic acid cycle, as well as metabolism of alanine, aspartic acid and glutamic acid. Conclusion The differential metabolites in serum and tissues of mice died of acute tetracaine poisoning are expected to be candidate biomarkers for this cause of death. The results can provide research basis for the mechanism and identification of acute tetracaine poisoning.

The impact of regional income inequality on residents’ health in China

Since the reform and opening up, China’s economy has been developing, residents’ income has been increasing, people’s health awareness has also been building up, and the health situation has gradually improved. However, with the development of Chinese economy, the problem of income inequality also follows, which has an impact on the health of Chinese residents. In particular, the imbalance of regional economic development and the difference between urban and rural residents’ disposable income inequality have a negative impact on the health of residents, which is not conducive to sustainable economic development and social harmony and stability. This paper analyzes the correlation between regional income inequality and residents’ health status, uses descriptive statistical analysis and principal component analysis to do empirical analysis on the relationship between regional income inequality and residents’ health status, and selects the data from “China Health Statistics Yearbook 2019”. The results of empirical analysis show that regional income inequality has an impact on the health of residents in China. The health level of residents in high-income areas is higher than that in low-income areas. The economic development of the eastern and northern regions is better than that of the central and western regions, and the health status of the residents in the eastern and northern regions is better than that in the central and western regions. In addition, urban-rural income inequality also has an impact on the health of residents. The data show that the health status of urban residents is better than that of rural residents. It can be seen that there is a positive correlation between income level and residents’ health level, and a negative correlation between income inequality and residents’ health level. That is, the higher the income, the higher the health level of residents, the lower the income, the lower the health level of residents; the higher the degree of income inequality, the worse the health status of residents, and it is not conducive to the overall improvement of the health level of residents.

Разработка индексов трофности для Онежского озера с помощью экологической информационной системы

GIS technologies were used to study the spatial distribution of zones with different trophic conditions in the water area of Lake Onego. Empirical data on the trophic conditions of waters sampled in 1982–2020 were as the basis for the map development. Two periods, before and after 2000, were considered separately to assess the impact of climate change. As indicators of trophic conditions, the number of saprophytic bacterioplankton, biomass of crustacean zooplankton, concentration of chlorophyll “a” and water transparency were used. All characteristics were converted to a normal distribution, and the values at individual points were interpolated for the entire water area of the lake, which was presented as a network of 544 squares. Each cell of the water area was characterized by four values of indicators. To assess trophic conditions, multivariate statistics (principal component analysis) and an expert system (Bayesian classifier) were used. The first principal component can be considered as the integral value of trophic conditions in a given part of the water area. The Bayesian classification method allows to get an estimate of the probability of classifying each cell in the water area as “high trophic conditions” and “low trophic conditions” and assign a significance threshold (p = 0.05). The cartograms built on the basis of both methods are basically the same. Most of Lake Onego area is characterized as low trophic conditions. The vast area with high trophic conditions was identified in the northwestern part of Lake Onego where on the coast large cities are located (Petrozavodsk and Kondopoga), which is associated with active anthropogenic pollution in the last century. In recent years, due to industry recession, trophic conditions in this area has sharply decreased, with the exception of Kondopozhskaya Bay, where there is an increase in water pollution as a result of trout farms.