Principal Component Cluster Analysis Research Articles

Positive relationship between plant diversity dietary patterns and disease activity in Australian adults with Inflammatory Bowel Disease

Diet is implicated in the development of Inflammatory Bowel Disease (IBD). However, the role of diet in reducing inflammation and managing prevalent disease is unclear (1–3). Previous studies have analysed the relationship between dietary patterns and occurrence of flares or symptoms, but not disease activity or inflammation (4–5). It is important to explore the role of habitual diet in management of IBD to provide targeted dietary recommendations. We explored the relationship between dietary intake with disease activity and inflammation in an Australian adult cohort with and without IBD. We analysed dietary and clinical data from the Australian IBD Microbiome (AIM) study. AIM is a prospective longitudinal cohort study of adults and children with Crohn’s Disease (CD), Ulcerative colitis (UC) and healthy controls (HC). Habitual dietary intake of food groups, fibre, polyphenols and fermented foods was collected by merging dietary data from 3-day food records and food frequency questionnaires with PhenolExplorer and the Australian Fibre Categories Database. Dietary patterns were explored using Principal Component Analysis (PCA) and cluster analysis (CA) in IBM SPSS Statistics (V29). Associations between dietary intake, clinical disease activity categorised as remission or active, and faecal calprotectin (FCAL) were explored in adult participants. A total of 412 participants (IBD = 223, HC = 189) were included. FCAL data was available for 211 participants (HC = 100, CD = 49, UC = 62). Median (IQR) FCAL at baseline was 20 (20) mg/kg for HC and 33 (127) mg/kg for IBD, indicating clinically irrelevant inflammation (FCAL >50mg/kg = clinical inflammation). PCA identified 7 distinct dietary patterns for adults with IBD. A dietary pattern of high plant diversity was associated with active CD. In the total IBD cohort, low association to a 'Prudent’ pattern was positively associated with low FCAL, and high association to a 'Meat-eaters’ dietary pattern was positively associated with moderate FCAL. CA revealed 3 distinct clusters amongst participants with IBD. No significant difference between diet cluster and disease activity or FCAL was seen. There were no significant differences in intake of fibre or polyphenols between remission vs active disease in participants with IBD. A significant difference between total, soluble and insoluble fibre and FCAL categories was seen with a higher fibre intake associated with lower FCAL. Higher plant-diversity and 'Prudent’ dietary patterns are associated with active disease and higher FCAL in Australian adults with IBD. Reverse causality cannot be ruled out, with analysis of larger cohorts and clinical trial data needed to clarify this.

Identification Markers Responsible for Differentially Processed Polygonatum cyrtonema Hua by Ultra-Performance Liquid Chromatography with Quadruple-Time-of-Flight Mass Spectrometry.

The rhizome of Polygonatum cyrtonema Hua has been used as a traditional Chinese medicine for over 2000 years. The fresh Chinese herb possesses micro toxicity and is thus traditionally alternately steamed and basked nine times to alleviate the toxicity and enhance the pharmaceutical efficacy. Different processing cycles usually result in variable therapeutic effects in the processed Polygonatum cyrtonema Hua (P-PCH). However, it can be hard to tell these various P-PCHs apart at present. To identify the P-PCHs that had undergone repeated steaming one to nine times, the chemical constituents were profiled based on Ultra-Performance Liquid Chromatography with Quadruple-Time-of-Flight Mass Spectrometry, and the Principal Component Analysis and Cluster Analysis methods were adopted to discriminate different cycles of P-PCH. A total of 44 characteristic markers were identified, which allowed the P-PCHs to be discriminated exactly.

Principal components analysis and K-means clustering of till geochemical data: Mapping and targeting of prospective areas for lithium exploration in Västernorrland Region, Sweden

To achieve the demand for elements used for the green transition energy, such as lithium, it is necessary to recognize the spatial distribution of the concentrations of these elements in different earth materials such as bedrock and soil and to identify areas with anomalous concentrations of such elements (i.e., mineralization) for further exploration and hopefully exploitation. This study carried out multivariate statistical analyses on compositional (i.e., element concentration) data from till samples to recognize areas that likely contain lithium pegmatite mineralization in the Västernorrland region, central Sweden. We applied principal components analysis (PCA) and K-means clustering techniques to reveal regional-scale patterns in the till geochemical data. We demonstrate that these two methods have potential for recognition of geochemical anomalies related to the underlying bedrock geology as well as to mineralization. The results of PCA- and K-means clustering were validated using known occurrences of lithium mineralization. Two different datasets were compared; one containing all available geochemical data and the second containing only available trace elements in the dataset and it was found that anomalous clusters of samples defined by K-means clustering have anomalous multi-element signatures defined by robust PCA. This demonstrated that principal components are the continuous solutions to the discrete cluster members for the K-means clustering. The results show that both PCA and K-means clustering of till geochemical datasets at the early stages of exploration and target generation may reveal useful information that can be used to identify potential areas for more detailed mapping or exploration activities.

HS-SPME-GC/MS Metabolomic Analysis for the Comparative Evaluation between a Plum–Apricot Hybrid and Its Parents

The “Stendesto” plumcot is the only successful Bulgarian plum–apricot hybrid having the “Modesto” apricot and the “Stanley” plum as parents. The current study reports on the metabolites and volatile organic compounds (VOCs) discovered in the three fruits. Forty-one metabolites in total, as represented by amino acids, organic acids, sugar acids and alcohols, phenolic acids, fatty acids, mono- and di-saccharides, and sterols, were identified in the samples. Additionally, sixty-five VOCs were profiled using the gas chromatography/mass spectrometry (GC/MS) analysis and HS-SPME technique. Among these VOCs, alcohols, aldehydes, esters, ketones, lactones, terpenoids, and benzene derivatives were the existing chemical classes. Not all metabolites were present in both apricot and plum, but the hybrid had managed to inherit all of the identified metabolites with the exception of γ-aminobutyric acid. This study is a first on the topic of plumcot fruit evaluation referencing its parental lines. Principal component (PCA) and hierarchical cluster (HCA) analyses further aided in revealing the differences and similarities between the “Stendesto” plum–apricot hybrid and its parents.

Metabolite profiling of potential bioactive fractions from ethanol extract of Boehmeria nivea flowers by GC–MS/MS analysis

BackgroundRamie (Boehmeria nivea) has been reported to have a wide variety of secondary metabolite compounds in its vegetative organ that have distinct organic activities. Regarding the chemical composition of flowers, there have been no study considering their pharmacological effects. PurposeThis research aims to determine profile metabolites contained in B. nivea flowers employing a metabolomics approach. Study DesignThis research was designed to obtain a metabolite database from five dissolved fractions of ramie flower using a Gas Chromatography-Mass Spectrometry (GC–MS/MS) instrument. This study includes metabolite profiles from each fraction of the spectrum obtained, which are then investigated for potential bioactivity based on previously reported studies. Metabolite profiles were developed using Principal Component Analysis (PCA) to obtain information on clustering of the compounds and determination of marker compounds from each fraction analyzed. MethodsThe active compounds of the flowers were separated utilizing the maceration method with ethanol solvent and multilevel fractionation using n-hexane, dichloromethane, ethyl acetic acid derivation, butanol, and water employing a GC–MS/MS approach. Principal Component Analysis (PCA) was optimized with a total represented data diversity range of 60 % - 65 %. This allowed for the identification of characteristic compounds and similarities in compound substances accross the fractions. ResultGC–MS/MS interpretation showed the dominant compounds contained in each fraction of n-hexane, dichloromethane, n-butanol, ethyl acetate, and water are 9,12-Octadecadienoic acid, ethanol, 2-phenoxy-, butyl ester, and hexanedioic acid, bis(2-ethylhexyl) ester. PCA clustering showed combined similarity of the compound for ethyl acetate fraction with butanol, and also butanol with n-hexane. Meanwhile, the dichloromethane and water fractions have very small content similarities. The score plot indicated marker compounds of each fraction are: ethanol, 2-butoxy; hexane, 2,2,3-trimethyl-; phthalic acid, in (2-propylpentyl) ester; n-hexadecanoic acid, and Hexanedioic acid, bis(2- ethylhexyl) ester. ConclusionsThe abundance of compounds in the fractions shows potential for application as a source of new drug candidates. In addition, there are still many compounds that are unknown in structures or biological activities which could lead to further research to provide potential clues towards the bioprospecting of the plant. AbbreviationsCH2Cl2, dichloromethane; Dm1, Dimension 1; Dm2, Dimension 2; EtOAc, ethyl acetate; GC–MS/MS, Gas Chromatography–Mass Spectrometry; n-BuOH, n-butanol; PCA, Principal Component Analysis.

Integrated application of multiple indicators and geographic information system-based approaches for comprehensive assessment of environmental impacts of toxic metals-contaminated agricultural soils and vegetables

Conventional monitoring and mapping approaches are laborious, expensive, and time-consuming because they need a large number of data and consequently extensive sampling and experimental operations. Therefore, due to the growing concern about the potential of contamination of soils and agricultural products with heavy metals (HMs), a field experiment was conducted on 77 farm lands in an area of 2300 ha in the southeast of Shiraz (Iran) to investigate the source of metal contamination in the soils and vegetables and to model spatial distribution of HMs (iron, Fe; manganese, Mn; copper, Cu; zinc, Zn; cadmium, Cd; nickel, Ni, and lead, Pb) over the region using geographic information system (GIS) and geostatistical (Ordinary Kriging, OK) approaches and compare the results with deterministic approaches (Inverse Distance Weighting, IDW with different weighting power). Furthermore, some ecological and health risks indices including Pollution index (PI), Nemerow integrated pollution index (NIPI), pollution load index (PLI), degree of contamination (Cdeg), modified contamination degree (mCd), PIaverage and PIvector for soil quality, multi-element contamination (MEC), the probability of toxicity (MERMQ), the potential ecological index (RI), total hazard index (THI) and total carcinogenic risk index (TCR) based on ingestion, inhalation, and dermal exposure pathways for adults and children respectively for analyzing the noncarcinogenic and carcinogenic risks were calculated. Experimental semivariogram of the mentioned HMs were calculated and theoretical models (i.e., exponential, spherical, Gaussian, and linear models) were fitted in order to model their spatial structures and to investigate the most representative models. Moreover, principal component analysis (PCA) and cluster analysis (CA) were used to identify sources of HMs in the soils. Results showed that IDW method was more efficient than the OK approach to estimate the properties and HMs contents in the soils and plants. The estimated daily intake of metals (DIM) values of Pb and Ni exceeded their safe limits. In addition, Cd was the main element responsible for ecological risk. The PIave and PIvector indices showed that soil quality in the study area is not suitable. According to mCd values, the soils classified as ultra-high contaminated for Cu and Cd, extremely high for Zn and Pb, very high, high, and very low degree of contamination for Ni, Mn, and Fe, respectively. 36, 60, and 4 % of the sampling sites had high, medium, and low risk levels with 49, 21, and 9 % probability of toxicity, respectively. The maximum health risk index (HRI) value of 20.42 with extremely high risk for children was obtained for Ni and the HI for adults and children were 0.22 and 1.55, respectively. The THI values of Pb and Cd were the highest compared to the other HMs studied, revealing a possible non-cancer risk in children associated with exposure to these metals. The routes of exposure with the greatest influence on the THI and TCR indices were in the order of ingestion > inhalation > dermal. Therefore, ingestion, as the main route of exposure, is the route of greatest contribution to health risks. PCA analysis revealed that Fe, Mn, Cu, and Ni may originate from natural sources, while Fe was appeared to be controlled by fertilizer, and Cu primarily coming from pesticide, while Cd and Pb were mainly associated with the anthropogenic contamination, atmospheric depositions, and terrific in the urban soils. While, Zn mainly originated from fertilization. Findings are vital for developing remediation approaches for controlling the contaminants distribution as well as for monitoring and mapping the quality and health of soil resources.

Assessment of Genetic Diversity in Aromatic Short Grain Rice (Oryza sativa L.) Genotypes using PCA and Cluster Analysis

A population panel of 90 aromatic short grain rice accessions were evaluated for 26 agro-morphological and quality traits using principal component analysis (PCA) and cluster analysis for the determination of genetic variation pattern, and identification of the major traits contributing to the diversity. First six principal components (PCs) exhibited Eigenvalue more than one with 74.4 per cent of total variability among the 26 characters. The PC1 showed 24.55% while, PC2, PC3, PC4, PC5 and PC6 exhibited 15.48 %, 11.48 %, 9.96 %, 7.89 % and 5.12 % variability, respectively among the accessions for the traits under study. The results of PCA suggested that characters such as effective tillers per plant, number of spikelets per panicle, number of filled spikelets per panicle, spikelet fertility %, milling %, head rice recovery %, kernel length and kernel length after cooking were the principal discriminatory characteristics of aromatic short grain accessions of rice. Seven divergent clusters were formed by UPGMA clustering method. The pattern of group constellation proved the existence of significant amount of variability. The intra cluster distance ranged from 0.00 (cluster VI) to 6.33 (cluster V). The inter cluster distance was maximum between cluster VI and VII (18.854) and minimum between cluster II and cluster IV (7.673). To realize much variability and high heterotic effect, parents should be selected from two clusters having wider inter-cluster distance. Considering the importance of genetic distance and relative contribution of characters towards total divergence, the present study indicated that parental lines selected from cluster VI (IGSR -3-1-5) for number of spikelets per panicle, number of filled spikelets per panicle, grain length, kernel length and length breadth ratio, and from cluster VII (Khasakani, Kolijoha) for effective tillers per plant, 1000 grain weight, grain yield per plant, harvest index, grain breadth, length breadth ratio after cooking and elongation index could be used in crossing programmes to achieve desired segregants.

Color and Nutritional Analysis of Ten Different Purple Sweet Potato Varieties Cultivated in China via Principal Component Analysis and Cluster Analysis.

Purple sweet potato (PSP) has abundant nutritional compounds, which are valuable constituents of the human diet, but its development and utilization are still in the primary processing phase. This study examined the differences in nutritional characteristics of 10 PSP varieties. A variety of nutritional components were evaluated and comprehensively compared using principal component analysis (PCA) and cluster analysis (CA). The PSP had 60.9-70.1% moisture. The dried PSP had abundant starch (43.9~67.2%) and dietary fiber (9.40~16.5%), moderate levels of protein (3.19~8.75%) and reducing sugar (1.44~4.01%), and low amounts of crude fat (0.51~1.01%). The anthocyanin profile varied significantly between the different varieties. A correlation analysis showed that a higher content of anthocyanins resulted in a darker color. The PCA and CA suggested that varieties XS, ZL, and JS18 are desirable for developing the diabetic patient's diet. JS1 had the highest anthocyanin, protein, and dietary fiber contents and the lowest starch, implying that it could be used as a source of natural colorants or functional foods. Varieties FX, GS, ES13, and EN are suitable for producing various starch-based food products, such as noodles, cookies, and pastries. This study provides a reference for the practical use and rational processing of PSP resources.

Genetic diversity of grain yield traits and identification of a grain weight gene SiTGW6 in foxtail millet.

Agronomic traits were evaluated in 1250 foxtail millet accessions, and a crucial gene SiTGW6 governing grain yield was identified. Elite haplotypes and dCAPS markers developed for SiTGW6 facilitate molecular breeding. A comprehensive evaluation of phenotypic characteristics and genetic diversity in germplasm resources are important for gene discovery and breeding improvements. In this study, we conducted a comprehensive evaluation of 1250 foxtail millet varieties, assessing seven grain yield-related traits and fourteen common agronomic traits over two years. Principal component analysis, correlation analysis, and cluster analysis revealed a strong positive correlation between 1000-grain weight and grain width with grain yield, emphasizing their importance in foxtail millet breeding. Additionally, we found that panicle weight positively correlated with 1000-grain weight but negatively correlated with branch and tiller numbers, indicating selection factors during domestication and breeding. Using this information, we identified 27 germplasm resources suitable for high-yield foxtail millet breeding. Furthermore, through an integration of haplotype variations and phenotype association analysis, we pinpointed a crucial gene, SiTGW6, responsible for governing grain yield in foxtail millet. SiTGW6 encodes an IAA-glucose hydrolase, primarily localized in the cytoplasm and predominantly expressed in flowering panicles. Employing RNAseq analysis, we identified 1439 differentially expressed genes across various SiTGW6 haplotypes. Functional enrichment analysis indicating that SiTGW6 regulates grain yield through the orchestration of auxin and glucan metabolism, as well as plant hormone signaling pathways. Additionally, we have identified elite haplotypes and developed dCAPS markers for SiTGW6, providing valuable technical tools to facilitate molecular breeding efforts in foxtail millet.

Evaluation of Water Quality of Groundwater of Sanghar District, Sindh, Pakistan: Chemical and Multivariate Analysis

Sanghar District is located in the central part of Sindh Province and shares a boarder with India to the east. This work examines the water quality of the groundwater of three subdistricts, Sanghar, Khipro, and Jan Nawaz Ali, mostly used for human consumption, cattle farming, and irrigation. A total of 74 representative samples were collected and analyzed for 26 different parameters, including anions, cations, trace, and toxic elements. The total dissolved salts (TDSs) contained in 41 samples (55.4%), the major cations, K, Na, Mg, and Ca, in 44.6–93.2% of samples, and the major anions, Cl, HCO3, and SO4, in 68.6–81.0% of samples were within the permissible guidelines of the World Health Organization (WHO). The samples containing elements Cr (24.0%), Pb (29.7%), Ni (39.2%), Cd (40.5%), As (10.8%), and F (39.18%) were above the permissible limits of the WHO. The groundwater samples were examined for water quality index (WQI), contamination index (Cd), chronic daily intake indices (CDIs), hazard quotient indices (HQ), principal component analysis (PCA), piper diagrams, Gibbs diagrams, and cluster analysis to ascertain nature of the groundwater present in the study area. The samples were also examined for suitability for irrigation by sodium percentage (Na%), sodium adsorption ratio (SAR), Kelly’s index (KI), permeability index (PI), and Wilcox diagrams. Samples in the range of 0 to 58.33% were inappropriate for irrigation.

Evaluating the Path to the European Commission’s Organic Agriculture Goal: A Multivariate Analysis of Changes in EU Countries (2004–2021) and Socio-Economic Relationships

This study comprehensively analyzed the dynamic landscape of organic farming in the European Union (EU) from 2004 to 2021, investigating the shifts in dedicated agricultural areas influenced by evolving preferences and the priorities of farmers and consumers. Examining the impact of socio-economic factors, including gross domestic product (GDP) per capita, the human development index (HDI), and human population density, this study established multivariate relationships through country-level analyses based on correlations, principal component analysis, cluster analysis, and panel analysis. Despite a universal increase in the organic agriculture areas across all the EU countries during the study period, the production levels exhibited negative correlations with the human population density, GDP per capita, and HDI. Notably, the Baltic countries and Austria led in organic farming production, while Malta, the Netherlands, Belgium, Ireland, and Luxemburg formed a distinct group in the cluster analysis with less intensive organic agriculture per capita. These insights are crucial for supporting the resilience and sustainability of organic farming as it continues to evolve. Predictions of organic agriculture share for 2030 based on trends evaluated using linear regression in the years 2004–2021 estimated about 12% of utilized agricultural area, which was much lower than the target share of the European Commission at 25%. Predictions based on linear regression showed that achieving the European Green Deal target of a 25% share of organic agriculture in unlikely in most EU countries by 2030. The target is only highly probable to be obtained in Austria, Estonia, and Sweden. The EU countries varied significantly across various indices characterizing organic agriculture, including organic agriculture area share. It should be noted that the study was conducted on the data obtained prior to the outbreak of the war in Ukraine, which could potentially alter the previous trends in the development of organic agriculture in the EU.

Working cycle condition construction for electric wheel loader based on principal component analysis and cluster analysis

With the rapid development of new energy technology, cycle condition is significant for the design of powertrain system, whole machine economy, endurance, and other performance parameters of pure electric drive machines. Many cycle conditions of the passenger car industry have been defined. While that for the loader which has complex operation modes and working conditions, has less research on this aspect. Cycle conditions suitable for loader have not been constructed. To support the research and development of electric loader power system, the cycle conditions that can reflect the driving characteristics of electric loader are studied in this paper. According to the typical “V” cycle condition of loader, the actual driving condition data are collected and the kinematic segments are divided. The kinematic segments are grouped into five categories by using principal component analysis and K-means clustering algorithm to obtain the clustering center of each category. The working cycle condition is formed by extracting the combination of segments closest to the clustering center. Finally, based on AMESim simulation and whole machine test analysis, the error between the simulation and vehicle test results of the energy consumption is 3.4%. The accuracy and effectiveness of the proposed constructed cycle conditions are verified.

Health risk assessment of heavy metals in soil and vegetables from major agricultural sites of Ohio and West Virginia

Contamination of soil and crops with heavy metals (HMs) poses a significant environmental challenge in the United States, while the status of metal contamination and associated health risks have been merely studied. Hence, this study aimed to assess metal contamination in major agricultural sites of Ohio and West Virginia, and evaluate the potential health risks associated with consuming metal-contaminated vegetables. Among the agricultural sites studied, Fe was the most predominant contaminant in all the sampling sites, ranging from 28,410 to 67,306 mg/kg of soil, exceeding the permissible limit of the US-EPA. Whereas Cd was the least contaminant, exceeding the permissible limit only in three sampling sites (ME1, ME3, and ME6), ranging from 0.13 to 2.91 mg/kg of soil. Interestingly, range of metals in vegetable samples were within the permissible limits. Principal component and hierarchical cluster analysis indicated the different natural and anthropogenic sources of metals in soil. In noncarcinogenic health risk assessment, the values of EDI, TEDI, THQ and HI levels for all the metals were lower than their daily uptake. However, CR and TCR assessment reveal significant risks associated with Ni exposure for both adults and children, with values of 3.487E-4 and 1.172E-3, and 5.362E-4 and 1.286E-3, respectively, within these agricultural sites. Therefore, regular monitoring of metal concentrations in soil and vegetables, as well as associated health risks, is strongly recommended to prevent potential health hazards in the future.

The marine conservation deposits of Monte San Giorgio (Switzerland, Italy): the prototype of Triassic black shale Lagerstätten

Marine conservation deposits (‘Konservat-Lagerstätten’) are characterized by their mode of fossil preservation, faunal composition and sedimentary facies. Here, we review these characteristics with respect to the famous conservation deposit of the Besano Formation (formerly Grenzbitumenzone; including the Anisian–Ladinian boundary), and the successively younger fossil-bearing units Cava inferiore, Cava superiore, Cassina beds and the Kalkschieferzone of Monte San Giorgio (Switzerland and Italy). We compare these units to a selection of important black shale-type Lagerstätten of the global Phanerozoic plus the Ediacaran in order to detect commonalities in their facies, genesis, and fossil content using principal component and hierarchical cluster analyses. Further, we put the Monte San Giorgio type Fossillagerstätten into the context of other comparable Triassic deposits worldwide based on their fossil content. The results of the principal component and cluster analyses allow a subdivision of the 45 analysed Lagerstätten into four groups, for which we suggest the use of the corresponding pioneering localities: Burgess type for the early Palaeozoic black shales, Monte San Giorgio type for the Triassic black shales, Holzmaden type for the pyrite-rich black shales and Solnhofen type for platy limestones.

Hydrochemical, hydrogeological, and cancer health risk analysis of groundwater due to anthropogenic activities, Hasilpur, Pakistan.

Drinking water quality deteriorates rapidly due to anthropogenic activities and rapid population growth. These activities, in developing countries, will lead to water scarcity. In Pakistan, 70% of the population has no access to safe water, and people use canal water to drink. This study performed hydrochemical, hydrogeological, and cancer risk analyses on Tahsil Hasilpur, Bahawalpur, Pakistan. Thirteen tube wells were selected for groundwater and borehole log study. Twenty-two drinking water quality parameters were analyzed using standard methods and quality checks. The borehole data (2D and 3D) shows the abundance of sand (fine and coarse) with some uniformities, which changes the groundwater quality. The results of water quality parameters show that the concentration of TDS (2064-11,159 mg/L), Cl-1 (213-4917 mg/L), As+3 (0.048-0.158 mg/L), Pb+2 (1.294-1.673 mg/L), and Cd+2 (0.008-0.053 mg/L) were beyond guideline values. The statistical analysis showed that the parameters have a moderate to strong correlation (Pearson correlation), which may be due to the same origin (ANOVA). The principal component analysis and cluster analysis confirm the multiple sources of pollutants in the groundwater of the study area. The Piper, Durov, Stiff, and Scholler diagrams confirm that the groundwater system has an abundance of Ca+2 and Mg+2 with Cl-1. The Gibbs diagram showed that the groundwater is not saturated and tends to dissolve more minerals. The hazard quotient values are above 1.0, which indicates noncancer risk severity. The HQ trend was As+3 > Pb+2 > Cd+2 > Ni+2 > Cu+2 > Cr+2 > Zn+2 > Fe+2. The cancer risk values showed that 3-5 people/100 population were exposed to cancer risk. The trend of CR was As+3 > Cd+2 > Cr+2 > Pb+2 > Ni+2. The GIP mapping of pollutants showed that the concentration of pollutants near the canals was high compared to the locations away from the canal. The overall groundwater quality is alarming and needs immediate government attention.

Chemical compositions of the essential oils of endemic wild Origanum majorana var. tenuifolium collected from three different locations during flowering and post-flowering phases in North Cyprus, Multivariate Statistical Analyses

Wild growing Origanum majorana L. var. tenuifolium Weston was collected from three different locations (Om1: Avtepe, Om2: Alevkayası, Om3: Kozanköy) in North Cyprus. The essential oils were hydrodistilled and chemical composition analyses were simultaneously performed by GC-FID and GC-MS. The average yields of essential oils were 5.46-8.14%. Major compounds of flowering Om1 oil were cis-sabinene hydrate (45.0%) and terpinen-4-ol (11.1%) while those of Om3 (Kozanköy) oil were cis-sabinene hydrate (33.3%), terpinen-4-ol (12.3%) and α-terpineol (11.5%). In the post-flowering stage, the major compounds for Om1 and Om3, respectively, were cis-sabinene hydrate (16.7% and 18.8%), terpinen-4-ol (18.1% and 14.5%) and α-terpineol (23.9% and 20.0%). However, the main compound of the essential oils of the flowering and post-flowering oil of Om2 was α-terpineol (76.9% and 51.6%). Statistical Principal Component (PCA) and Hierarchical Cluster Analyses (HCA) tools demonstrated the composition variations of the essential oils at different times and locations. Om1 and Om3 (HCA similarity level: 98.98%) were similar while Om2 (HCA similarity level: 46.32%) was discordant.

Determination, distribution, and health risk assessment of 12 heavy metals in various edible oils in Taiwan

AbstractBackgroundMinimizing the risk of heavy metal pollution is of utmost importance to ensure consumer safety. In this research, we utilized inductively coupled plasma‐mass spectrometry to determine the presence of 12 heavy metals in Taiwanese commercial edible oils, both refined and unrefined.ResultsOur analytical findings revealed significantly lower levels of heavy metals in refined oils compared with unrefined oils. Through principal component analysis and cluster analysis of 19 types of edible oils, we identified Cd, Ba, Fe, Pb, and Co as the primary variables influencing heavy metal levels in edible oils. Notably, the heavy metal contents in the edible oils did not surpass international standards. Risk assessment results indicated that unrefined oils carried higher risk levels than refined oils. The incremental lifetime cancer risk values calculated for unrefined oils exceeded 10−6 in all age groups except for the 19 ~ 65 years category, while the noncarcinogenic risks remained well below 1, regardless of refined or unrefined oils.ConclusionsOur study sheds light on the distribution characteristics of heavy metal contents in edible oils, highlighting higher levels of heavy metals and associated risks in non‐refined oils compared with refined oils. Ultimately, the presence of heavy metals in edible oils poses low health risks to the human body.

Evaluation of the impact of coal mining on surface water in the Boesmanspruit, Mpumalanga, South Africa

Surface water quality has major environmental and socioeconomic consequences, notably in terms of the country’s long-term fresh water supply. This study aimed at assessing the current state of water quality and status of the Boesmanspruit in a coal mining environment. The study used historical water quality data for a period of five years from 2017 to 2021. Aluminum, calcium, iron, manganese, magnesium, sodium, sulfate, electrical conductivity, pH, and total dissolved solids were the water quality variables selected for the study; the chosen variables were chosen based on the available secondary data. The water quality was evaluated against South African resource quality objectives, the South African water quality guidelines, and the Canadian Council of Ministers of the Environment water quality index (CCME-WQI). The data were analyzed using such as the CCME-WQI, the comprehensive pollution index (CPI), and multivariate statistics. The following parameters were above the prescribed thresholds: pH, total dissolved solids, electrical conductivity, sulfate, manganese, and iron. The CCME-WQI results showed that monitoring locations GR S26 and GR S21 near mining activities had poor water quality (40–44), whereas comprehensive pollution index (CPI) also had similar category results for the monitoring points, indicating that they were heavily polluted (2.4–4.8). The WQI showed that if certain variables, such as aluminum, iron, magnesium, sulfate, electrical conductivity, and total dissolved solids, exceed the permissible range, the water quality would deteriorate in accordance with the CPI classification. Therefore, the CPI was the best way to categorize the water quality. The principal component analysis and cluster analysis identified two primary sources of pollution which are anthropogenic and natural. The utilization of statistical analysis proved to be effective in determining the ideal quantity of significant variables within the study area. The study recommends low-cost options for reducing the effects of acid mine drainage, which includes passive mine water treatment methods using artificial wetlands.

Ecological risk assessment, source identification and spatial distribution of organic contaminants in terms of mucilage threat in streams of Çanakkale Strait Basin (Türkiye)

In this study, the spatial distributions of organic contamination stressors in water of fluvial habitats in the Çanakkale Strait (ÇS) watershed were investigated and the data were assessed in terms of human health and mucilage threat. Seven significant riverine ecosystems flowing into the ÇS were defined in the basin. Water samples were taken in the spring season (2023), when the phytoplankton communities reach their highest densities. Then they were tested for a total of 8 limnological parameters.The Nutrient Pollution Index (NPI) and Water Quality Index (WQI) were applied to assess the comprehensive quality characteristics of waters. The Hazard Quotient (HQ) and Hazard Index (HI) were applied to indicate the prospective non-carcinogenic human health risks of organic stressors. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to categorize the investigated habitats and define the sources of investigated contamination parameters. Also, Geographic Information System (GIS) was used to make an effective assessment through visualization.The determined spatial mean values of the measured variables in ÇS watershed as follows: 18.21 °C for temperature, 8.51 mg/L for DO, 4.57 NTU for turbidity, 3.95 mg/L for suspended solids, 1.11 mg/L for NO3–N, 0.012 mg/L for NO2–N; 0.173 mg/L for PO4–P and 2.32 mg/L for BOD. It has been determined that the organic pollution loads and water temperature values of the investigated sub-basins increase from the upstream to the downstream locations and Çanakkale Stream was recorded as the riskiest fluvial habitat for the ÇS watershed. According to the results of health risk assessment indices, non-carcinogenic risks of organic pollutants would not be expected for all age groups.

Three-dimensional Analysis of Maxillary Morphology in Infants with Unilateral Cleft Lip and Palate.

To three-dimensionally (3D) analyze the maxillary morphology of infants with unilateral cleft lip and palate (UCLP) and preliminarily classify the alveolar arch to assist in personalization of sequence therapy. Retrospective study. Patients with UCLP referred to outpatients' clinic. 84 nonsyndromic infants with complete UCLP were recruited (58 boys, 26 girls, mean age 29.48 days). Morphometric analysis was conducted on 3D maxillary models. Principal component analysis (PCA) and cluster analysis were combined to classify maxillary phenotypes preliminarily. The Wilcoxon Signed Rank test and the Kruskal-Wallis test were used to compare differences between variables. A P value less than .05 was considered statistically significant. The maxilla was divided into three types: narrow, homogenous and broad, accounting for 9.52%, 23.81% and 66.67% respectively. The alveolar cleft site (median value) was located in 61% of the total length of the alveolar arch. In the comparison of anterior and total alveolar lengths, the non-cleft side had longer alveolar bone than the affected side, a difference of approximately 2 mm. Pairwise comparisons of variables describing alveolar symmetry revealed significant differences in all subjects; whereas type C had poorer arch symmetry than types A and B, mainly in terms of anterior and overall symmetry. In infants with UCLP, the maxillary alveolar arch was inherently asymmetrical with partially bone missing (about 2 mm). Significant differences in alveolar bone morphology and symmetry exist between different types of infants, with individuals with broad clefts (type C, the largest proportion) having the worst maxillary development.