Results Of Principal Component Analysis Research Articles

Influence of plasma collection tubes on N-glycome in human blood samples

Background and aimsQuantitative analysis of plasma N-glycome is a promising method for identifying disease biomarkers. This study aimed to investigate the impact of using blood collection tubes with different anticoagulants on plasma N-glycome. Materials and methodsWe used a robust mass spectrometry method to profile plasma N-glycomes in two cohorts of healthy volunteers (cohort 1, n = 16; cohort 2, n = 53). The influence of three commonly used blood collection tubes on fully characterized N-glycomic profiles were explored. ResultsPrincipal component analysis revealed distinct clustering of blood samples based on the collection tubes. Pairwise comparisons demonstrated significant differences between EDTA and heparin plasma in 55 out of 82 quantified N-glycan traits, and between EDTA and citrate plasma in 62 out of 82 traits. These differences encompassed various N-glycan features, including glycan type, sialylation, galactosylation, fucosylation, and bisection. Trends in N-glycan variations in citrate and heparin plasma were largely consistent compared to EDTA plasma. In correlation analysis (EDTA vs. heparin; EDTA vs. citrate), Pearson's correlation coefficients were consistently higher than 0.7 for the majority of N-glycan traits. ConclusionSample matrix variations impact plasma N-glycome measurements. Caution is crucial when comparing samples from different plasma collection tubes in glycomics projects.

HPLC Fingerprint Analysis of Cibotii rhizoma from Different Regions and Identification of Common Peaks by LC-MS.

To establish the fingerprint of Cibotii rhizoma using high-performance liquid chromatography (HPLC) and evaluate the quality of Cibotii rhizoma from different regions using chemometrics to identify the potential quality markers, thirteen batches of Cibotii rhizoma samples were analyzed. the similarity evaluation system of TCM chromatographic fingerprint similarity evaluation was used to confirm common peaks. The SPSS 27 software was used for hierarchical cluster analysis (HCA), and SIMCA 14.1 software was used for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Moreover, a batch of Cibotii rhizoma was selected for LC-MS analysis and speculated on 15 common components. HPLC fingerprint were established, 15 common peaks were matched, two chromatographic peaks were identified using standard substances (protocatechuic acid and protocatechuic aldehyde), and 13 common components were inferred through liquid chromatograph-mass spectrometer (LC-MS). The 13 batches of the samples showed good similarities (>0.910). The results of HCA, PCA and OPLS-DA showed that 13 batches of samples were divided into three groups, and different markers were selected. The method is simple, rapid and reproducible, and can provide a reference for the overall quality evaluation of Cibotii rhizoma.

Effects of Aerated Drip Irrigation on the Soil Nitrogen Distribution, Crop Growth, and Yield of Chili Peppers.

In order to study the soil nitrogen (N) distribution pattern in the root zone of chili peppers under aerated drip irrigation (ADI) conditions and analyze the relationship between soil N distribution and crop growth, two irrigation methods (conventional drip irrigation and ADI) and three N levels (0, 140, and 210 kg hm-2) were set up in this experiment. Soil samples were collected by the soil auger method at the end of different reproductive periods, and the uniformity coefficient of soil N in the spatial distribution was calculated by the method of Christiansen's coefficient. The growth status and soil-related indices of pepper were determined at each sampling period, and the relationships between soil N distribution and chili pepper growth were obtained based on principal component analysis (PCA). The results showed that the spatial content of soil nitrate-N (NO3--N) fluctuated little during the whole reproductive period of chili peppers under ADI conditions, and the coefficient of uniformity of soil NO3--N content distribution increased by 5.29~37.63% compared with that of conventional drip irrigation. The aerated treatment increased the root length and surface area of chili peppers. In addition, the ADI treatments increased the plant height, stem diameter, root vigor, and leaf chlorophyll content to some extent compared with the nonaerated treatment. The results of PCA showed that the yield of chili peppers was positively correlated with the uniformity coefficient of soil NO3--N, root vigor, and root length. ADI can significantly improve the distribution uniformity of soil NO3--N and enhance the absorption and utilization of N by the root system, which in turn is conducive to the growth of the crop, the formation of yields, and the improvement of fruit quality.

Development of a novel five dye insertion/deletion (INDEL) panel for ancestry determination.

The use of genetic markers, specifically Short Tandem Repeats (STRs), has been a valuable tool for identifying persons of interest. However, the ability to analyze additional markers including Single Nucleotide Polymorphisms (SNPs) and Insertion/Deletion (INDELs) polymorphisms allows laboratories to explore other investigative leads. INDELs were chosen in this study because large panels can be differentiated by size, allowing them to be genotyped by capillary electrophoresis. Moreover, these markers do not produce stutter and are smaller in size than STRs, facilitating the recovery of genetic information from degraded samples. The INDEL Ancestry Informative Markers (AIMs) in this study were selected from the 1000 Genomes Project based on a fixation index (FST) greater than 0.50, high allele frequency divergence, and genetic distance. A total of 25 INDEL-AIMs were optimized and validated according to SWGDAM guidelines in a five-dye multiplex. To validate the panel, genotyping was performed on 155 unrelated individuals from four ancestral groups (Caucasian, African, Hispanic, and East Asian). Bayesian clustering and principal component analysis (PCA) were performed revealing clear separation among three groups, with some observed overlap within the Hispanic group. Additionally, the PCA results were compared against a training set of 793 samples from the 1000 Genomes Project, demonstrating consistent results. Validation studies showed the assay to be reproducible, tolerant to common inhibitors, robust with challenging casework type samples, and sensitive down to 125 pg. In conclusion, our results demonstrated the robustness and effectiveness of a 25 loci INDEL system for ancestry inference of four ancestries commonly found in the United States.

Face anthropometry for filtering facepiece respirators: analysis of the association between facial dimensions and respirator fit.

Ensuring proper respirator fit for individuals remains a persistent challenge in occupational environments, yet there is limited knowledge about how respirators interact with the face to "'fit." Previous studies have attempted to understand the association between face dimensions and respirator fit using traditional head/face anthropometry not specifically tailored for respirators. The purpose of this study was to assess and compare the ability of filtering facepiece respirator (FFR)-specific face anthropometry with traditional head/face anthropometry in exploring the relationship between facial dimensions and the fit of FFR. The study utilized 3D face scans and quantitative fit factor scores from 56 participants to investigate the relationship between face anthropometry and FFR fit. Both FFR-specific and traditional anthropometric measurements were obtained through 3D anthropometric software. Intra-correlation of anthropometry was analyzed to evaluate the efficiency and effectiveness of FFR-specific and traditional anthropometry respectively. Principal component analysis (PCA) was conducted to test the usefulness of the PCA method for investigating various facial features. Logistic regression was used to develop fit association models by estimating the relationship between each face measurement set and the binary outcome of the fit test result. The prediction accuracy of the developed regression models was tested. FFR-specific face anthropometry consists of a set of measurements that can inform the detailed facial shape associated with the FFRs more effectively than traditional head/face anthropometry. While PCA may have been effective in reducing the variable dimensions for the relatively large parts of the human body such as upper and lower bodies in previous literature, PCA results of FFR-specific and traditional anthropometry were inconsistent and insufficient to describe face dimensions with complex anatomy in a small-detailed area, suggesting that facial shape should be understood through a variety of approaches including statistical methods. Logistic regression analysis results confirmed that the association models of FFR-specific face anthropometry were significant with higher prediction accuracy and had a better model's goodness of fit than those of traditional head/face anthropometry in 3 conditions inputting all measurements, all PC scores, or top 5 measurements from PCA. The findings showed that the FFR fit association model enables an understanding of the detailed association between face and respirator fit and allows for the development of a system to predict respirator fit success or failure based on facial dimensions. Future research would include testing the validity of the model and FFR-specific measurement set on different respirator types, expanding the population set, and developing an integrated approach using automated and machine learning technologies to inform FFR selection for occupation workers and the general population.

Spatiotemporal ecological risk evaluation and source identification of heavy metals and nutrients in the water and lake surface sediment in a protected catchment area of a volcanic lake.

Indonesia has numerous lakes; however, research on the spatiotemporal sediment quality and source identification in lakes remains limited. The overaccumulation of heavy metals and nutrients in lakes severely threatens aquatic ecosystems. This study aims to identify potential sources of metallic deposits (Cu, Pb, Cr, Fe, Al, and Cd) and nutrients (TN and TP) in lake-surface sediment, utilizing enrichment factors (EF), geoaccumulation indices (Igeo), potential ecological risk indices (Er), and risk indices (RI). Multivariate statistical analyses, including principal component analysis (PCA) and Pearson's correlation analysis, were conducted to pinpoint pollution sources linked to land use. Eight sampling sites for surface sediment and water were examined in both wet and dry seasons at Menjer Lake, chosen for its diverse applications in tourism, hydropower, floating net cages, and extensive agriculture in its catchment. Correlation and PCA results indicated that Pb, Fe, and Al mainly originate from tourism, while Al, Fe, TN, and TP are associated with agriculture. The highest average loading from land use was observed in agriculture (> 0.8), floating net cages (> 0.76), and tourism (> 0.68). Furthermore, the highest loading from nutrients and all metals were TP (> 0.71) and all metals (> 0.35), respectively. Ecological risk assessment revealed low to moderately polluted EFs and Igeo in the dry season. However, Menjer Lake's Er and RI for heavy metals were generally classified as unpolluted.

Satellite tracking of American Woodcock reveals a gradient of migration strategies

Abstract Diversity in behavior is important for migratory birds in adapting to dynamic environmental and habitat conditions and responding to global change. Migratory behavior can be described by a variety of factors that comprise migration strategies. We characterized variation in migration strategies in American Woodcock (Scolopax minor), a migratory gamebird experiencing long-term population decline, using GPS data from ~300 individuals tracked throughout eastern North America. We classified woodcock migratory movements using a step-length threshold, and calculated characteristics of migration related to distance, path, and stopping events. We then used principal components analysis (PCA) to ordinate variation in migration characteristics along axes that explained different fundamental aspects of migration, and tested effects of body condition, age-sex class, and starting and ending location on PCA results. The PCA did not show evidence for clustering, suggesting a lack of discrete strategies among groups of individuals; rather, woodcock migration strategies existed along continuous gradients driven most heavily by metrics associated with migration distance and duration, departure timing, and stopping behavior. Body condition did not explain variation in migration strategy during the fall or spring, but during spring adult males and young females differed in some characteristics related to migration distance and duration. Starting and ending latitude and longitude, particularly the northernmost point of migration, explained up to 61% of the variation in any one axis of migration strategy. Our results reveal gradients in migration behavior of woodcock, and this variability should increase the resilience of woodcock to future anthropogenic landscape and climate change.

Novel principal component analysis tool based on python for analysis of complex spectra of time-of-flight secondary ion mass spectrometry

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis tool, which can simultaneously provide elemental, isotopic, and molecular information with part per million (ppm) sensitivity. However, each spectrum may be composed of hundreds of ion signals, which makes the spectra data complex. Principal component analysis (PCA) is a multivariate analysis technique that has been widely used to figure out the variances among samples in ToF-SIMS spectra data analysis and is showing great success in the explanation of complex ToF-SIMS spectra. So far, several software tools have been developed for PCA of ToF-SIMS spectra; however, none of them are freely available. Such a situation leads to some difficulties in extending applications of PCA to various research fields. More importantly, it has long been challenging for common researchers to understand PCA plots and extract chemical differences among samples. In this work, we developed a new and flexible software tool (named “advanced spectra pca toolbox”) based on python for PCA of complex ToF-SIMS spectra along with an easy-to-read manual. It can generate data analysis reports automatically to explain chemical differences among samples, allowing less experienced researchers to easily understand tricky PCA results. Moreover, it is expandable and compatible with artificial intelligence/machine learning functions. Pure goethite and different lignin adsorbed goethite samples were used as a model system to demonstrate our new software tool, proving that our software tool can be readily used in complex spectra data processing. Our new software tool is open-source, convenient, flexible, and expandable. We expect this open-source tool will benefit the ToF-SIMS community.

Positive matrix factorization of seasonally resolved organic aerosol at three different central European background sites based on nuclear magnetic resonance Aerosolomics data

Concentration data derived from 1H NMR analysis of the water-soluble organic compounds from fine aerosol (PM2.5) at three Central European background stations, Košetice, Frýdlant (both in the Czech Republic), and Melpitz (Germany), were used for detailed source apportionment analysis. Two winter and two summer episodes (year 2021) with higher organic concentrations and similar wind directions were selected for NMR analyses. The concentration profiles of 61 water-soluble organic compounds were determined by NMR Aerosolomics and a principal component analysis (PCA) was performed on this dataset. Based on the PCA results, 23 compounds were selected for positive matrix factorization (PMF) analysis in order to identify dominant aerosol sources at rural background sites in Central Europe. Both the PCA and the subsequent PMF analyses clearly distinguished the characteristics of winter and summer aerosol particles.In summer, four factors were identified from PMF and were associated with biogenic aerosol (61–78 %), background aerosol (9–15 %), industrial biomass combustion (7–13 %), and residential heating (5–13 %). In winter, only 3 factors were identified - industrial biomass combustion (33–49 %), residential heating (37–45 %) and a background aerosol (8–30 %). The main difference was observed in the winter season with a stronger contribution of emissions from industrial biomass burning at the Czech stations Košetice and Frýdlant (47–49 %) compared to the Melpitz station (33 %). However, in general, there were negligible differences in identified sources between stations in the given seasons, indicating a certain homogeneity in PM2.5 composition within Central Europe at least during the sampling periods.

P122 Digital spatial profiling reveals the predictive characteristics in patients with immune checkpoint inhibitor-induced colitis

Abstract Background The increasing use of immune checkpoint inhibitors (ICIs) against cancers is inevitably accompanied with immune-related adverse events (irAEs), and irAE colitis is one of the common types. The overactivation of CD8+ T cells plays a central role in irAEs. However, the local mechanisms related to irAE colitis remain unclear. Hence, we aimed to explore the transcriptional characteristics related to CD8+ T cell responses of irAE colitis. Methods Colon biopsy samples were collected from 6 irAE colitis patients and 5 non-irAE controls after ICIs treatment. Leveraging digital spatial profiling, 23 regions of interest (ROI) enriched with CD8+ T cells were selected and divided into 3 groups, which were non-irAE, pre-treatment irAE and post-treatment irAE. Differentially expressed genes (DEGs), protein-protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) were performed to identify the hub genes, and they were further verified by external single-cell RNA-seq data. Also, the immune infiltration analysis was performed (Figure 1A). Results Principal component analysis (PCA) indicated similar distribution among the 3 groups (Figure 1B). The comparisons of non-irAE vs. pre-treatment irAE, and pre-treatment irAE vs. post-treatment irAE yielded 105 and 210 DEGs respectively. WGCNA resulted in 14 clustering modules, with MEturquoise and MEmagenta exhibiting the highest correlation scores (Figure 1C). Importantly, 174 key genes were identified and prominently associated with various mitochondrial processes e.g. oxidative phosphorylation (Figure 1D). PPI analysis revealed a sub-module consisting of 22 nodes in MCODE. Further overlap analysis of CytoHubba and DEGs pinpointed 9 hub genes. Remarkably, COX5B was found at the intersection of both groups (Figure 1E). External single-cell RNA-seq data verified that all the hub genes were significantly highly expressed in the irAE colitis group except COX6A1 (Figure 1F). Besides, immune infiltration analyses revealed the higher levels of endothelial cells (P = 0.043) and fibroblasts (P <0.01) by SpatialDecon, and activated dendritic cells (P <0.001) by xCell in pre-treatment irAE, compared to non-irAE (Figure 1G). Further, immune cells correlation analysis demonstrated highly similar patterns of endothelial cells, fibroblasts, pDCs, and macrophages (Figure 1H). Conclusion COX5B related to mitochondrial processes characterized irAE colitis. Expansion of dendritic cells and stromal compartments including endothelial cells and fibroblasts may contribute to irAE development.

Fatty acid composition and lipid nutritional quality of adult and pediatric enteral diets

This study evaluates the fatty acid composition and lipid nutritional quality of enteral diets (ED), focusing on patients requiring enteral nutrition (EN). EN is crucial for patients unable to take adequate oral nutrition. The emphasis is on the importance of unsaturated fatty acids, particularly those from the omega-6 (n-6) and omega-3 (n-3) series, present in diets. These fatty acids are associated with health benefits, such as preventing cardiovascular disease and modulating the immune system. Seven adult ED and three pediatric ED were analyzed, using techniques such as gas chromatography to identify the fatty acid profile. Chemometric analysis, including Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), was employed to explore patterns and similarities between samples. The PCA results indicate that there were similarities and distinctions between the samples studied, especially samples DA6 and DA7. HCA corroborated these observations, identifying four distinct groups. The analysis of lipid nutritional quality indices revealed that some samples meet the recommended proportions of fatty acids, while others present discrepancies, especially in relation to the ∑n-6/∑n-3 and ∑PUFA/∑SFA ratios. These findings emphasize the importance of considering the lipid composition of ED, to ensure adequate nutrition and benefit the health of EN-dependent patients. Understanding these aspects contributes to more informed choices when prescribing enteral diets, optimizing clinical results.

Microbial contaminated paper substrate: UV–Vis–NIR spectra of model systems

This study aims to study the possibility of distinguishing the UV–Vis–NIR spectra of filamentous fungi on a paper substrate from the background. Model samples of five filamentous fungi were used: Alternaria alternata, Aspergillus niger, Cladosporium herbarum, Penicillium chrysogenum, and Trichoderma atroviride. The model samples were cultivated on paper substrates, and two methods, cross-validation (CV) and principal component analysis (PCA), were utilised to compare their spectra with the reference background spectra. The results of the CV analysis indicated that certain combined spectra sets of Cladosporium herbarum, Penicillium chrysogenum, Trichoderma atroviride, Aspergillus niger, and Alternaria alternata, at specific surface concentrations, exhibited two active components, signifying distinguishable differences from the background spectra. Additionally, the score scatter diagrams derived from PCA revealed clusters of samples, further confirming the distinguishability of the filamentous fungi spectra from the background. However, for Trichoderma atroviride, the scatter diagram demonstrated a relatively large scattering of points, impeding the resolution of spectra with a surface concentration of 2 105 cm−2 due to measurement inaccuracies. Based on the combined results of CV and PCA, the study concluded that the lower threshold of measurability for UV–Vis–NIR spectra varied among the different filamentous fungi. For example, Cladosporium herbarum, Penicillium chrysogenum, and Trichoderma atroviride exhibited a threshold around a surface concentration of 2 106 cm−2, while Aspergillus niger had a threshold around 2 105 cm−2, and Alternaria alternata had a threshold around 2 103 cm−2. In summary, this research provides insights into the distinguishability of filamentous fungi spectra on the paper substrate from background spectra using spectral analysis techniques, offering potential applications in fungal identification and characterisation.

Selection of Landsat-8 Operational Land Imager (OLI) Optimal Band Combinations for Mapping Alteration Zones

In typical alteration extraction methods, e.g., band math and principal component analysis (PCA), the bands or band combinations unitized to extract altered minerals are usually selected based on empirical models or previous rules. This results in significant differences in the alteration of mineral mapping even in the same area, thus greatly increasing the uncertainty of mineral resource prediction. In this paper, an intelligent alteration extraction approach was proposed in which an optimization algorithm, i.e., a genetic algorithm (GA), was introduced into the PCA; this approach is termed GA-PCA and is used for selecting the optimized band combinations of mineralized alterations. The proposed GA-PCA was employed to map iron oxides and hydroxyl minerals using the most commonly adopted multispectral data, i.e., Landsat-8 OLI data, at the Lalingzaohuo polymetallic deposits, China. The results showed that the spectral characteristics of GA-PCA-selected OLI band combinations in the research area were beneficial for enhancing alteration information and were more capable of suppressing the interference of vegetation information. The mapping alteration zones using the GA-PCA approach had a higher agreement with known ore spots, i.e., 25% and 33.3% in ferrous-bearing and hydroxyl-bearing deposits, compared to the classical PCA. Furthermore, two predicted targets (not shown in the classical PCA results) were precisely obtained via analyzing the GA-PCA alteration maps combined with the ore-forming geological conditions of the mine and its tectonic characteristics. This indicated that the intelligent selection of mineral alteration band combinations increased the reliability of remote sensing-based mineral exploration.

Integrating chemical similarity and bioequivalence: an overall evaluation of the quality consistency of traditional decoction and dispensing granule decoction of Amomum villosum

ABSTRACT: Objective This study aims to investigate the quality consistency between traditional decoction (TD) of Amomum villosum and its dispensing granule decoction (DGD). Fifteen batches of TD and nine batches of dispensing granules (manufactured by A, B, and C) were prepared and evaluated for their consistency. Methods Firstly, The chemical similarity of TD and DGD was examined using GC and HPLC, coupled with hierarchical cluster analysis (HCA), criteria importance though intercrieria correlation(CRITIC) weighting method, and principal component analysis (PCA). Secondly, the gastrointestinal motility experiments in mice, along with the CRITIC weighting method, were employed to assess the bioequivalence of TD and DGD of Amomum villosum. Finally, the entropy weight technique-gray relative analysis(GRA) method was used to compare the quality of Amomum villosum decoctions. Results ①The CRITIC weighting method indicated significantly higher scores for TD than DGD (p < 0.01). HCA and PCA results demonstrated a clear distinction between TD and DGD. ②Gastrointestinal motility test results revealed no significant difference between TD and DGD in other indicators (p > 0.05).③Gray relative analysis results showed that the relative correlation of TD was more significant than that of DGD. Conclusion The chemical composition of DGD and TD differed. The biological activity of DGD-A/B was consistent with that of TD, while the difference between DGD-C and TD was significant. A comprehensive evaluation showed that TD exhibited better quality than DGD. DGD manufacturers should optimize the preparation process to enhance product quality.

Multi-layered physical factors govern mercury release from soil: Implications for predicting the environmental fate of mercury

Despite the recognised risks of human exposure to mercury (Hg), the drivers of gaseous elemental mercury (GEM) emissions from the soil remain understudied. In this study, we aimed to identify the environmental parameters that affect the GEM flux from soil and derive the correlations between environmental parameters and GEM flux. Principal component analysis (PCA), factor analysis (FA), and structural equation modelling (SEM) were performed on samples from forest and non-forest sites. The associated results revealed the impact of each environmental parameter on GEM flux, either due to the interaction between the parameters or as a coherent set of parameters. An introductory correlation matrix examining the relationship between two components showed a negative correlation between GEM flux and atmospheric pressure at the two sites, as well as strong correlations between atmospheric pressure and soil temperature. In cases of non-forest open sites with no trees, the PCA and FA results were consistent, indicating that atmospheric pressure, solar irradiance, and soil moisture—defined as primary causality—are largely independent drivers of GEM flux. In contrast, the PCA and FA results for the forest areas with high humidity, tree coverage, and shade were inconsistent, confirming the hypothesis that primary causality affects GEM flux rather than consequent parameters driven by primary causality, such as air and soil temperature and atmospheric humidity. The SEM results provided further evidence for primary and consequent causality as crucial drivers of the GEM flux. This study demonstrates the importance of key primary parameters, such as atmospheric pressure, solar irradiance, and soil moisture content, that can be used to predict mercury release from soils, as well as the importance of consequent parameters, such as air and soil temperature and atmospheric humidity. Monitoring the magnitude of these environmental parameters alone may facilitate the estimation of mercury release from soils and be useful for detailed modelling of soil–air Hg exchange.

Groundwater Assessment in Udham Singh Nagar, Uttarakhand, India Using Multivariate Statistical Techniques, WQI, and HPI

We conducted a study to assess the variations in groundwater quality and metal pollution and identify the sources in the U S Nagar district of Uttarakhand state of India using multivariate statistical techniques. The two essential indicators of any developed society are Safe drinking water and decontamination. This research aims to undertake drinking water quality analyses of the groundwater and the sources of contamination in Udham Singh Nagar district, Uttarakhand. We produced results of 250 samples collected from hand pumps (Govt. and Private) and artesian wells. We measured 19 parameters which nine physicochemical parameters (pH, electrical conductivity, total dissolved solids, dissolved oxygen, oxidation and reduction potential, salinity, fluoride, chloride, nitrate), 7 Heavy metals (Lead, nickel, chromium, copper, iron, manganese, zinc) along with three metals (potassium, magnesium, sodium). Water quality index, Heavy metal pollution index, PCA (Principal component analysis)/FA (factor analysis), and CA (Cluster analysis) methods were applied. WQI index shows five samples (2 %) comes under the excellent, 211 samples (84.4 %) fall under good quality, and 34 samples (13.6 %) have poor water quality wqi status as per Yadav index. Further, referring to the Ramakrishnaiah index, 216 samples (86.4 %) fall under excellent quality and only 13 samples (13.6 %) come under good water quality. For HPI, as per Indian Standard, nearly 40.4% of samples show a low degree of pollution, 33.2% of samples show a medium degree of pollution, and 26.4% show a High degree of pollution. According to the International HPI standard, 46% of samples show a low degree of pollution, 38% have a medium degree, and 16% show a high-grade degree of pollution. The results of PCA show that groundwater has mainly geogenic (geochemical alteration and weathering of source rock like carbonate, dolomite, quartzite, etc.) followed by anthropogenic sources (agrogenic, domestic sewage and industrial wastes etc.). The results obtained through the PCA are also moderately supported by Cluster analysis. The cations which were over the limit in groundwater samples are presented in chronological order Fe &gt; Pb &gt; Ni &gt; Mg &gt; Mn &gt; Zn &gt; Cu, and significant anions were over the limit F¯ &gt; Cl¯, and the rest was under the limit. The highly heavy metal-contaminated drinking groundwater sample, being used for drinking purpose, is deteriorating and need a proper treatment strategy before domestic water supply.

Disentangling the response of vegetation dynamics to natural and anthropogenic drivers over the Qinghai-Tibet Plateau using dimensionality reduction and structural equation model

The Qinghai-Tibet Plateau (QTP), also referred to as the “roof of the world”, is a distinct alpine ecosystem located at an average elevation of more than 4000 m. Climate variation and anthropogenic activity have affected vegetation development on the QTP during the past few decades, but it is still debatable how significant a role any of these causes has played. According to land surface air temperature data around the world, during the past 50 years, temperatures on the QTP have risen twice as rapidly as the average global level. Previous studies have employed the residual method to examine the impacts of anthropogenic activities on vegetation development, with a lack of actual data validation and less attention to explaining the complex interactions and pathways between vegetation growth, natural factors (climate change and topography), and anthropogenic drivers. Furthermore, although the relationships between vegetation dynamics and terrain have been addressed, the interactions in which meteorological factors influence vegetation growth in accordance with topographical variations remain inadequately elucidated. The raster datasets employed in this study include MODIS NDVI, meteorology, topography, as well as anthropogenic activity from 2000 to 2018. A suite of methods including partial correlation coefficient (PCC), principal component analysis (PCA), and partial least squares structural equation model (PLS-SEM) were applied to explore the interrelationships between various influencing factors and regional vegetation development over the past two decades. The results suggested that: (1) an elevation-specific temperature threshold existed, indicating that temperature could promote or inhibit vegetation growth depending on whether it is below or above this threshold, respectively, which was controlled by a trendline equation; (2) the PLS-SEM and PCA results showed that vegetation development was dominantly promoted by temperature and was most sensitive to precipitation; (3) topography, especially elevation, affected vegetation growth indirectly by influencing climatic change, while anthropogenic drivers had positively contributed to vegetation dynamics on the QTP under the influence of implementing ecological management such as the nature reserves establishment.

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis.

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

Characterization of mono‐varietal virgin olive oils obtained from Turkish National Germplasm Bank according to fatty acid composition, volatile profile, and sensory properties

AbstractMono‐varietal virgin olive oils were obtained from Olea europaea L. olives grown in the National Olive Germplasm Bank in the same agronomic conditions for two harvesting years. Samples were characterized according to fatty acid composition, volatile profile, and sensory attributes, and varieties were compared using Pearson correlation, principal component analysis (PCA), and cluster analysis. Odor activity values (OAVs) of the volatiles were calculated to discuss relationships between individual sensory properties and volatiles. Although most of the samples overlapped on the score plots, some samples were located away from the others. The sample groupings on the dendrogram plotted using Ward's linkage were generally suitable with PCA results. Ethanol had a negative correlation with 9 out of 14 pleasant attributes. According to OAVs, trans‐2‐hexenal content had the most important effect on sensory attributes and positively correlated with flower odor, whereas hexenal content contributed to sweet attributes. Similarly, the effect of 1‐hexanol content strengthened the flower attribute but weakened the bitter taste. These findings were also confirmed by separating some varieties rich in those volatiles on the PCA bi‐plot of sensory attributes. Samples were grouped on a dendrogram in accordance with total ester and total C3, C5, and C6 acid contents.Practical Application: Most olive production in Turkey is based on a few varieties, whereas 95 different endemic varieties were registered by Turkish National Germplasm Bank. This study provides information on the volatile composition, sensory profile, and fatty acid composition of virgin olive oils produced from some rare varieties with high oil content. Moreover, relationships and correlations between individual volatiles and sensory attributes were evaluated, and the main differences in fatty acid composition, volatile profile, and sensory attributes of these Turkish virgin olive oils and those produced from well‐known varieties from major producing countries were compared. Thus, data on the main characteristics of extra virgin olive oils produced from Turkish olives were provided to the literature, these varieties were promoted internationally, and their cultivation was encouraged.

Predictions of heavy metal concentrations by physiochemical water quality parameters in coastal areas of Yangtze river estuary

Due to the degradation-resistant and strong toxicity, heavy metals pose a serious threat to the safety of water environment and aquatic ecology. Rapid acquisition and prediction of heavy metal concentrations are of paramount importance for water resource management and environmental preservation. In this study, heavy metal concentrations (Cr, Ni, Cu, Pb, Zn, Cd) and physicochemical parameters of water quality including Temperature (Temp), pH, Oxygen redox potential (ORP), Dissolved oxygen (DO), Electrical conductivity (EC), Electrical resistivity (RES), Total dissolved solids (TDS), Salinity (SAL), Cyanobacteria (BGA-PE), and turbidity (NTU) were measured at seven stations in the Yangtze river estuary. Principal Component Analysis (PCA) and Spearman correlation analysis were employed to analyze the main factors and sources of heavy metals. Results of PCA revealed that the main sources of Cr, Ni, Zn, and Cd were steel industry wastewater, domestic and industrial sewage, whereas shipping and vessel emissions were typically considered sources of Pb and Cu. Spearman correlation analysis identified Temp, pH, ORP, EC, RES, TDS, and SAL as the key physicochemical parameters of water quality, exhibiting the strongest correlation with heavy metal concentrations in sediment and water samples. Based on these results, multiple linear regression as well as non-linear models (SVM and RF) were constructed for predicting heavy metal concentrations. The results showed that the results of the nonlinear model were more suitable for predicting the concentrations of most heavy metals than the linear model, with average R values of the SVM test set and RF test set being 0.83 and 0.90. The RF model showed better applicability for simulating the concentration of heavy metals along the Yangtze river estuary. It was demonstrated that non-linear research methods provided efficient and accurate predictions of heavy metal concentrations in a simple and rapid manner, thereby offering decision-making support for watershed managers.