Principal Component Analysis Research Articles

Determination of Local Cherry Tomato Genotypes' Agromorphological Features in Türkiye and Kyrgyzstan

The purpose of this study was to ascertain some morphological traits and interrelationships of 36 genotypes of cherry tomatoes that were gathered from Turkey and Kyrgyzstan. Measurements and observations include flower number of cluster, leaf length, leaf width, leaf attitude, fruit color, fruit neck shape, fruit cross-sectional shape, fruit weight, fruit firmness, time of maturity, number of locules, thickness of pericarp, and total soluable solid content, in accordance with the guidelines of the International Union for the Protection of New Varieties of Plants (UPOV). The genotypes T40 (62.7 g), T24 (50.8 g), T107 (50 g), T103 (41.4 g), and T105 (40.8 g) had the highest average fruit weights, whereas T3 (9.4 g) and K1 (10.6 g) had the lowest fruit weights. The pericarp had an average thickness of 3.82 mm and an average of 2.49 locules. A spherical fruit cross-section was observed in 97.2% of genotypes. Genotype K2 had the highest total soluble solids content (TTSC%) at 9.4%, whereas genotype K13 had the lowest TTSC% at 3.1%. To measure the data, Principal Component Analysis (PCA) was used. Five separate principle component axes were identified via PCA, and these axes together account for 75.57% of the dataset's variation. These principal components' eigenvalues, which varied from 1.07 to 3.24, showed how much variance was accounted for by each component. The PCA results showed that the genotypes T107, T40, T105, and T87 performed better in PC1 in terms of the number of locules, fruit weight, fruit cross-sectional shape, and pericarp thickness characteristics. Fruit color was influenced by genotypes T90 and T142, fruit firmness by genotype T40, and total soluble solids content by genotype T98. The indigenous cherry tomato varieties from Turkey and Kyrgyzstan were shown to have a large amount of morphological variety, which may be useful for breeding initiatives in the future.

Research on Factors Influencing Housing Price based on Principle Component Analysis and Polynomial Regression

Housing price is influenced by natural, social, economic and other factors. Only by understanding and conducting specific research on these influencing factors can people grasp the laws of housing price movements. Focusing on this issue, this paper chooses the average unit housing prices and their influencing factors of 11 cities in Zhejiang Province in 2022 as the research objects, then establishes a model reflecting the quantitative relationship between housing price and influencing factors. Firstly, relevant data is collected from official websites, including housing prices and 10 main influencing factor indicators of above cities. Secondly, principal component analysis is used for data preprocessing, and five prominent indicators are selected by calculating the relative contribution rate of each factor. Then, by drawing functional images and solving correlation coefficients, a qualitative analysis is conducted on the basic algebraic structure of the regression model. The results indicate that all independent variables include independent terms and cross-terms. Subsequently, through polynomial regression, data matrices for both dependent variable and independent variables are set to solve the coefficients of constant term, linear terms, quadratic independent terms and quadratic cross-terms respectively. Therefore, a model of factors influencing housing price is established. Finally, residuals sum of squares, mean square error, regression sum of squares and determination coefficient are calculated in sequence for the regression model. It turns out that all the statistics are within the ideal range, indicating high precision of data fitting and verifying the accuracy and reliability of the model.

Enhancing Industrial Valve Diagnostics: Comparison of Two Preprocessing Methods on the Performance of a Stiction Detection Method Using an Artificial Neural Network

The detection and mitigation of stiction are crucial for maintaining control system performance. This paper proposes the comparison of two preprocessing methods for detecting stiction in control valves via pattern recognition via an artificial neural network (ANN). This method utilizes process variables (PVs) and controller outputs (OPs) to accurately identify stiction within control loops. The ANN was comprehensively trained using data from a data-driven model after processing them. Validation and testing were conducted with real industrial data from the International Stiction Database (ISDB), ensuring a practical assessment framework. This study evaluated the impact of two preprocessing methods on fault detection accuracy, namely, the D-value and principal component analysis (PCA) methods, where the D-value method achieved a commendable overall accuracy of 76%, with 86% precision in stiction prediction and a 66% success rate in nonstiction scenarios. This signifies that feature reduction leads to a degraded stiction detection. The data-driven model was implemented in SIMULINK, and the ANN was trained in MATLAB with the Pattern Recognition Toolbox. These promising results highlight the method’s reliability in diagnosing stiction in industrial settings. Integrating this technique into existing control systems is expected to enhance maintenance protocols, reduce operational downtime, and improve efficiency. Future research should aim to expand this method’s applicability to a wider range of control systems and operational conditions, further solidifying its industrial value.

Rework in civil construction projects at design phase: Influencing factors and suggested solutions

Among the various project phases, the design phase often contains errors that result in rework in civil construction projects. This rework can lead to budget overruns, schedule delays, and a decline in construction quality. There is rare knowledge of reworks in civil construction projects, especially in the design phase. This paper presents an overview of the influencing factors and solutions for reworks in civil construction projects at the design phase. To achieve this objective, a survey on the causes of rework was conducted with experts experienced in designing and constructing civil projects in southern Vietnam. Civil engineers and investors involved in the project at the design phases were targets of the survey. Five groups, including designers, design managers, investors, subcontractors, and objective conditions, were selected for designing survey questionnaires. Then, the PCA (principal component analysis) was applied to the collected data to reveal the most influencing factors on rework causes. Based on the results obtained, several suggestions are proposed to minimize rework in civil construction projects. These findings can serve as valuable insights for investors and design consultants, fostering learning and process improvements to reduce rework in future projects.

Machine Learning in Agriculture: KNN-Based Classification of Fruits and Vegetables

In modern agriculture and the food industry, it is essential to classify fruits and vegetables accurately and efficiently to meet growing consumer demand and reduce post-harvest losses. Traditional manual methods are often labor-intensive and error prone, highlighting the need for automated solutions. This paper discusses the application of the k Nearest Neighbor (KNN) algorithm to fruit and vegetable recognition using image processing technology. In this study, image data sets are used for feature extraction using Directional Gradient Histogram (HOG) and dimensionality reduction using Principal Component Analysis (PCA). The accuracy of KNN model on verification set and test set reaches 97%, which proves its validity. Confusion matrix analysis and F1 score evaluation further revealed the strengths and areas of improvement of the model, particularly in distinguishing visually similar categories. The results show that the integration of artificial intelligence (especially KNN) offers great potential for the automation of agricultural classification tasks. Future studies could combine more advanced models, such as CNNS, with larger datasets to improve accuracy and robustness.

Thoron, radon and microbial community as supportive indicators of seismic activity in groundwater

Earthquakes have a significant impact on groundwater environments as well as human life. However, identifying active and affected zones from seismic events using isotopic and microbial diversity indicators remains a challenging frontier. To validate the applicability of this coupled method for real-time analysis, we analyzed thoron (220Rn), radon (222Rn), microbial community compositions, and hydrochemistry in groundwater samples during the 2017 Pohang earthquake for the first time. We observed the detection of 220Rn in groundwater right before the aftershocks, with a high correlation to 222Rn concentrations. This indicates that 220Rn and 222Rn can serve as reliable seismic indicators for real-time analysis. The microbial data can assist in identifying affected groundwater zones, particularly when real-time detection of 220Rn is not feasible. At the phylum level, Peregrinibacteria and Firmicutes were only found in samples with detected thoron. At the genus level, hydrogen-oxidizing or sulfur-oxidizing bacteria could serve as indicators of active zones. Two statistical analyses, self-organizing map (SOM) and principal component analysis (PCA) using hydrochemical parameters, also correlated with the results from these coupled indicators. This study demonstrates the theoretical and practical applicability of 220Rn, 222Rn, and microbial community compositions as new multi-faceted ecological indicators, whether for real-time analysis or otherwise.

Genetic Relationships and Population Structure among Nigerian Ethnic Groups (Ibibio, Igbo, Hausa, Tiv and Yoruba) Based on Nine DNA Loci

Abstract Context: Population-specific attributes typically determine the behavior of human genotypes and traits in individuals. Examining the genetic relationships between populations presents an opportunity to observe patterns of genetic changes over time. By comparing populations, it is possible to see factors that may have sparked the evolution of a trait or genotype as well as the genetic diversity of the populations. Genetic diversity in the groups in Nigeria is still largely undefined. This can be achieved with the use of genetic markers. Aims: To determine the genetic relationships between Igbo, Ibibio, Yoruba, Tiv, and Hausa ethnic groups using nine short tandem repeat DNA markers. Settings and Design: Observational studies of 250 consenting participants. Methods and Material: Individuals of Igbo, Yoruba, Hausa, Ibibio, and Tiv were randomly sampled from their ethnic homes in Nigeria at participating institutions. DNA was extracted from blood samples using commercial DNA kits, and tested for nine autosomal short tandem repeat loci alleles using PCR and electrophoresis. The allele types and sizes were read and recorded for each locus for every individual. Statistical Analysis Used: Allelic frequencies, population pairwise genetic distances (FST and RST, analysis of molecular variance (AMOVA), and principal component analysis (PCA) were calculated using GenAlEx v6.502. Results: The fixation index (FST) was between 0.001 and 0.500 in the five populations. The estimated variation was 99.98% within individuals and 0.10% among populations. Four heterogeneous clusters were observed with three axes accounting for 32.86% variation. Conclusions: The genetic relationships closely mirrored linguistic groupings showing that language may have shaped early interaction and ultimately gene transfer between these groups.

Psychometric evaluation of the Chinese version of the nurse turnover intention scale: a translation and validation study

BackgroundGlobally, the issue of high nurse turnover rates is prevalent in China. The COVID-19 pandemic exacerbated the attrition rate of nurses. Turnover intention is the most reliable predictor of nurse turnover. However, there is currently a lack of proper evaluation tools in China. This study aimed to translate the Nurses Turnover Intention Scale (NTIS) into Chinese and examine the psychometric characteristics of the Chinese version of the NTIS among clinical nurses in China.MethodsApproximately 418 nurses were recruited from three tertiary general hospitals in Jiangxi, Guangdong, and Zhejiang provinces. This study used Brislin’s double-back translation, back-translation, and cross-cultural adaptation methods to translate the scale. Internal consistency, split-half reliability, and test-retest reliability were used to measure the reliability of the Chinese scale version. The validity of the Chinese scale version’s content was evaluated via the Delphi method. Exploratory and confirmatory factor analyses were carried out to evaluate the construct validity of the Chinese scale versions. In exploratory factor analysis, principal component analysis and maximum variance rotation method were used in this study. In the confirmatory factor analysis, Amos 24.0 software was used for analysis.ResultsThe NTIS in Chinese has ten elements: a Cronbach α coefficient of 0.864, a Cronbach α coefficient of 0.852∼0.903 for the three dimensions, and a split-half reliability and test-retest reliability of 0.878 and 0.960, respectively. This indicates that the Chinese version of the NTIS has good internal consistency and time stability. The scale-level content validity index (S-CVI) was 0.957, which indicated that the Chinese version of NTIS had good content validity. The total variance contribution rate was 79.055%, and three common factors were identified via exploratory factor analysis. The examined three-factor structure was confirmed by confirmatory factor analysis, which confirmed that all fit indices were appropriate.ConclusionsThe Chinese version of the NTIS was appropriate in terms of reliability and validity. This scale can be used to evaluate nurses’ turnover intention, and the outcomes can aid nurse management in designing training initiatives and implementing preventive measures to decrease nurse turnover.

Effect of soil and community factors on the yield and medicinal quality of Artemisia argyi growth at different altitudes of the Funiu mountain

Altitude and ecological factors significantly influence plant growth and the accumulation of secondary metabolites. However, current research on the impact of altitude and ecological factors on the yield and medicinal quality of Artemisia argyi (A. argyi) is limited. This study established sampling sites in wild populations of A. argyi across seven altitude ranges on Funiu Mountain. We quantified the yield, output rate of moxa, and key medicinal ingredients. Additionally, we analyzed the response of yield and medicinal quality of wild A. argyi populations to various ecological factors at different altitudes. The results showed that wild populations of A. argyi exhibited higher yields and medicinal quality at altitudes below 500 m. Yield was positively correlated with higher soil total nitrogen (TN) content and lower soil total phosphorus (TP) content, while the improvements in medicinal quality were positively associated with higher population density and lower contents of both soil TN and TP. The variation in soil C/N, C/P, and N/P ratios across different altitudes was substantial, affecting soil mineralization and subsequently influencing the absorption of mineral elements by A. argyi. Notably, the phosphorus content in leaves and stems was negatively correlated with yield and medicinal quality, respectively. In contrast, the accumulation of nitrogen, phosphorus, and potassium in leaves was positively correlated with yield. The differences in the primary medicinal ingredients between the leaves and stems of A. argyi were maximum at altitudes below 500 m. The contents of neochlorogenic acid and cryptochlorogenic acid in both leaves and stems showed a significant positive correlation. In the principal component analysis, the primary medicinal ingredients from the leaves contributed more significantly to the overall quality than those from stems. These results suggest that A. argyi is best suited for cultivation at altitudes below 500 m. Population density and the soil’s TN and TP contents play a crucial role in determining the yield and medicinal quality of A. argyi. Futhermore, the medicinal quality of A. argyi is more indicative of the main medicinal ingredients found in the leaves, while the stems also serve as a key organ for accumulating flavonoids and phenolic acids.

Characterization of quality parameters and phytosterol content in oils and their formulated margarines

AbstractUnlike lipid stability and oxidation studies in commonly used edible oils and margarines, margarines formulated with unconventional oils are not well characterized. This study investigated the effect of heat treatment (HT) on the stability and content of phytosterol in njangsa seed oil (NSO), bush mango oil (BMO), soybean oil (SBO), coconut oil (CCO), and margarines formulated from their blends: BN (BMO and NSO), BS (BMO and SBO), CN (CCO and NSO), CS (CCO and SBO), and commercial margarines (CM1 and CM2). Both oils and margarines were heat‐treated at 130, 170, and 210°C for 10, 15, 20, and 120 min (only oils). Changes in free fatty acid (FFA), peroxide value (PV), para‐anisidine value (AnV), and fatty acid (FA) composition and phytosterol content were determined after 20 min (margarines) and 120 min (oils) of HT and compared to their control/pre‐HT/unheated (UH) counterparts. The FA composition did not change significantly with HT. Polyunsaturated fatty acids (PUFA)‐rich oils such as NSO and SBO showed significantly higher increase in FFA content with HT compared with oils with higher saturated fatty acid content (BMO and CCO). Oils with higher proportions of linoleic acid, such as SBO (68.3%) and NSO (60.35%), had higher AnV at the end of the HT compared with oils with lower content, such as BMO (0.51%). Phytosterol content fluctuated with HT, and changes in content were generally more pronounced in β‐sitosterol than in stigmasterol and campesterol. Both principal component analysis (PCA) and partial least‐squares discriminant analysis (PLS‐DA) were carried out to observe possible clusters. The results suggest that changes in quality and content of oils and margarines during heating are dependent on more than their fatty acid composition.

Climate change vulnerability assessment for adaptation planning in Uttarakhand, Indian Himalaya

Climate change vulnerability estimation at all spatial scales is imperative for the development of effective adaptation strategies in the biogeographically fragile Himalayan region. This study aims to estimate district-wise climate change vulnerability in the state of Uttarakhand for the year 2022 by integrating climatic, environmental, and socio-economic factors. Employing an integrated approach, nine components (climate change, natural disaster, ecosystem services, agriculture, socio-economic status, human resource capacity, infrastructure, basic facilities, and social/natural capital) incorporating a total of 63 indicators, are used to estimate exposure (E), sensitivity (S), adaptive capacity (AC) and the vulnerability of the study area. Principal component analysis (PCA) is used to assess the suitability and weights of all the indicators. The findings show that middle (1400–2400 m a.s.l.) and higher (>2400 m a.s.l.) districts of the state are more vulnerable (−0.68 to −1.50) than lower (1–1400 m a.s.l.) districts (0.16 to −0.26). Based on the vulnerability index (−0.68 to −1.50), five districts-Uttarkashi, Rudraprayag, Chamoli, Champawat, Pithoragarh, and Bageshwar are identified as priority districts for adaptation planning. The high vulnerability is primarily attributed to increased exposure to excessive precipitation, cold waves, cloudbursts, and flood events, coupled with high ecosystem sensitivity and low adaptive capacity. In contrast, the lower districts of the state benefit from better infrastructure, social and natural capital, and connectivity, which contribute to low vulnerability. The suggested strategies in the present study would help policymakers to allocate resources efficiently, fostering long-term resilience to climate change and sustainable development.

Quality assessment of various Xiushui Ninghong tea types.

Xiushui Ninghong tea (XSNH) has a long history and is renowned both in China and internationally. Based on different processing techniques, XSNH can be classified into Ninghong Congou, Ninghong Tea Jinhao, Ninghong Tea Longxucha and other types. To investigate the differences in nutrient compounds and mineral element contents among various types of XSNH, 34 samples from seven types were collected, primarily from tea-producing areas. Statistical analysis indicated no significant differences in the contents of crude polysaccharides, K, Mg and Fe, whereas significant differences were observed in the levels of moisture, free amino acids, caffeine, tea polyphenols, thearubigin, theaflavins, Zn, P, Mn, Cu and Se. The data were analyzed using various statistical methods such as hierarchical cluster analysis, principal component analysis and partial least squares discriminant analysis. Characteristic compounds and elements such as theaflavin, Se, free amino acids, P and tea polyphenols were identified as key differential components for distinguishing different sample types. Our research has highlighted the differences in chemical indicators among various types of XSNH, providing a crucial theoretical basis for the future classification and grading of XSNH quality. © 2024 Society of Chemical Industry.

Selection and immobilization of the optimal phytase-producing probiotic: a comparative in silico analyses of five autochthonous lactic acid bacteria

Phytic acid and its salts are the major storage form of both phosphate and myo-inositol, especially in cereals. Phytic acid chelates multivalent cations, which decreases the solubilized form of these nutrients and their dietary bioavailability for absorption and assimilation. Increasing micronutrient bioavailability from the economic phytate-rich food can be achieved by subjecting this food to fermentation by phytase-producing bacteria. A total of 8 lactic acid bacteria were isolated from different fermented food samples. Among the bacteria tested for their ability to produce phytase, only five isolates were selected as promising producers. The results revealed that isolate No. 4 produced the highest phytase levels of 411 U/mL. The five isolates were differentially able to tolerate acidic, alkaline, heat, surfactant, osmotic, bile, and pancreatic enzyme stresses, with the superiority of isolate No. 4, which demonstrated the most desirable probiotic potentials as confirmed by principal component analysis (PCA), heat map, and network analysis. Isolate No. 4 was identified by 16S rRNA sequencing and later confirmed as Pediococcus pentosaceus strain NMP4762Ch under the accession number MZ413646. Moreover, the active cells of P. pentosaceus were immobilized in alginate; the reusability of the immobilized active cells resulted in continuous production with an optimum phytase activity of 432.0 U/mL after 14 days, which decreased with time to reach 142.5 U/mL after 56 days. The maximum shelf stability was also observed, 557.5 U/mL, after 14 days and declined with time, reaching 133.5 U/mL by the end of the 56th day. Since humans don’t naturally produce phytase, utilizing probiotics for phytase production is important to combat mineral deficiencies.

Assessing methods for telluric removal on atmospheric retrievals of high-resolution optical exoplanetary transmission spectra

Recent advancements in ultra-stable ground-based high-resolution spectrographs have propelled ground-based astronomy to the forefront of exoplanet detection and characterisation. However, the resultant transmission and emission spectra of exoplanetary atmospheres are inevitably contaminated by telluric absorption and emission lines due to the light's transmission through the Earth's atmosphere above the observatory. Retrieving accurate atmospheric parameters depends on accurate modelling and removal of this telluric contamination while preserving the faint underlying exoplanet signal. There exist many methods to model telluric contamination, whether directly modelling the Earth's transmission spectrum via radiative transfer modelling, or using a principal component analysis (PCA)-like reconstruction to fit the time-invariant features of a spectrum, and removing these models from the observations. We aimed to assess the efficacy of these various telluric removal methods in preserving the underlying exoplanetary spectra. We compared two of the most common telluric modelling and removal methods molecfit and the PCA-like algorithm SysRem using planetary transmission spectra injected into three high-resolution optical observations taken with ESPRESSO. These planetary signals were injected at orbital periods of P = 2 days and P = 12 days, resulting in differing changes in radial velocity during transit. We then retrieved various injected atmospheric model parameters in order to determine the efficacy of the telluric removal methods, as well as their effect on the transmission spectra of exoplanets with varying orbital architectures. For the close-in, high velocity injected signal, we found that SysRem performed better for species that are also present in the Earth's atmosphere--with accurate and precise retrieval of atmospheric abundances and $T$-$P$ profiles, across each of the datasets. As we moved to slower moving signals at larger orbital separations, for one of the three datasets SysRem dampened the planetary H$_2$O signal, leaving the retrieved abundance value unconstrained. In contrast, the H$_2$O signal was preserved for the telluric modelling method molecfit . However, this behaviour was not ubiquitous across all three of the injected datasets, with another dataset showing a more precise H$_2$O/Fe ratio when preprocessed with SysRem . These conflicts highlight the importance of testing individual high-resolution dataset reduction routines independently to ensure real exoplanetary signals are preserved.

Urban Heat Island and Environmental Degradation Analysis Utilizing a Remote Sensing Technique in Rapidly Urbanizing South Asian Cities

Rapid urbanization in South Asian cities has triggered significant changes in land use and land cover (LULC), degrading natural biophysical components and intensifying urban heat islands (UHIs). This study investigated the impact of LULC changes on land surface temperature (LST) and the role of biophysical indicators in enhancing urban resilience to thermal extremes. We used Landsat satellite imageries from 1993 to 2023, conducted a comprehensive analysis of LULC changes, and estimated LST variations at 6-year intervals in the Dhaka, Gazipur, and Narayanganj districts in Bangladesh. Afterward, we performed statistical analysis upon employing correlation, regression, and principal component analysis (PCA) techniques to summarize information. The results reveal that 339.13 km2 worth of urban expansion has occurred in last 30 years, with an average annual growth rate of 3.5%, accompanied by a substantial reduction in water bodies (−139.17 km2) and vegetation cover. Consequently, summer temperatures exceeded approximately 36.52 °C in dense urban areas. Also, the results highlighted the strong influence of built-up areas (BSI and SAVI) on LST, while vegetation (NDVI) and water indices (NDWI) exhibited a negative association. The findings emphasize the urgency of integrating green infrastructure and deploying sustainable urban planning policies to mitigate the potential adverse impacts of scattered urbanization in the face of climate change.

Peripheral innate immunophenotype in neurodegenerative disease: blood-based profiles and links to survival.

The innate immune system plays an integral role in the progression of many neurodegenerative diseases. In addition to central innate immune cells (e.g., microglia), peripheral innate immune cells (e.g., blood monocytes, natural killer cells, and dendritic cells) may also differ in these conditions. However, the characterization of peripheral innate immune cell types across different neurodegenerative diseases remains incomplete. This study aimed to characterize peripheral innate immune profiles using flow cytometry for immunophenotyping of peripheral blood mononuclear cells in n = 148 people with Alzheimer's disease (AD), frontotemporal dementia (FTD), corticobasal syndrome (CBS), progressive supranuclear palsy (PSP), Lewy body dementia(LBD) as compared to n = 37 healthy controls. To compare groups, we used multivariate dissimilarity analysis and principal component analysis across 19 innate immune cell types. We identified pro-inflammatory profiles that significantly differ between patients with all-cause dementia and healthy controls, with some significant differences between patient groups. Regression analysis confirmed that time to death following the blood test correlated with the individuals' immune profile weighting, positively to TREM2+ and non-classical monocytes and negatively to classical monocytes. Taken together, these results describe transdiagnostic peripheral immune profiles and highlight the link between prognosis and the monocyte cellular subdivision and function (as measured by surface protein expression). The results suggest that blood-derived innate immune profiles can inform sub-populations of cells relevant for specific neurodegenerative diseases that are significantly linked to accelerated disease progression and worse survival outcomes across diagnoses. Blood-based innate immune profiles may contribute to enhanced precision medicine approaches in dementia, helping to identify and monitor therapeutic targets and stratify patients for candidate immunotherapies.

Enhancing Mineral Exploration Programs Through Quantitative XRD: A Case Study from the Gumsberg Polymetallic Sulphide Deposits, Sweden

As challenges in precious and base metal exploration intensify due to the diminishing availability of high-grade ore deposits, rising demand, energy costs, and stricter regulations towards net-zero carbon activities, advanced techniques to enhance exploration efficiency are becoming increasingly critical. This study demonstrates the effectiveness of quantitative X-ray diffraction (QXRD) with Rietveld refinement, coupled with multivariate statistical analysis (including agglomerative hierarchical clustering, principal component analysis, and fuzzy analysis), in characterizing the complex mineralogy of strata-bound volcanic-associated limestone-skarn Zn-Pb-Ag-(Cu-Au)-type sulphide deposits (SVALS). Focusing on 113 coarse rejects from the Gumsberg project located in the Bergslagen mining district in central Sweden, the research identified five distinct mineralogical clusters corresponding to polymetallic base metal sulphide mineralization, its proximal alteration zones, and variably metamorphosed host rocks. The results reveal significant sulphide mineralization, ranging from disseminated to massive occurrences of sphalerite, pyrrhotite, pyrite, and galena, with trace amounts of secondary minerals like anglesite in certain samples indicating weathering processes. The study also identifies rare minerals such as armenite, often overlooked in traditional geological logging. These findings underscore the potential of QXRD to enhance resource estimation, optimize exploration strategies, and contribute to more efficient and sustainable mineral exploration programs. The accuracy of QXRD was cross-validated with geological logs and geochemical data, confirming its reliability as a mineralogical discrimination tool.

Non-destructive and rapid identification of yeasts by nearinfrared spectroscopy and machine learning

This study aimed to apply near-infrared (NIR) spectroscopy combined with machine learning techniques to identify yeast strains rapidly and practically, comparing the results with traditional molecular identification methods. Yeasts were isolated from the digestive tracts of aquatic mining insects collected in the extreme north of the Western Amazon (Roraima), Brazil, and preserved through cryopreservation and mineral oil methods. Molecular identification involved PCR amplification and sequencing of ribosomal DNA regions. NIR spectroscopy, coupled with multivariate analysis and machine learning algorithms such as principal component analysis (PCA), hierarchical cluster analysis (HCA), k-nearest neighbor (KNN), and soft independent modeling by class analogy (SIMCA), was used to analyze and classify the yeast samples, accurately identified yeast strains at the genus and species levels, achieving 100% accuracy in both the calibration and validation sets. The results indicate that this method provides a rapid, non-destructive, and environmentally friendly alternative to traditional molecular techniques, making it suitable for real-time, in situ analysis with minimal sample manipulation.

Isolation, Characterization, and Anti-Inflammatory Effects of β-Sitosterol-β-D-Glucoside from Hygrophila auriculata: Experimental Validation, Molecular Docking, and Molecular Dynamics Simulations.

Hygrophila auriculata (K. Schum) Heine is known to treat various common aliment e.g. rheumatoid arthritis, kidney infections, jaundice, edema, and gout. This study aims to isolate bioactive components from the methanolic extract, assess their anti-inflammatory effects, and investigate their interactions with drug targets through docking and molecular dynamics simulations. Methanolic extract of H. auriculata furnished stigmast-5-en-3-ol-β-D-glucopyranoside (HA-06) which was characterized by using IR, NMR and mass spectral data. HA-06 alleviated carrageenan-induced inflammation in rats, while the methanolic extract of H. auriculata produced comparable results. The findings were similar to those of the positive control, indomethacin. The chemical structure of HA-06 was optimized using DFT at the B3LYP level and subsequently used for molecular docking against anti-inflammatory drug targets. HA-06 exhibited strong affinity towards phospholipase A2 and glucocorticoid receptor exhibiting binding energies of -11.25 kcal/mol and -11.07 kcal/mol, respectively.Molecular dynamics simulation was used to assess the dynamical stability of these two complexes and their native co-crystallized ligands. Principal component analysis, radius of gyration, free energy landscapes, solvent-accessible-surface-area, and root-mean square deviation/fluctuation all indicated stable interactions. Therefore, HA-06 could be a promising candidate for development into an effective therapy against inflammatory diseases targeting phospholipase A2 and glucocorticoid receptor.

Biofilms in modern CaCO3-supersaturated freshwater environments reveal viral proxies

Biofilms are mucilaginous-organic layers produced by microbial activity including viruses. Growing biofilms form microbial mats which enhance sediment stability by binding particles with extracellular polymeric substances and promoting growth through nutrient cycling and organic matter accumulation. They preferentially develop at the sediment-water interface of both marine and non-marine environments, and upon the growing surfaces of modern tufa and travertine. In this context, however, little is known about the factors, environmental or anthropogenic, which affect viral communities in freshwater spring settings. To explore this issue, geochemical and metagenomic data were subjected to multidimensional analyses (Principal Component Analysis, Classical Multidimensional Scaling, Partial Least Squares analysis and cluster analysis based on beta-diversity), and these show that viral composition is specific and dependent on environment. Indeed, waters precipitating tufa and travertine do vary in their geochemistry with their viruses showing distinct variability between sites. These differences between virus groups allow the formulation of a viral proxy, based on the Caudoviricetes/Megaviricetes ratio established on the most abundant groups of viruses. This ratio may be potentially used in analysing ancient DNA preserved in carbonate formations as an additional source of information on the microbiological community during sedimentation.