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We used biological and physical responses at 71 shallow waterbodies with contrasting nutrient levels undergoing recovery from eutrophication to predict potential changes in waterbird species abundance, an important component of lake ecosystems. These general predictions were tested using 28 years of breeding waterbird data from three Danish shallow eutrophic lakes, comparing species-specific responses to improved nutrient and water transparency in two lakes with a third where conditions remained constantly suitable for breeding waterbirds. We predicted positive responses to improved water quality from pursuit diving predators (three grebe species), a specialist zooplankton feeder (northern shoveler Anas clypeata) and waterbirds feeding on (common pochard Aythya ferina) or within (tufted duck A. fuligula) submerged macrophyte underwater canopies. These species were characterised by positive waterbird community composition changes (using Principal Components Analysis) associated with decreasing nutrient loading and increasing water transparency at two lakes, with no change in breeding waterbird community at the third. Secchi depth explained 73–95% of variance in both PC axes at both restored lakes, but not at the third, suggesting water transparency was the major factor driving waterbird community composition. These examples show predicting waterbird species-specific responses to management can usefully direct the use of breeding waterbirds as indicator species. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC), one of the most common and heterogeneous malignancies, is the second leading cause of cancer death worldwide and is closely related to dietary habits. Fatty acid is one of the main nutrients of human beings, which is closely related to diabetes, hypertension and other diseases. However, the correlation between fatty acid metabolism and the development and progression of GC remains largely unknown. Here, we profiled the genetic alterations of fatty acid anabolism-related genes (FARGs) in gastric cancer samples from the TCGA cohort and GEO database to evaluate the possible relationships and their internal regulatory mechanism. Through consistent clustering and functional enrichment analysis, three distinct fatty acid anabolism clusters and three gene subtypes were identified to participate in different biological pathways, and correlated with the characteristics of immune cell infiltration and clinical prognosis. Importantly, a distinctive FA-score was constructed through the principal component analysis to quantify the characteristics of fatty acid anabolism in each GC patient. Further analysis showed patients grouped in the high FA-score group were characterized with greater tumor mutational burden (TMB) and higher microsatellite stability (MSI-H), which may be more aeschynomenous to immunotherapy and had a favorable prognosis. Altogether, our bioinformatics analysis based on FARGs uncovered the potential roles of fatty acid metabolism in GC, and may provide newly prognostic information and novel approaches for promoting individualized immunotherapy in patients with GC. [ABSTRACT FROM AUTHOR]

Purpose: The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from. Methods: A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. Results: The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application. Conclusion: The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future. [ABSTRACT FROM AUTHOR]

Integration of multi-omic data for the purposes of biomarker discovery can provide novel and robust panels across multiple biological compartments. Appropriate analytical methods are key to ensuring accurate and meaningful outputs in the multi-omic setting. Here, we extensively profile the proteome and transcriptome of patient pancreatic cyst fluid (PCF) (n = 32) and serum (n = 68), before integrating matched omic and biofluid data, to identify biomarkers of pancreatic cancer risk. Differential expression analysis, feature reduction, multi-omic data integration, unsupervised hierarchical clustering, principal component analysis, spearman correlations and leave-one-out cross-validation were performed using RStudio and CombiROC software. An 11-feature multi-omic panel in PCF [PIGR, S100A8, REG1A, LGALS3, TCN1, LCN2, PRSS8, MUC6, SNORA66, miR-216a-5p, miR-216b-5p] generated an AUC = 0.806. A 13-feature multi-omic panel in serum [SHROOM3, IGHV3-72, IGJ, IGHA1, PPBP, APOD, SFN, IGHG1, miR-197-5p, miR-6741-5p, miR-3180, miR-3180-3p, miR-6782-5p] produced an AUC = 0.824. Integration of the strongest performing biomarkers generated a 10-feature cross-biofluid multi-omic panel [S100A8, LGALS3, SNORA66, miR-216b-5p, IGHV3-72, IGJ, IGHA1, PPBP, miR-3180, miR-3180-3p] with an AUC = 0.970. Multi-omic profiling provides an abundance of potential biomarkers. Integration of data from different omic compartments, and across biofluids, produced a biomarker panel that performs with high accuracy, showing promise for the risk stratification of patients with pancreatic cystic lesions. [ABSTRACT FROM AUTHOR]

Determining the factors that drive vegetation variation is complicated by the intricate interactions between climatic and anthropogenic influences. Neglecting the short-term time-lag and cumulative effects of climate on vegetation growth (i.e., temporal effects) exacerbates the uncertainty in attributing long-term vegetation dynamics. This study evaluated the climatic and anthropogenic influences on vegetation dynamics in China from 2000 to 2019 by analyzing normalized difference vegetation index (NDVI), temperature, precipitation, solar radiation, and ten anthropogenic indicators through linear regression, correlation, multiple linear regression (MLR), residual, and principal component analyses. Across most regions, growing season NDVI (G-NDVI) exhibited heightened sensitivity to climatic variables from earlier periods or from both earlier and current periods, signaling extensive temporal climatic effects. Constructing new time series for temperature, precipitation, and solar radiation from 2000 to 2019, based on the optimal vegetation response timing to each climatic variable, revealed significant correlations with G-NDVI across 27.9%, 26.7%, and 23.3% of the study area, respectively. Climate variability and anthropogenic activities contributed 45% and 55% to the G-NDVI increase in China, respectively. Afforestation significantly promoted vegetation greening, while agricultural development had a marginally positive influence. In contrast, urbanization negatively impacted vegetation, particularly in eastern China, where farmland conversion to constructed land has been prevalent over the past two decades. Neglecting temporal effects would significantly reduce the areas with robust MLR models linking G-NDVI to climatic variables, thereby increasing uncertainty in attributing vegetation changes. The findings highlight the necessity of integrating multiple anthropogenic factors and climatic temporal effects in evaluating vegetation dynamics and ecological restoration. [ABSTRACT FROM AUTHOR]

Surface residual stresses (Rs) have a significant impact on the performance of machined parts, including fatigue life and corrosion resistance. To enable online monitoring of Rs, many studies have focused on obtaining real-time Rs. However, direct measurement methods, including destructive and non-destructive techniques, will consume too much time or even damage the machined surface. Meanwhile, prediction methods rarely consider dynamic factors as identifying key features from dynamic data is challenging for humans. Therefore, this paper proposes an intelligent prediction method of Rs based on multi-source heterogeneous data, which contain cutting force, cutting temperature, power consumption, and cutting noise. Firstly, an Improved Convolutional Neural Network is established to identify features from the dynamic heterogeneous data. The mean training identification accuracy reaches 99.6%, which is significantly better than that (71%) obtained by the original convolutional neural network. Then, the Principal Component Analysis is built to automatically determine the key features, which benefit the subsequent Rs prediction. Finally, based on the key features, the Gaussian Process Regression is proposed to predict Rs in two directions. From the various experiments, the performance of the intelligent prediction method is validated, and the prediction accuracy rates for two directions reach 99.10% and 99.13%, respectively. Based on the proposed method, the real-time Rs can be predicted with the key features, which are automatically extracted from the multi-source heterogeneous data. This provides the basis for surface quality monitoring based on online data and greatly improves the level of intelligent manufacturing. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau (QTP), a high mountain area prone to destructive rainstorm hazards and inducing natural disasters, underscores the importance of developing precipitation intensity-duration-frequency (IDF) curves for estimating extreme precipitation characteristics. Here we introduce the Qinghai-Tibet Plateau Precipitation Intensity-Duration-Frequency Curves (QTPPIDFC) dataset, the first gridded dataset tailored for estimating extreme precipitation characteristics in QTP. The generalized extreme value distribution is chosen to fit the annual maximum precipitation samples at 203 weather stations, based on which the at-site IDF curves are estimated; then, principal component analysis is done to identify the southeast-northwest spatial pattern of at-site IDF curves, and its first principal component gives a 96% explained variance; finally, spatial interpolation is done to estimate gridded IDF curves by using the random forest model with geographical and climatic variables as predictors. The dataset provides precipitation information within 1, 2, 3, 6, 12, 24 hours and 5, 10, 20, 50,100 return years, with a 1/30° spatial resolution. The QTPPIDFC dataset can solidly serve for hydrometeorological-related risk management and hydraulic/hydrologic engineering design in QTP. [ABSTRACT FROM AUTHOR]

This research investigates the concentrations, sources, and health risks of polycyclic aromatic hydrocarbons (PAHs) and nitrated PAHs (NPAHs) in particulate matter with an aerodynamic diameter of 10 m or less (PM ) from critical urban centers in northern Mexico: Metropolitan Monterrey Area (MMA), Chihuahua (CHI), and Ciudad Juárez (CDJ). Advanced gas chromatography-mass spectrometry (GC-MS and GC-NCI-MS) revealed significant PAHs concentrations, with levels in MMA reaching 108.89 ± 99.90 ng/m , CHI at 100.69 ± 122.60 ng/m and CDJ at 73.26 ± 90.85 ng/m . Significantly, 3-nitrofluoranthene (3N-FLA) and 1-nitropyrene (1N-PYR), known for their potent toxicity, were among the most prominent NPAHs, with total concentrations in MMA, CHI, and CDJ at 470.32 pg/m , 247.26 pg/m , and 193.20 pg/m , respectively. Source apportionment using diagnostic ratios (DRs) and principal component analysis (PCA) indicated that biomass burning, vehicular emissions, and industrial activities were the primary sources of MMA. At the same time, CHI and CDJ were influenced more by industrial and diesel emissions. Health risk assessments based on benzo[a]pyrene equivalent (BaPeq) concentrations and excess cancer risk (ECR) demonstrated moderate to significant cancer risks, with CDJ exhibiting the highest NPAHs-related risk. This study makes several significant contributions: it presents the first analysis of PAHs and NPAHs levels in these urban areas, identifies key emission sources, and quantifies associated health risks, providing essential data for developing targeted public health policies and environmental regulations. [ABSTRACT FROM AUTHOR]

This study aims to explore the low phosphorus (P) tolerance of saplings from different Gleditsia sinensis Lam. families. It also seeks to screen for Gleditsia sinensis families with strong low P tolerance and identify key indicators for evaluating their tolerance. This research provides a foundation for the breeding of superior families of Gleditsia sinensis and the study of mechanisms underlying low P tolerance. Using saplings from 30 Gleditsia sinensis families as the research subjects, a sand culture pot experiment was conducted. This study set up low P treatment (0.01 mmol L−1) and normal P treatment (1 mmol L−1). Twenty-five indicators including growth morphology, biomass, root morphology, and P content were measured. The low P tolerance coefficient was used as the basic data for assessing the low P tolerance of Gleditsia sinensis. The fuzzy comprehensive evaluation method was employed to comprehensively assess the low P tolerance types of Gleditsia sinensis a stepwise regression model was established to identify the key evaluation indicators for low P tolerance. The results indicate that low P stress reduced plant height, stem diameter, and biomass in most Gleditsia sinensis families, but increased the root morphological indicators, root-shoot ratio and PUE of various organs. Principal component analysis transformed the 25 indicators into 6 independent comprehensive indicators, with a cumulative contribution rate of 86.743%. The fuzzy comprehensive evaluation method calculated a comprehensive evaluation value (D value), enabling the screening of Gleditsia sinensis families into low P tolerant and low P sensitive types. Cluster analysis grouped the 30 Gleditsia sinensis families into 4 types. Among them, F13, F10, F9, F18, F15, and F28 were classified as low P tolerant types; F6, F23, F3, F17, F20, F2, F12, F11, F16, F8, F5, F27, F1, and F26 were categorized as intermediate types; F30, F7, F22, F4, F19, F29, F24, F14 and F25 were considered low P sensitive types, and F21 was classified as extremely low P sensitive types. The stepwise regression analysis identified the indicators stem diameter, total root volume, shoot dry weight, total root projection area, and leaf P content as the key factors for discriminating the low P tolerance of Gleditsia sinensis. The regression model is as follows: D=-0.005 + 0.323 stem diameter *+0.154 * total root volume + 0.196* shoot dry weight + 0.139* total root projection area − 0.112* leaf P content. In summary, low P stress inhibited the growth of Gleditsia sinensis saplings, but it increased the root morphological indicators, root-shoot ratio and PUE of various organs to cope with low P environments. The screening identified F13, F10, F9, F18, F15, and F28 as low P tolerant Gleditsia sinensis families. The evaluation indicators for low P tolerance in Gleditsia sinensis were identified as stem diameter, total root volume, shoot dry weight, total root projection area and leaf P content. [ABSTRACT FROM AUTHOR]

While a broad consensus about the first successful migration modern humans out of Africa seems established, the peopling of Arabia remains somewhat enigmatic. Identifying the ancestral populations that contributed to the gene pool of the current populations inhabiting Arabia and the impact of their contributions remains a challenging task. We investigate the genetic makeup of the current Yemeni population using 46 whole genomes and 169 genotype arrays derived from Yemeni individuals from all geographic regions across Yemen and 351 genotype arrays derived from neighboring populations providing regional context. Principal Component Analysis shows stratification between Yemen districts but also with respect to nearby populations: Yemeni, other Arabian and Bedouin samples form a continuum towards the populations of the Levant, whereas East Africa and India appear strongly differentiated. This finding is further supported by higher Principal Components, admixture and haplogroup analyses, and F-statistics. Moreover, two-reference linkage disequilibrium decay estimates are most significant for Yemeni admixture from an ancient northern influx (up to 5220BP from Palestine) and East Africa (750BP). We show that the initial gene flow into the Yemeni populations of today came from the rest of Arabia and the Levant, and a less substantial and more recent genetic impact into coastal Yemen from East Africa, particularly. [ABSTRACT FROM AUTHOR]

Using Fourier Transform Infrared spectroscopy (FTIR), it is possible to show chemical composition of materials and / or profile chemical changes occurring in tissues, cells, and body fluids during onset and progression of diseases. For diagnostic application, the use of blood would be the most appropriate in biospectroscopy studies since, (i) it is easily accessible and, (ii) enables frequent analyses of biochemical changes occurring in pathological states. At present, different studies have investigated potential of serum, plasma and sputum being alternative biofluids for lung cancer detection using FTIR. However, until now, it has not been shown which biofluid; among serum and plasma, that can serve as the best material medium for detecting lung cancer with highest levels of accuracy. In this study, plasma and serum isolated from blood consenting participants without lung cancer symptoms (controls) and lung cancer patients. The samples were measured using FTIR and subsequently analyzed by machine learning (ML) algorithms in order to show which fluids (serum or plasma) would better enhance detection of lung cancer. Higher absorbances values of PO2−, CH2, CH3 and amides vibrations in FTIR spectra of both serum and plasma samples, collected from lung cancer patients were observed in comparison to individuals without lung cancer symptoms (controls). Principal component analysis (PCA) of FTIR spectra showed plasma and serum samples collected from lung cancer patients and individuals without lung cancer symptoms were better differentiated in fingerprinting region (from 800 to 1800 cm− 1) when compared to lipid region (2800–3000 cm− 1). Moreover, also sensitivity specificity and accuracy calculated by logistic regression (LR) and receive operating characteristic (ROC) showed higher values for fingerprint range (800–1800 cm− 1) in comparison with lipids (2800–3000 cm− 1) one for both, serum and plasma. However, using these methods differences between serum and plasma were not existed. From the all obtained results, it was visible, that both fluids could be used in detected lung cancer using FTIR. Moreover, it was also showed that fingerprint range gave a better distinction between the studied patient groups than the lipid range. This was noticeable for both serum and plasma. [ABSTRACT FROM AUTHOR]

Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest. The networks associated with these patterns have been extensively analyzed. However, the overall dynamic organization and how it relates to consciousness remains unclear. We hypothesized that deep learning networks would help to model this relationship. Recent studies have used low-dimensional variational autoencoders (VAE) to learn meaningful representations that can help explaining consciousness. Here, we investigated the complexity of selecting such a generative model to study brain dynamics, and extended the available methods for latent space characterization and modeling. Therefore, our contributions are threefold. First, compared with probabilistic principal component analysis and sparse VAE, we showed that the selected low-dimensional VAE exhibits balanced performance in reconstructing dFCs and classifying brain patterns. We then explored the organization of the obtained low-dimensional dFC latent representations. We showed how these representations stratify the dynamic organization of the brain patterns as well as the experimental conditions. Finally, we proposed to delve into the proposed brain computational model. We first applied a receptive field analysis to identify preferred directions in the latent space to move from one brain pattern to another. Then, an ablation study was achieved where we virtually inactivated specific brain areas. We demonstrated the model's efficiency in summarizing consciousness-specific information encoded in key inter-areal connections, as described in the global neuronal workspace theory of consciousness. The proposed framework advocates the possibility of developing an interpretable computational brain model of interest for disorders of consciousness, paving the way for a dynamic diagnostic support tool. [ABSTRACT FROM AUTHOR]

The geochemical and chemical constituents of river water quality could be influenced by human activities and organic processes like water interacting with the lithogenic structure that the river flows through. Evaluating evidence based primary root of the predominant pollutant ions, their interactions as well as the factors controlling their dominance is crucial in studies regarding water environment and hydrology especially as most studies focus on theoretical methods. In order to understand the water cycle, safeguard surface water resources, and preserve the human environment, this study evaluated surface water hydro-chemical facies, quality dynamics, and portability in southern Nigeria using multivariate statistical approaches by analyzing selected hydro-chemical characteristics as indicators of pollution along the river during wet and dry seasons. Twenty water samples were taken, analyzed, and subjected to mathematical statistics: Gibbs plot, trilinear piper analysis, stiff pattern analysis, ionic scatter analysis, correlation, and principal component analysis. Result of surface water recorded mean pH ranges from 4.8 for wet season and 5.3 for dry season, above the WHO, and during dry season TDS, Mg2+, Pb, and Cd were above the WHO limits, respectively. Abundance of cation and anion in surface water was in a decreasing trend of: HCO3 > Ca2+ > Mg2+ > Cl− > Na+ > SO42− > K + > NO3−. Trilinear plot, stiff pattern, and Gibbs ratio indicated hydrochemical facie of water dominated by calcium bicarbonate (Ca-HCO3) water type. From plots and ionic ratio, the major hydrochemical process of water chemistry during wet and dry seasons was rock-water interaction arising majorly from weathering processes. Ionic ratios of Ca2+ and Mg2+1, Ca2+ and HCO2− [1:2], Ca2+ + Mg2+ and HCO3− + SO42− [1:1], revealed dissolution of dolomite as their common origin, with total cations in wet and dry seasons ranging between 43 and 57% and total anions: 37.3–62.7, with dry season dominance. The overall WQI of the river seemed good quality due to rapid flow and self-purification of the river but may be harmful in the future. It was recommended that constant surveillance and monitoring of human activities along waterways be enforced in order to ensure that undesirable pollution levels don't occur in the river. [ABSTRACT FROM AUTHOR]

The height of the water-conducting fracture zones (WCFZ) is crucial for ensuring safe coal mining beneath aquifers, particularly considering the secondary development of the WCFZ in upper seams due to repeated mining in close distance coal seams. Accurately predicting this height is essential for mine safety, groundwater protection, and optimal coal resource use. This study compiles extensive measured data from various mining areas in China to analyze the coupling relationship between the WCFZ development height and six influencing factors: mining thickness, mining depth, coal seam spacing, hard rock lithology ratio, and the slope length of working face. Using principal component analysis and fuzzy comprehensive evaluation, we derived the combined weights of these factors. We developed a prediction model based on multiple regression analysis that incorporates the weighted influences of these factors, further refined into a multivariate nonlinear regression model for greater accuracy. Compared against a multivariate linear regression model, empirical formulas, and measured results, our model demonstrated higher accuracy, stability in both absolute and relative errors, and practical applicability. It was successfully applied and validated on the 100,501 working face of Nanyaotou Coal Mine in Shanxi Province, offering a new scientific approach for predicting the WCFZ. [ABSTRACT FROM AUTHOR]

Lycium barbarum is an important economic crop in the arid region of Northwest China, and the regulation of irrigation and fertilisation is an important way to improve the quality and yield of Lycium barbarum. To explore the effects of water-fertiliser coupling on photosynthesis, quality and yield of Lycium barbarum under irrigation methods based on predicted crop evapotranspiration (ETc), ETc was calculated via reference evapotranspiration (ETo) predicted on the basis of public weather forecasts, and the irrigation water volume was determined as a proportion of this ETc. A field experiment was conducted via a completely randomised experimental design with five irrigation water volumes (W0 (100% ETc), W1 (90% ETc), W2 (80% ETc), W3 (70% ETc) and W4 (65% ETc)) and three fertiliser application rates (high fertiliser (FH), medium fertiliser (FM) and low fertiliser (FL)). The results revealed that the chlorophyll content, Gsw, Ci of Lycium barbarum leaves and 100-grain weight and yield of fresh fruit of Lycium barbarum increased with increasing irrigation, and the protein content, fat content, total sugar content and polysaccharide content in the dried fruits of Lycium barbarum first increased and then decreased with increasing irrigation under the same level of fertilisation, and the maximum value of these indexes reached 70% ETc − 100% ETc. At the same irrigation level, E, A, Gsw, Ci of Lycium barbarum leaves and protein content, fat content, total sugar content and polysaccharide content in the dried fruits of Lycium barbarum increased and then decreased with increasing of fertiliser application volume, and these indexes reached the maximum value at the fertiliser application rate of FM. A comprehensive evaluation based on principal component analysis (PCA) revealed that the optimum treatment in both years was W0FM (irrigation level of 100% ETc, corresponding to irrigation water in the range of 254.2–309.4 mm, and fertiliser application of N-P2O5-K2O of 315-82-135 kg ha−1), which was significantly greater in 2021 (2022) than in the CK. E, A, Gsw and Ci of daily changes of Lycium barbarum leaves in the W0FM treatment in 2021 (2022) increased by 46.54% (31.53%), 7.08% (59.26%), 18.55% (10.74%) and 34.58% (29.81%), respectively. In 2021 (2022), W0FM treatment increased fat content, polysaccharide content, and betaine content of dried fruits of Lycium barbarum and the 100-grain weight and yield of fresh fruit by 2.88% (10.11%), 1.56% (10.02%), 8.37% (21.69%), 13.57% (24.81%) and 31.39% (71.50%), respectively. The results of this study may provide a theoretical basis for improving the quality and efficiency of Lycium barbarum in the field in the arid zone of Northwest China. [ABSTRACT FROM AUTHOR]

Double negative T (DNT) cells are a unique subset of CD3 + TCRαβ + T lymphocytes that lack CD4, CD8, or NK1.1 expression and constitute 3–5% of the total T cell population in C57BL/6 mice. They have increasingly gained recognition for their novel roles in the immune system, especially under autoimmune conditions. Conventional machine learning approaches such as principal component analysis have been employed in single-cell RNA sequencing (scRNA-seq) analysis to characterize DNT cells. However, advanced deep learning models such as Single Cell Variational Inference (scVI) have the capability to capture nonlinear gene expression patterns in the sequencing data. In this study, employing the deep learning methodology, we have revealed novel markers for splenic DNT cells in C57BL/6 mice which were validated with flow cytometry analysis. We classified DNT cells into two subgroups, naïve DNT (nDNT) cells differentiated by the expression of Ly6C and activated DNT (aDNT) cells differentiated by the expression of MHC-II. A prior study had predicted elevated expression of CD137/4-1BB encoded by Tnfrsf9 in nDNT cells; however, our analysis predicted and validated that CD137 was a marker for aDNT cells instead of nDNT cells. Innovatively, our data also identified CD30 encoded by Tnfrsf8 and CD153/CD30L encoded by Tnfsf8 as additional markers for aDNT cells. In addition, we classified three subgroups in nDNT cells and two subgroups in aDNT cells. Our scVI analysis suggested, and flow cytometry analysis confirmed, that Ly49G2 encoded by Slamf7 was a marker for the nDNT0 subgroup. Importantly, we validated that MHC-II was indeed expressed by a subset of human DNT cells suggesting the presence of a human aDNT population. Furthermore, we found increased expression of CD30, CD153, and CD137 on aDNT cells in MRL/lpr mice compared to those in C57BL/6 mice suggesting potential pathogenic roles of these molecules in autoimmunity. Together, our comprehensive analysis has uncovered and validated novel markers for different subpopulations of DNT cells that can be used in the phenotypic and/or functional characterization of these relatively rare cells in health and disease. [ABSTRACT FROM AUTHOR]

Multicellular spheroids embedded in 3D hydrogels are prominent in vitro models for 3D cell invasion. Yet, quantification methods for spheroid cell invasion that are high‐throughput, objective and accessible are still lacking. Variations in spheroid sizes and the shapes of the cells within render it difficult to objectively assess invasion extent. The goal of this work is to develop a high-throughput quantification method of cell invasion into 3D matrices that minimizes sensitivity to initial spheroid size and cell spreading and provides precise integrative directionally-dependent metrics of invasion. By analyzing images of fluorescent cell nuclei, invasion metrics are automatically calculated at the pixel level. The initial spheroid boundary is segmented and automated calculations of the nuclear pixel distances from the initial boundary are used to compute common invasion metrics (i.e., the change in invasion area, mean distance) for the same spheroid at a later timepoint. We also introduce the area moment of inertia as an integrative metric of cell invasion that considers the invasion area as well as the pixel distances from the initial spheroid boundary. Further, we show that principal component analysis can be used to quantify the directional influence of a stimuli to invasion (e.g., due to a chemotactic gradient or contact guidance). To demonstrate the power of the analysis for cell types with different invasive potentials and the utility of this method for a variety of biological applications, the method is used to analyze the invasiveness of five different cell types. In all, implementation of this high‐throughput quantification method results in consistent and objective analysis of 3D multicellular spheroid invasion. We provide the analysis code in both MATLAB and Python languages as well as a GUI for ease of use for researchers with a range of computer programming skills and for applications in a variety of biological research areas such as wound healing and cancer metastasis. [ABSTRACT FROM AUTHOR]

Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability. The proposed method achieved remarkable accuracy rates of 93.22% and 95.20% on the UCI and Kaggle datasets, respectively, underscoring its exceptional performance even with small sample sizes. Ablation experiments further validated the efficacy of the data preprocessing and feature selection techniques employed. Notably, the EPSOSA algorithm surpassed classical optimization algorithms in terms of convergence speed, while also demonstrating improved sensitivity and specificity. This model holds significant potential for facilitating early identification of high-risk patients, which could ultimately save lives and optimize the utilization of medical resources. Despite implementation challenges, including technical integration and data standardization, the algorithm shows promise for use in emergency settings and community health services for regular cardiac risk monitoring. [ABSTRACT FROM AUTHOR]

Salinity and lead are two important abiotic stresses that limit crop growth and yield. In this study, we assayed the effect of these stresses on tolerant and sensitive maize genotypes. Four-week-old maize plants were treated with 250 mM sodium chloride (NaCl) and 250 µM lead (Pb). Our results show that NaCl or Pb treatment of the sensitive genotype caused a significant reduction in the root length, plant height, total fresh and dry weights, as well as chlorophyll, and carotenoid content. Salt stress led to a significant decrease in the relative water content, shoot and root length, fresh and dry weight as well as leaf area and K+ content but increase Na+ content. Both NaCl and Pb stresses increased the antioxidant enzyme activity, proline content, malondialdehyde, and hydrogen peroxide levels. Principal component analysis (PCA) accounted for 69.8% and 16.5% of the total variation among all the variables studied. PCA also suggested a positive correlation between hydrogen peroxide, malondialdehyde, peroxidase, catalase, ascorbate peroxidase levels, and Na+ content and a negative correlation between K+ content, chlorophyll content, relative water content, leaf area, root length, plant height, and total fresh, and dry weights. Together, these results suggest that the salt-tolerant maize genotype is more suitable for adapting to Pb stress compared to the salt-sensitive genotype. [ABSTRACT FROM AUTHOR]

Dried blood spot (DBS) sampling offers significant advantages over conventional blood collection methods, such as reduced sample volume, minimal invasiveness, suitability for home-based sampling, and ease of transport. However, understanding the effects of variable storage temperatures and times on metabolite stability is crucial due to varying intervals and delivery conditions between sample collection and metabolomics analysis. To minimize biological variances, all samples were collected from the same individual simultaneously and stored at three different temperatures (4 °C, 25 °C, and 40 °C) for diverse time points (3, 7, 14, and 21 days). Metabolic profiling was conducted an untargeted gas chromatography-mass spectrometry (GC-MS) and ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS)-based multi-platform metabolomics. Principal component analysis (PCA) showed alterations in metabolite stability at different temperatures, with phosphatidylcholines (PCs) and triglycerides (TAGs) as the first principal component (PC1). Specifically, we identified 69 metabolites that remained stable across all three temperatures over the 21-day period, while 78 metabolites exhibited instability. Furthermore, linear correlations between metabolite intensity and storage time were observed. Overall, our study elucidated the influence of storage temperature and time on specific metabolite stability in DBS samples, providing valuable insights for study design, biomarker selection, and data improvement. [ABSTRACT FROM AUTHOR]

Vaginal pessaries have been used for millennia to alleviate symptoms of pelvic organ prolapse (POP). Despite their long-standing use, the success rate of pessary treatment is approximately 60%, and the underlying mechanisms of support are not well understood. This study aims to investigate three previously proposed hypotheses regarding the support mechanisms of pessaries, utilizing supine and upright magnetic resonance imaging (MRI): (1) support by bony structures, (2) support by levator ani muscles (LAM), and (3) the uterus keeping the pessary in place by acting as a lever. Thirty POP patients, with a successful pessary fit for more than 3 months, underwent MRI scans in both upright and supine position. The position of the pelvic bony structures, LAM, uterus, and pessary were analysed by placing corresponding points on these structures and comparing the differences between supine and upright. Also, the angle of the pessary with the horizontal plane of the pelvic inclination corrections system was analysed. Principal component analysis was applied to evaluate how the positions of the LAM and the pessary changed between the two positions. The lowest point of the pessary descends below the lowest point of the bony structures in upright position, disproving the first hypothesis. The pessary significantly descends towards the LAM from supine to upright, but does not drop below it, strengthening the second hypothesis. The pessary angle is below 90° in upright position, the uterus is positioned in the pessary centre and remains constant from supine to upright, strengthening the third hypothesis. [ABSTRACT FROM AUTHOR]

Characterization of serological responses to Plasmodium falciparum (Pf) is of interest to understand disease burden and transmission dynamics; however, their interpretation is challenging. Dried blood spots from 30,815 participants aged 6 months to 15 years from the 2018 Nigeria HIV/AIDS Indicator and Impact Survey were analyzed by multiplex bead-based assay to measure immunoglobulin G (IgG) to Pf-stage-specific MSP-1, AMA-1, GLURPR0, LSA-1, and CSP. These IgG levels were analyzed by principal component analysis (PCA). PC1 and PC2 scores explained 41% and 17% of the total variance, respectively. PC1 unit vectors represented seropositivity. PC2 unit vectors for blood-stage antigens were in opposite directions to liver-stage and sporozoite antigens. PC2 scores were correlated with MSP-1 positively (R = 0.52, P < 0.001) and CSP negatively (R=-0.65, P < 0.001) and may help identify areas with prior exposure but higher risk for increased infections or epidemics. PCA of Pf serology can provide summary scores to possibly inform future programmatic interventions. [ABSTRACT FROM AUTHOR]

Low-chlorinated polychlorinated biphenyls (LC-PCBs) pose a significant environmental threat to river systems due to their higher volatility, mobility, and solubility compared to higher chlorinated PCBs (HC-PCBs). This study aimed to detect and quantify mono-, di-, and tri-chlorinated PCB congeners in the Ganga River, focusing on sections in Kanpur, an industrial hub, and Prayagraj, known for religious activities. We developed and optimized a liquid–liquid microextraction method using hexane as a solvent. The extraction parameters were optimized using Central Composite Design (CCD), confirming a significant quadratic model fit (p < 0.0001) with a R2 of 0.9262, achieving optimal extraction efficiency. The method demonstrated limits of detection (LOD) ranging from 0.04 to 2.1 µg L−1, limits of quantification (LOQ) from 0.1 to 6.5 µg L−1, and recovery rates between 73.3 and 106.7%. The expanded uncertainty (%) ranged between 2.4 to 4.0%. Analysis of water samples from Kanpur (K1–K11) and Prayagraj (P1–P11) revealed significantly higher LC-PCB contamination in Kanpur. Principal component analysis (PCA) indicated that industrial activities were the primary contributors to the observed pollution, with the first principal component explaining 49% of the variance. Partial least squares discriminant analysis identified PCB-2, PCB-4, PCB-5, and PCB-18 as significant contaminants. Spatial distribution mapping highlighted K10 (Kanpur) and P1 (Prayagraj) as critical hotspots, suggesting localized point-source contamination. This study provides a robust analytical framework for the detection and quantification of LC-PCBs in aquatic environments. The findings underscore the impact of industrialization on LC-PCB pollution in the Ganga River and highlight the need for targeted environmental interventions to mitigate contamination and protect ecosystem and public health. Article highlights: Optimization of a microextraction method using CCD to improve efficiency and measure LC-PCB concentration in Ganga River water samples. PCA was performed to understand the LC-PCB sources in Kanpur and Prayagraj, and the VIP plot identified key congeners as PCB no. 2, 4, 5, 18. Spatial mapping visualizes PCB pollution hotspots primarily associated with the industry and downstream accumulation. [ABSTRACT FROM AUTHOR]

Lycium barbarum is an important economic crop in the arid region of Northwest China, and the regulation of irrigation and fertilisation is an important way to improve the quality and yield of Lycium barbarum. To explore the effects of water-fertiliser coupling on photosynthesis, quality and yield of Lycium barbarum under irrigation methods based on predicted crop evapotranspiration (ETc), ETc was calculated via reference evapotranspiration (ETo) predicted on the basis of public weather forecasts, and the irrigation water volume was determined as a proportion of this ETc. A field experiment was conducted via a completely randomised experimental design with five irrigation water volumes (W0 (100% ETc), W1 (90% ETc), W2 (80% ETc), W3 (70% ETc) and W4 (65% ETc)) and three fertiliser application rates (high fertiliser (FH), medium fertiliser (FM) and low fertiliser (FL)). The results revealed that the chlorophyll content, Gsw, Ci of Lycium barbarum leaves and 100-grain weight and yield of fresh fruit of Lycium barbarum increased with increasing irrigation, and the protein content, fat content, total sugar content and polysaccharide content in the dried fruits of Lycium barbarum first increased and then decreased with increasing irrigation under the same level of fertilisation, and the maximum value of these indexes reached 70% ETc − 100% ETc. At the same irrigation level, E, A, Gsw, Ci of Lycium barbarum leaves and protein content, fat content, total sugar content and polysaccharide content in the dried fruits of Lycium barbarum increased and then decreased with increasing of fertiliser application volume, and these indexes reached the maximum value at the fertiliser application rate of FM. A comprehensive evaluation based on principal component analysis (PCA) revealed that the optimum treatment in both years was W0FM (irrigation level of 100% ETc, corresponding to irrigation water in the range of 254.2–309.4 mm, and fertiliser application of N-P2O5-K2O of 315-82-135 kg ha−1), which was significantly greater in 2021 (2022) than in the CK. E, A, Gsw and Ci of daily changes of Lycium barbarum leaves in the W0FM treatment in 2021 (2022) increased by 46.54% (31.53%), 7.08% (59.26%), 18.55% (10.74%) and 34.58% (29.81%), respectively. In 2021 (2022), W0FM treatment increased fat content, polysaccharide content, and betaine content of dried fruits of Lycium barbarum and the 100-grain weight and yield of fresh fruit by 2.88% (10.11%), 1.56% (10.02%), 8.37% (21.69%), 13.57% (24.81%) and 31.39% (71.50%), respectively. The results of this study may provide a theoretical basis for improving the quality and efficiency of Lycium barbarum in the field in the arid zone of Northwest China. [ABSTRACT FROM AUTHOR]

Although food security is multidimensional, most previous studies have measured households' vulnerability to food insecurity via unidimensional indices. To address this gap, this study assesses households' vulnerability to multidimensional food insecurity (VMFI) by employing the vulnerability-as-expected poverty technique for 422 randomly selected wheat-producing households from three districts of the Arsi Zone. Before households' VMFI was estimated by the three-stage feasible generalized least squares approach (FGLS), the multidimensional food security (MFS) index was developed by using principal component analysis (PCA). The MFS index was then calculated to be 0.4825 by the geometric aggregation technique to summarize the PCA results, and the descriptive summary indicated that 45.02% of the respondents were food secure. The three-stage FGLS revealed that 57.11% of the respondents were vulnerable to multidimensional food insecurity. Moreover, 33.41% of the respondents had stable food security, 45.5% were chronically food insecure, 9.48% experienced transitory food insecurity, and 11.61% were vulnerable to multidimensional food insecurity. The results of the probit model, which is employed to identify the determinants of VMFI, confirm that contact with agricultural extension agents, family size, and participation in off/nonfarm activities reduce VMFI, whereas the dependency ratio, credit utilization, shock experiences, and living in lowland areas increase it. Accordingly, it is important to adopt policies and strategies that enhance household interaction with agricultural extension agents and develop an efficient early warning mechanism to build a resilient food security system. To reduce household VMFI, it is also crucial to promote financial education and provide special support to drought-prone areas by developing drought-tolerant agricultural technologies and reducing agricultural rain dependency. [ABSTRACT FROM AUTHOR]

This study attempted to determine how political engagement, accreditation, and female autonomy controlled intimate partner violence (IPV) in the COVID-19 scenario. The empirical investigation used published statistics on IPV for 27 Indian states and two union territories. The investigation used methods such as principal component analysis, the technical inefficiency effects model, and nonparametric analysis of variance (ANOVA). The results showed that Odisha, West Bengal, Uttarakhand, and Chhattisgarh were the states most efficient in regulating IPV. The Judiciary and Public Safety Score, female autonomy, and political involvement and accreditation, as evaluated based on female Members of Parliament (MPs) and Members of the Legislative Assembly (MLAs), are the most important factors in minimizing state-managed inefficiency. The study concludes with pertinent conclusions, some policy recommendations, and a focus on the trajectory of future research. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) and breeding value analysis (BV) were used to discover genotypes with better features for future breeding efforts. This research included a thorough analysis of the 300 exceptional rice germplasms provided by the Bangladesh Rice Research In (BRRI) and the International Rice Research Institute (IRRI). In the PCA scheme, the dendrogram generated using the UPGMA method identified Cluster II as the most favorable. This cluster comprised 125 genotypes distinguished by remarkable plant height, growth, and grain quality. Properties such as amylose content, chalkiness, and zinc concentration, which have a notable influence on crop productivity, were also taken into consideration. A biplot analysis, specifically examining principal components 1 and 2, revealed a favorable correlation between certain genotypes located in the top right quadrant and important agronomic factors like plant height, flowering, maturity dates, yield, and grain width. Genotypes 11, 31, 36, 48, 68, 78, 119, 126, 133, 195, 218, 220, 235, 241, 265, 266, 284, and 295 had favorable traits that suggested the possibility of higher rice production. The BV analysis revealed six genotypes 86, 142, 143, 239, 282, and 295 that showed promise for future breeding endeavors. Among the six, genotype 295 (IRRI 154) showed remarkable features. These studies provided a substantial contribution to the progress of rice breeding by opening up possibilities for developing rice varieties with improved yield and quality. [ABSTRACT FROM AUTHOR]

Healthcare supply chain management's (HSCM) significance to economic and societal development is huge. In today's very competitive market, supply chains have seen significant changes in the last several years. There is a need for technology that can handle the increasing complexity of today's dynamic supply chain activities. Both machine learning (ML) and the quick dissemination of information have the potential to revolutionize the supply chain. ML has spawned a slew of useful supply chain applications in recent years, HSCM has received comparatively less attention. In this study, we applied three ML algorithms such as gradient boosting (GB), histogram gradient boosting (HGB), and cat boosting (CB) with data preprocessing tools to predict whether the medicines are delivered on time or not in the HSCM. The data preprocessing tools are used to manage datasets and increase the performance of ML algorithms. There are three methods of feature selection that are applied in this study such as Pearson correlation, chi-square test, and principal component analysis to select the best features to push in the ML algorithms. The main results show the CB is the best algorithm with the highest accuracy, precision, recall, and f1 score with values respectively. The three ML algorithms are compared with other ML algorithms to show the robustness of the applied ML algorithms. We made a sensitivity analysis to show the chaining in learning rate (LR) and compute the accuracy of the ML algorithms. We show the CB is not sensitive to values between 0.1 and 1. [ABSTRACT FROM AUTHOR]

A method is proposed for the nondestructive control of azithromycin and clarithromycin active substances in tableted pharmaceuticals without opening blister packs by measuring the intensity of the diffuse reflection of IR radiation. Two methods are used: near-IR Fourier-transform spectrometry and colorimetry using a smartphone and a 3D-printed device. The data array (IR diffuse-reflection spectra and digital values of colorimetric channels) were processed by principal component analysis, hierarchical cluster analysis, and partial least squares regression using the TQ Analyst, PhotoMetrix PRO® software. The use of chemometric algorithms for the determination of the concentration of active substances and the identification of the producer of the studied pharmaceuticals was considered. Methods of IR spectrometry and colorimetry have shown equally precise results in the identification of the producer of pharmaceuticals and the determination of the concentration of active substances in tablets without opening blister packs. [ABSTRACT FROM AUTHOR]

Single-sample face and ear recognition (SSFER) is a challenging sub-problem in biometric recognition that refers to the difficulty in feature extraction and classification when only a single-face or ear training image is available. SSFER becomes much more challenging when images contain a variety of lighting, positions, occlusions, expressions, etc. Virtual sample generation methods in SSFER have gained popularity among researchers due to their simplicity in the augmentation of training sets and improved feature extraction. In this article, we propose a novel and simple method for the generation of virtual samples for training the classifiers to be used in SSFER. The proposed method is based on 2D local patches, and six training samples are generated for a single face or ear image. Further, training is performed using one of the variations along with its generated virtual samples, while during testing, all the variations were considered except the one used during training. Features are extracted using principal component analysis, and classification is performed using the nearest-neighbour classifier. Extensive experiments were performed for the image quality of the virtual samples, classification accuracy, and testing time on ORL, Yale, and AR (illumination) face databases, and AMI and IITD ear databases which are publicly available. The results are also compared with other state-of-the-art methods, with classification accuracy and universal image quality being the major outcomes. The proposed method improves the classification accuracy by 14.50%, 1.11%, 0.09%, 21.60%, and 10.00% on AR (illumination), Yale, ORL, IITD, and AMI databases, respectively. The proposed method showed an improvement in universal image quality by 15%, 20%, 14%, 30%, and 15% on AR (illumination), Yale, ORL, IITD, and AMI databases, respectively. Experimental results prove the effectiveness of the proposed method in generating virtual samples for SSFER. [ABSTRACT FROM AUTHOR]

Meteorological and edaphic factors significantly affect the first flush metabolite composition of Darjeeling tea, impacting flavor, nutrition, and customer preferences. The Camellia sinensis var. sinensis grown in the Darjeeling hills was sampled at different stages of processing. 75 metabolites were identified using a GC/MS-based metabolomic and chemometric technique. The study aimed to pinpoint quality markers and create a database for Darjeeling tea authenticity, India's first geographical indication product. Principal component analysis and partial least squares discriminant analysis revealed changes in tea metabolites during processing, while the chemometric study demonstrated variations in key compounds. Antioxidant activity exhibited significant variations among the samples with final processed showing highest antioxidant potential. The concentration of amino acids increased significantly (p < 0.05) from fresh tea leaves to the final processed product. Conversely, the content of organic acids, inorganic acids, and fatty acids was observed to decrease. Additionally, caffeic acids, epicatechin, epigallocatechin, chlorogenic acid, 3, 4-dihydroxybenzoic acid, and 4-hydroxycinnamic acid increased in concentration, while catechin, (-)-epicatechin, gallic acid, pyrogallol, quinic acid, shikimic acid, and arbutin decreased significantly in concentration in the final processed tea. Meteorological parameters were documented during the study period, showing variation in the levels of PM2.5, PM10, O3, NO2, and SO2. The findings of this study offer insightful information on the variables influencing Darjeeling tea metabolite composition, which tea producers and processors can use to raise the standard and authenticity of this beverage. [ABSTRACT FROM AUTHOR]

Purpose: Controversies exist regarding the prevailing spectrum of microorganisms in microbial ureteral stent colonization (MUSC) and their clinical significance. The aim of this comprehensive review is to determine the predominant microbial spectrum in patients with an indwelling ureteral stent in comparison to catheter-associated urinary tract infections (CAUTI) and uncomplicated urinary tract infections (UTI). Methods: Google scholar, PubMed, Embase, Medline, and Cochrane literature databases were searched from inception to April 2022 to identify manuscripts on MUSC, uncomplicated UTI and CAUTI. A principal component analysis (PCA) was performed to identify patterns of the pathogen spectrum of the different groups. Results: We included 29 studies on MUSC, 28 studies on uncomplicated UTI and 23 CAUTI studies. The proportion of Staphylococci, Enterococci and Candida were significantly higher in MUSC and stent associated bacteriuria compared to their proportion in uncomplicated UTIs where E. coli dominates. By comparing MUSC, CAUTI and UTI with a PCA, the detected pathogen spectrum exhibited clearly distinguishable trends in the frequency of the main isolated pathogens influencing these three groups of urinary tract infections. With respect to MUSC and UTI, their 95% confidence interval ellipse only showed minimal overlap emphasizing that the spectrum of pathogens in the two groups is clearly distinct. Conclusions: The frequency of detection of Staphylococci, Enterococci and Candida is more common in MUSC as compared to UTI. Thus, patients with indwelling ureteral stents should undergo an antimicrobial prophylaxis targeting this microbial spectrum in case of further surgery. [ABSTRACT FROM AUTHOR]

The appearance of plasma generated during femtosecond laser machining depends strongly on the features present on the sample before machining occurs. However, the complexity of femtosecond light-matter interaction means that development of a theoretical understanding of plasma generation is challenging. In this work, principal component analysis is applied to experimental images of plasma generated during femtosecond laser machining of silicon to calculate the orthogonal spatial patterns of the plasma variance (plasma modes), and to identify which sample variance (sample modes) are associated with these plasma modes. The results demonstrate the potential of principal component analysis for data-driven scientific discovery in the field of femtosecond light-matter interactions. [ABSTRACT FROM AUTHOR]

The insect biorefinery (IB) is central to recycling nutrients from food losses and waste (FLW) in an emerging circular bioeconomy approach. The sustainable leadership of the European insect industry and its integration into the bioeconomy depend on implementing symbiotic production models through site selection and partnerships. Only 8 studies, including 3 in the EU, have examined stakeholder acceptance. This pilot study is the first to focus on potential upstream symbiosis partners (USPs) in the EU, examining the factors that influence the willingness of potential USPs in suitable Italian locations to adopt IB. Preliminary analyses identified a final sample of 31 respondents who completed an online-survey divided into four sections. After data elaboration, a principal component analysis was conducted, considering 27 questions and their correlations with the first two principal components. The analysis revealed that the presence of internal research and development within the company, along with the company's role in the supply chain and the seasonality of production, had a minor influence on explaining the variance of the data. Interestingly, company size was negatively correlated with the willingness to become part of the insect-rearing supply chain or receive information about this technology. On the contrary, interest in innovative production and self-management of by-products were strongly related. Finally, funding opportunities could positively influence both the Italian and EU contexts, encouraging innovative practices among small and medium-sized enterprises. In contrast, barriers to implementing insect farming include misinformation, anticipated negative media impacts on branding, and resistance to changing by-product management practices. This pilot study serves as a foundational exploration, providing insights into the acceptability of insect farming among potential USPs and offers valuable insights for future studies, thereby shaping the discourse on the acceptability and integration of insect farming within the circular bioeconomy. [ABSTRACT FROM AUTHOR]

This study investigated the sources and distribution characteristics of PM2.5 and its chemical components (ions, carbons, elements) at five locations within the Banwal and Sihwa National Industrial Complexes in Ansan and Siheung. These large-scale industrial clusters, comprising 7642 businesses across sectors such as petrochemicals, steel, machinery, and electronics, operate throughout the year. From 2020 to 2023, the average PM2.5 concentration in the study area was 28.66 ± 16.72 μg/m3, with notable seasonal differences observed across the five measurement points. Ionic components were the primary contributors to PM2.5, while carbon and trace element concentrations fluctuated with the seasons. The coefficient of divergence (COD) analysis indicated that emission source differences between sites were insignificant, with COD values consistently below the threshold of 0.3. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) identified secondary aerosols and vehicle emissions as the main sources of PM2.5, alongside additional contributions from Asian dust, industrial emissions, road dust, coal combustion, metal processing, biomass burning, and soil dust. These results highlight the need for systematic and economical air pollution control strategies in complex industrial areas, using COD to identify source differences and quantify contributions at different sites. [ABSTRACT FROM AUTHOR]

The identification of seismic clusters is essential for many applications of statistical analysis and seismicity forecasting: uncertainties in cluster identification leads to uncertainties in results. However, there are several methods to identify clusters, and their results are not always compatible. We tested different approaches to analyze the clustering: a traditional window-based approach, a complex network-based technique (nearest neighbor—NN), and a novel approach based on fractal analysis. The case study is the increase in seismicity observed in Molise, Southern Italy, from April to November 2018. To analyze the seismicity in detail with the above-mentioned methods, an improved template-matching catalog was created. A stochastic declustering method based on the Epidemic Type Aftershock Sequence (ETAS) model was also applied to add probabilistic information. We explored how the significant discrepancies in these methods' results affect the result of NExt STrOng Related Earthquake (NESTORE) algorithm—a method to forecast strong aftershocks during an ongoing cluster—previously successfully applied to the whole Italian territory. We performed a further analysis of the spatio-temporal pattern of seismicity in Molise, using the Principal Component Analysis (PCA), the ETAS algorithm, as well as other analyses, aimed at detecting possible migration and diffusion signals. We found a relative quiescence of several months between the main events of April and August, the tendency of the events to propagate upwards, and a migration of the seismicity consistent with a fluid-driven mechanism. We hypothesize that these features indicate the presence of fluids, which are also responsible for the long duration of the sequence and the discrepancies in cluster identification methods' results. Such results add to the other pieces of evidence of the importance of the fluid presence in controlling the seismicity in the Apennines. Moreover, this study highlights the importance of refined methods to identify clusters and encourages further detailed analyses when different methods supply very different results. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the common signal integrity issues in vibration monitoring caused by sensor saturation and signal distortion, or sensor loosening and detachment, and the development of a method of detecting the occurrence of vibration signal integrity issues using a one-class support vector machine. For this, vibration signals with distortions due to sensor saturation and/or sensor detachment are analysed to determine parameters sensitive to common integrity issues. These features are then extracted from training data of good quality vibration signals. Principal Component Analysis is used to dimensionally reduce the parameters which are then used in the training of the one-class support vector machine. The proposed method was validated on trained and untrained health conditions. Additionally, model sensitivity was also tested on trained health conditions with varying defect sizes. The method successfully detected all signal integrity issues tested proving to be an effective pre-processing step in condition monitoring systems for increased reliability and accuracy. [ABSTRACT FROM AUTHOR]

Average chemical composition data can be effectively exploited in provenance, i.e. raw material's origin, research of archaeological polished stone artefacts where direct correlation with potential raw materials is possible. This study summarizes the success of Prompt Gamma Activation Analysis (PGAA) method on two different levels of chemical data interpretation for variable rock types found in Hungary, in the Carpathian Basin and their wider vicinity, i.e. material characterization and provenance identification level. Characterization of different lithologies is possible by variable combinations of some major element oxides (Si, Mg, Ca, Na, K, Al, Fe and Ti). Applying statistical methods, boxplots and Principal Component Analysis, we proved that serpentinites, nephrites and Mg-Si-rich subgroup of 'white stones' can be clearly distinguished, while the 'green stone' category cannot be further differentiated, partly overlaps with hornfels and significantly overlaps with other rock types (basalts, metadolerites-metagabbros, andesites and metavolcanites). The provenance identification is successful in the case of contact metabasites and HP metaophiolites, while less exact for nephrites, basalts and hornfels. The non-destructive PGAA proved to be very effective in the identification of certain lithologies, while for other polished stone tool raw materials it has medium certainty and requires complementing methods. [ABSTRACT FROM AUTHOR]

The emergence of new synthetic substances (NPS) leads to rapid changes in substance prevalence. Here urine analysis and questionnaires were employed to construct a landscape of substance abuse. 3,064 urine samples were randomly selected from suspected substance abusers (N = 6,335) from 2014 to 2018 in Taiwan. Demographic information was self-reported. Principal component analysis (PCA) were used to explore the substance pattern. The positive detection of NPS reached 19.6%. The highest links of polysubstance use were the use of phenethylamines with the co-use of amphetamines (aOR = 4.33), benzodiazepines (aOR = 2.09), and synthetic cathinones (aOR = 5.01) and the use of synthetic cathinones with the co-use of ketamine (aOR = 6.72) and benzodiazepines (aOR = 2.62). No evidence indicating an increase in the use of NPS/other substances as a replacement for traditional substances. The presence of polysubstance patterns may be due to similarities in the effects of the various substances or users undergoing a transition in their substance abuse patterns. [ABSTRACT FROM AUTHOR]

Power transformers are one of the most important and expensive equipment in power systems which could be affected by electrical stresses such as traveling waves, switching and thermal stresses such as overload and chemical reactions in dielectric oil. The DGA index expresses the dissolved gases generated due to faults in the transformer oil. The oil quality index determines the effect of electrical, physical and chemical parameters of transformer oil. Oil quality and DGA indices are important in determining the transformer health index. In this paper, the principal components analysis method used to determine the weight coefficients of DGA and oil quality indices. Also, the modes determined by CIGRE technical brochure 761 are used to calculate the mentioned indices. Then, the transformer is considered as a second-order system, and the mentioned indices are determined as parameters of the damping coefficient and natural frequency related to the mass-spring-damper model of the second-order system. Using state space matrices, the transformer system matrix of the second-order system is determined. Finally, the transformer health index is calculated using the ratio of the infinity norm matrix of the new transformer system to the infinity norm matrix of the worked transformer system. [ABSTRACT FROM AUTHOR]

Part of a larger project aimed at performing an empirical meta-theoretical analysis of the entire corpus of scientific literature on Social Representations Theory (SRT), this research presents the state of the art of the anthropological and ethnographic approaches to SRT. Applying the Grid for Meta-Theoretical Analysis on 295 publications selected from the So.Re.Com"A.S. de Rosa"@-library, we compiled a rich set of meta-data and data illustrative of how SRT was conceptualized and operationalized within the anthropological and ethnographic approaches, as well as its positioning among other theoretical and disciplinary frameworks. The data was submitted to textual analysis, followed by a Hierarchical Clustering on Principal Components analysis. The empirical results suggest that from a theoretical standpoint, the anthropological and ethnographic approaches - inspired by its main exponents Jodelet (1991, 2016) and Duveen and Lloyd, (1986, 1993) - are consistent with the dynamic conceptualization of social representations set out by Moscovici (1961/1976, 1984/2003, 1988, 2000, 2013), as revolutionary paradigm that has shifted the emphasis of social psychology from looking at isolated variables in individuals in the abstract, towards a supra-disciplinary integrative vision of a social science, that investigates the genesis, transformation and negotiation of social representations in the communicative actual contexts (Billig 1991; de Rosa 2013a, b; Sammut et al. 2015a). From an empirical perspective, the variety of qualitative methods employed were open to investigate socio-cultural dimensions and symbolic universes, reflecting the integrative tradition of SRT that bridges diverse neighbouring disciplines in an effort towards a multifaceted perspective on the object of study. [ABSTRACT FROM AUTHOR]

Defining species within the Canidae family is challenging due to morphological convergence, behavioral plasticity, traditional taxonomic limitations, and possible hybridisation. This challenge is timely with the recent population and range expansion of the golden jackal (Canis aureus). Exploring their morphological data and sexual dimorphism is essential for identifying factors driving their success in new habitats. The proven hybridization of golden jackals with dogs and wolves may affect species description, population dynamics, and genetic diversity, impacting conservation strategies. This study, for the first time, conducts a morphometric analysis of golden jackals in Somogy County, Hungary, to prove sexual size dimorphism (SSD) in body and skull and sexual shape dimorphism (SShD) in skull across juvenile and adult age groups. 719 golden jackals (362 females and 357 males) were collected between January 2021 and January 2023. Descriptive statistics revealed significant SSD in body and skull measurements among both age groups, with males generally larger than females, particularly in body mass (11.72% in juveniles and 13.37% in adults). Most skull dimensions differed significantly between sexes and age groups, except for foramen magnum height, foramen magnum width, and postorbital breadth among juveniles and foramen magnum height and postorbital breadth among adults. We used principal component analyses (PCA) on raw dimension data and the log shape ratio method to extract shape information. Linear discriminant analysis (LDA) explored skull SShD between sexes. Notably, our study achieved over 71% accuracy in sex classification, illustrating the clear presence of SShD of the skull in golden jackals across both age groups. Our study provides a comprehensive database of golden jackals in the overpopulated Hungarian habitat, which will be helpful for further research on ecology, behavior, and conservation management. [ABSTRACT FROM AUTHOR]

The elemental composition of 29 granite rock samples from the Gabel Elba–Wadi Eikhwan area situated in the southern sector of Egypt's Eastern Desert were analyzed using neutron activation analysis (NAA) and X-ray fluorescence (XRF). Altogether, 45 major and trace elements were quantitatively determined. Bivariate and multivariate statistical analyses were conducted to distinguish and categorize the rock-forming elements according to their elemental content. The applied ratio indicators and discriminant diagrams show that the studied rock samples are well classified to be granite. The principal component analysis results demonstrate clustered samples of common geochemical features. The integration of NAA and XRF has demonstrated its efficacy and relevance in rock studies. The obtained results hold significant importance for the regions investigated and can serve as fundamental data for future assessments and prospects. [ABSTRACT FROM AUTHOR]

The 2030 Agenda has among its key objectives the poverty eradication through increasing the level of education. A good level of education and investment in culture of a country is in fact necessary to guarantee a sustainable economy, in which coexists satisfactory levels of quality of life and an equitable distribution of income. There is a lack of studies in particular on the relations between some significant dimensions, such as education, culture and poverty, considering time lags for the measurement of impacts. Therefore, this study aims to fill this gap by focusing on the relationship between education, culture and poverty based on a panel of data from 34 European countries, over a 5-year period, 2015–2019. For this purpose, after applying principal component analysis to avoid multicollinearity problems, the authors applied three different approaches: pooled-ordinary least squares model, fixed effect model and random effect model. Fixed-effects estimator was selected as the optimal and most appropriate model. The results highlight that increasing education and culture levels in these countries reduce poverty. This opens space to new research paths and policy strategies that can start from this connection to implement concrete actions aimed at widening and improving educational and cultural offer. [ABSTRACT FROM AUTHOR]

Stephania tetrandra S. Moore (family: Menispermaceae), a dioecious herbaceous vine and the only species in the subgenus Botryodiscia of the genus Stephania of the family Menispermaceae, is mainly distributed in hilly areas south of the Huaihe River in China and found in many provinces of China, showing a high genetic diversity. This paper aimed to study genetic diversity of and genetic relationship among individuals of S. tetrandra within China to provide a basis for evaluation, exploitation, and utilization of S. tetrandra by using simple sequence repeat (SSR) molecular markers. Our results show that effective products were amplified from the 26 screened SSR gene loci, a total of 183 alleles amplified (2–16 alleles amplified by each pair of primers). Among the 26 loci, 16 had a PIC value higher than 0.5, indicating a high level of polymorphism. For most of the loci, the number of effective alleles was lower than that of the observed alleles, and the observed heterozygosity was lower than the expected heterozygosity. The genetic differentiation coefficient (0.021–0.547) was lower than 0.05 (low level of genetic differentiation) for 7 loci and higher than 0.25 (very high level of genetic differentiation) for 2 loci, and had a value representing a medium level of genetic differentiation for the remaining 17 loci. The intra-population inbreeding coefficient had a positive value for 21 loci, suggesting the presence of inbreeding and homozygous excess. The gene flow value was bigger than 1, indicating that genetic drift and natural selection played an unimportant role in population genetic differentiation of S. tetrandra. Based on discriminant analysis of principal components and Bayesian Information Criterion, K-means clustering was performed on 620 samples. These samples were divided into 9 genetic clusters, whose similarity coefficients and genetic distances were 0.755–0.918 and 0.067–0.280, respectively, indicating that these clusters were highly similar and short-distanced. The Bayesian clustering analysis was implemented in the STRUCTURE software to analyze the genetic structure of S. tetrandra and it was found that the 620 samples could be clustered into 5 ancestor groups; the 9 clusters and 40 natural populations inherited genes from the 5 groups to varying degrees, but the proportion of genes inherited from the 5 groups by each cluster and natural population differed. S. tetrandra was characterized by the presence of population structure and pronounced genetic subdivision, which, together with the presence of gene flow, may indicate a relatively stable recent state of these populations. [ABSTRACT FROM AUTHOR]

Objectives: Diabetic retinopathy (DR), an earnest complication of diabetes, is one of the most common causes of blindness worldwide. This study aimed to investigate the altered metabolites in the serum of non-DR (NDR) and DR including non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) subjects. Methods: In this study, the 1HNMR platform was applied to reveal the discriminating serum metabolites in three diabetic groups based on the status of their complications: T2D or NDR (n = 15), NPDR, (n = 15), and PDR (n = 15) groups. Multivariate analyses include principal component analysis (PCA) and Partial Least Structures-Discriminant Analysis (PLS-DA) analysis that were performed using R software. The main metabolic pathways were also revealed by KEGG pathway enrichment analysis. Results: The results revealed the significantly different metabolites include 10 metabolites of the NPDR versus PDR group, 24 metabolites of the PDR versus NDR group, and 25 metabolites of the NPDR versus NDR group. The results showed that the significantly altered metabolites in DR compared with NDR serum samples mainly belonged to amino acids. The most important pathways between NPDR/PDR, and NDR/DR groups include ascorbate and aldarate metabolism, galactose metabolism, glutathione metabolism, and tryptophan metabolism, respectively. In addition, some metabolites were detected for the first time. Conclusions: We created a metabolomics profile for NDR, PDR and NPDR groups. The impairment in the ascorbate/aldarate, galactose, and especially amino acids metabolism was identified as metabolic dysregulation associated with DR, which may provide new insights into potential pathogenesis pathways for DR. [ABSTRACT FROM AUTHOR]

Purpose: Hepatocellular carcinoma (HCC) is the third-leading cause of cancer-related deaths globally. Patients are often diagnosed with advanced disease, in which systemic and locoregional therapies are commonly used as first-line treatment. Such treatments can cause adverse events (AEs) that negatively affect quality of life (QoL), which is particularly undesirable where prognosis is poor. The aim of the present study was to evaluate the impact of common AEs on QoL in patients with HCC. Methods: Data from the SARAH randomized controlled trial (RCT) were analyzed. Given the large number of distinct AEs that occurred in the trial, AEs were grouped as in the SARAH trial and prioritized using principal component analysis (PCA). Linear mixed-effects models were then applied with age, ECOG status, and AEs as predictors of the QoL change as measured with the EORTC Core Quality of Life Questionnaire (QLQ-C30). Results: The PCA resulted in the selection of 28 AEs for inclusion in the linear mixed-effects models. Of the 28 AEs, diarrhea, decreased appetite, abdominal pain, and palmar-plantar erythrodysesthesia syndrome (hand-foot syndrome) were significant drivers of reductions in QoL as measured using the QLQ-C30 global health status scale. Diarrhea, abdominal pain, and hand-foot syndrome were also significant drivers of reduced QoL outcomes. Conclusion: The present analysis showed that diarrhea, decreased appetite, abdominal pain, and palmar-plantar erythrodysesthesia were significantly associated with reduced QoL in patients with unresectable HCC. Reducing the incidence and/or severity of these AEs should therefore be a key focus when selecting the optimal treatments for these patients. Plain English summary: Patients with advanced liver cancer can have a poor prognosis, and preservation of quality of life is an important consideration when choosing appropriate treatments. The SARAH clinical study looked at two treatment options for primary liver cancer — sorafenib and selective internal radiation therapy or SIRT — and collected data on quality of life and how often patients experienced adverse events. Our study used these data to understand which adverse events may have had the largest effect on quality of life. We found that, no matter how severe, gastrointestinal disorders (such as diarrhea, vomiting, nausea, and anorexia) were associated with significant reductions in patient quality of life. We also found that severe constitutional symptoms (such as fever, fatigue, and weight loss) significantly reduced quality of life. The findings may be used to inform choices of treatments or to better prioritize the management of adverse events that cause the greatest reductions in quality of life. [ABSTRACT FROM AUTHOR]

Purpose: The Core Outcome Measures Index (COMI) is a short, multidimensional instrument translated into several languages that covers five domains recommended in the assessment of outcome in patients with low-back and neck pain. The purpose of this study was to cross-culturally adapt the COMI from English to Czech language and to test the face and construct validity and reproducibility of its results in patients with low-back and neck pain. Methods: Participants (n = 125) were included from primary and secondary care. The participants reported moderate pain and disability levels. All participants filled in the COMI forms before and after surgery. Descriptive statistics, Wilcoxon paired test, Crombach's alpha, principal component analysis and information entropy calculation were used. Results: The instrument was successfully forward and back-translated. It can be seen that the questionnaire applied as part of our intervention study produces answers with a sufficient degree of variability and with a satisfactory degree of representation of extreme values. It can be also seen that the questionnaire can diagnose an objectively occurring change associated with the surgeon within the intervention procedure. Our other findings support the idea of ​​a possible reduction in the number of questions that measure the same latent variable. Our investigations also showed that it is possible to reduce the range of the point scale of the perception of pain to 5 degrees of intensity and thus unify the range with the other questions. Conclusion: The Czech COMI shows acceptable properties and is thus suitable to use as a short instrument for measuring important domains in patients with low-back and neck pain. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is widespread globally, with the emergence of metabolites derived from both the host and microbes playing a pivotal role in its pathogenesis. This study aims to elucidate the relationships between serum metabolites and the immunological and clinical features of RA. Serum samples were collected from 35 RA patients and 37 healthy controls (HC). Metabolite profiling was performed using gas chromatography-mass spectrometry (GC/MS). Principal component analysis revealed a significant distinction between the RA and HC cohorts. Employing univariate statistical analysis, we identified 36 differential metabolites. Among these, 9 metabolites, including galactose and glucose, were found to be enriched, while the remaining metabolites, such as citric acid, fumaric acid, and inosine, were depleted in RA. These diverse metabolites encompassed various metabolic processes, including the biosynthesis of fatty acids, amino acids, and glucose. The enrichment of glucose and galactose in RA exhibited a substantial correlation with elevated IgG levels, as determined through correlation analysis. Conversely, the depletion of citric acid was correlated with elevated levels of C3 and CRP. Methionine, which also declined in RA patients, displayed a negative correlation with ESR. Furthermore, galactose and glucose exhibited significant positive correlations with naïve B cells, while the decreased eicosanoic acid level in RA was significantly associated with an increase in natural killer cells. Our findings suggest that the altered serum metabolite profile in RA is closely linked to disease severity and the dysregulated immune responses observed in RA patients. Key Points • Identified nine metabolites with upregulated expression and twenty-seven metabolites with downregulated expression. • Established a correlation between alterations in serum metabolite levels and inflammatory markers in RA patients. • Discovered a significant association between changes in serum metabolites and immune cell profiles in RA patients. [ABSTRACT FROM AUTHOR]

Freshwater resources, specially surface water are under threat due to over extraction, discharge of pollutants and improper waste disposal. This study investigates the present status of water quality of the River Ganga at Varanasi, India and further predict its future status using Principal Component Analysis (PCA), Entropy water quality index (EWQI) and Stochastic models. To begin with, water quality data of 37 variables for eleven years were acquired followed by which PCA was applied which reduced the number of water quality variables from 37 to 13. EWQI of River Ganga was calculated for drinking and bathing purposes by using these 13 variables. Most of the physico-chemical variables were within the permissible limit. The EWQI values were calculated for all the samples indicated that none of the water sample was suitable for drinking without treatment. However, 74.24% of the samples were classified as fair, 10.60% as poor, and 15.15% as unfit for bathing. Five different time-series models with 10 different structures were accessed then compared the effectiveness of various time series models in predicting water quality with the help of data from the past eleven years. Finally, the chosen models were used to predict future water quality variables and EWQI for both drinking and bathing purposes. The optimized models were selected based on auto-correlation function and partial auto-correlation function as well as the use of Akaike information criteria and Bayesian Information Criteria. This research suggests that time series modelling can be a cost-effective and time-saving approach for long-term water quality monitoring. [ABSTRACT FROM AUTHOR]