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The inter-ionic relationships of groundwater present in a region as well as various chemical and physical factors all have an impact on the geochemistry of groundwater in an aquifer. To assess the factors influencing the geochemical composition of groundwater in the eco-sensitive area of Western Ghats, Kerala, India, various weathering, and ionic indices were analyzed. Results show groundwater ranges from soft to extremely hard and acidic to alkaline, with high Mn and Fe levels. WQI analysis found 7% of samples unfit for drinking due to Fe/Mn contamination in the southeast part of the study area. Main water types are CaHCO 3 (46%) and CaMgCl. Geochemical modeling indicates silicate and carbonate weathering, cation exchange, and reverse ion processes influence the aquifer. Groundwater is often supersaturated with iron minerals, saturated with carbonate minerals, and undersaturated with sulfate and chloride minerals. Cluster analysis (CA) revealed that NO 3 − and K + are derived from anthropogenic sources. Principal component analysis (PCA) resulted in three factors. Factor 1 is for geogenic processes, while Factors 2 and 3 imply the anthropogenic and weathering of iron-rich minerals. Hierarchical cluster analysis defines anthropogenic input, silicate and carbonate weathering, and different patterns of mineralization in the groundwater. The study underscores the need for comprehensive management to protect groundwater quality, considering the complex interplay of natural processes and human factors.

Dam failures pose catastrophic risks to human life and property, necessitating robust safety monitoring systems for risk mitigation. However, the specific contributions of distinct monitoring modalities to dam safety remain inadequately characterized, particularly regarding their differential impacts on structural integrity assessment. This study investigates the correlation between diverse monitoring modalities and dam structural safety through a comprehensive analysis of the Silin Hydropower Station dam. We analyzed 324 datasets collected from nine types of monitoring sensors installed across 36 dam cross-sections. Statistical analyses including one-way ANOVA, cluster analysis, and principal component analysis (PCA) were employed to quantify the influence patterns of monitoring parameters. The safety impact levels of all 36 cross-sections were systematically ranked, establishing a prioritized reference framework to inform decision-making in dam safety management. Unlike conventional dam safety assessments that predominantly rely on subjective empirical judgments, this study introduces an objective methodology integrating principal component analysis (PCA) of heterogeneous monitoring data across multiple dam cross-sections. The analytical outcomes were systematically quantified, hierarchically ranked, and visualized through multidimensional mapping techniques. The results demonstrated that variations in fissure (X2), horizontal displacement (X3), tilt (X4), stress (X6), soil-displacement (X8), and denotes water-level (X9) exerted highly significant effects on dam safety ( p  < 0.001). The first two principal components cumulatively accounted for 74.1876% of the total variance, with eigenvalues reaching 6.6769. In the comprehensive evaluation, cross-section T4 (T4) obtained the maximum score (0.8500), while cross-section T35 (T35) showed the minimum score (0.0175). In conclusion, the analysis revealed that X9, X8, X2, X3, and X4 exerted significant impacts on dam safety, while cross-section T4 achieved the highest comprehensive evaluation score. This approach employs Principal Component Analysis (PCA) with integrated scoring to reduce multivariate dimensionality, enabling rapid identification of key monitoring sections critical to dam safety, and demonstrates broad applicability for dam safety monitoring.

This study evaluated the genetic variability, explored trait interrelationships, and identified efficient physiological quality traits of soybean seeds using multivariate techniques. Fourteen soybean varieties were evaluated based on twelve physiological quality traits related to germination and vigor tests. The data for each trait was analyzed using genetic variability estimation, analysis of variance, Tukey post-hoc test, and multivariate analysis. The findings indicated that most traits exhibited high genetic variability and high heritability, particularly in TSW (CVg=22.72%, h_x^2 =99.79) and SDM (CVg=26.59%, h_x^2 =94.98). There were no significant differences in SEF and SDMF among the fourteen varieties. Correlation analysis revealed significant positive relationships between TSW and SDM (r=0.95, p<0.05), as well as between G and TTZ (r=0.75, p<0.05). In contrast, TSW and SDM showed negative correlations with GSI and SEF. PCA analysis revealed two PCs explained 68.54% of the total variance. The PCA biplot indicated that G, FGC, GSI, and SL were the most efficient traits for assessing the physiological quality of soybean seeds. Heatmap clustering further revealed that six varieties (Gepak Kuning, Demas 1, Devon 1, Deja 1, Dering 1, and Dering 3) grouped in Cluster II were the most genetically divergent among those evaluated.

Background A primary threat to food security stems from the expanding global population and climate change, which have increased the frequency of droughts. Owing to shifting climatic conditions, abiotic stresses such as severe drought are intensifying, reducing wheat productivity. This study aimed to evaluate the response of elite drought-tolerant wheat genotypes to water deficit stress by analysing agronomic and physio-biochemical traits, with the goal of identifying promising genotypes for breeding. Methods Twenty wheat genotypes sourced from various national and international drought-tolerant nurseries, including a benchmark variety, were tested under water deficit and well-watered conditions over two consecutive years. The data collected included agronomic traits such as plant height (PH), days to heading (DH), days to anthesis (DA), days to physiological maturity (DPM), canopy temperature, SPAD values at different growth stages, intercepted photosynthetically active radiation above the canopy (IPARAC) and on the ground (IPAR OG), yield stability index (YSI), stress tolerance index (STI), stress index (SI), leaf area index (LAI), spike length (SL), grains per spike (GPS), 1000-grain weight (TSW), grain yield (GY; t/ha), and biomass yield (BY; t/ha). Results To streamline the study, two years of aggregated data were analysed for each parameter. Drought tolerance was assessed based on grain yield, and multitrait genotype‒ideotype distance (MGIDI) indices were employed to select drought-tolerant wheat genotypes. Significant differences were observed among the wheat genotypes across all measured parameters under both conditions. Under normal conditions, correlation analysis revealed that grain yield (GY) and biomass yield (BY) had the strongest positive relationship ( r  = 0.75**), followed by TSW, LAI, GPS, SL, PH, DPM, and DA. In contrast, under water deficit stress, BY exhibited a notable correlation with plant height (PH) ( r  = 0.42). Under both irrigated and water deficit stress situations, GY had positive and substantial correlations with PH, DA, DPM, GPS, SL, the STI, and the YSI. Two of the ten main components (PCs) accounted for 52.3% and 50.4% of the overall variation under water deficit and well-watered conditions, respectively. Additionally, the genotypes were separated into three clusters via a cluster heatmap, and the most tolerant genotypes (E38, E40, E41, E35, and E33) were found to be in cluster 3, which revealed their genetic relatedness. Genotypes E9 and E29 were found to be sensitive to water deficit, whereas genotypes E40, E38, and E35 were drought tolerant, according to tolerance indices. Conclusion Plant breeders may find the MGIDI useful for selecting genotypes on the basis of a variety of characteristics because it is a straightforward and robust selection method. Among the 20 wheat genotypes, the most stable and productive were E38, E30, E35, E40, and E34, according to an analysis of MGIDI for diverse settings. This was likely caused by the high MPS (mean performance and stability) of specific traits under different situations. The features that have been identified can be used as genitors in hybridization procedures to create wheat breeding materials that are resistant to drought. The genotypes and features that were found to be drought tolerant could be used to create new genotypes that are resistant to drought stress.

Organic farming and the introduction of underutilized crops such as buckwheat (Fagopyrum esculentum Moench) play an important role in enhancing agricultural biodiversity and promoting sustainable farming practices. This study examines the variability and stability of 11 European buckwheat genotypes at two different elevations (610 and 830 m) under organic farming principles over two growing seasons. Genotype, elevation, and year factors significantly contributed to the phenotypic variation of traits. Environmental factors had the greatest impact on the variation in biomass yield (32.6%) and grain yield (28.4%), while the least influence was observed on the variation in thousand grain weight (6.3%). Higher plant height (120.11 cm) and biomass yield (14.2 t [ha.sup.-1]) were recorded at the lower elevation (610 m), while higher grain yield (662.2 kg [ha.sup.-1]) and thousand grain weight (22.9 g) were observed at the higher elevation (830 m). In 2015, plant height, biomass yield, thousand grain weight, and grain yield were 3.87%, 14.87%, 2.70%, and 15.95% higher, respectively, compared to 2016. Excessive rainfall in 2016 led to plant lodging, negatively affecting vegetative growth and yield. The additive main effect multiplicative interaction (AMMI) analysis showed that genotypes 'La Harpe' and 'Heljda 2' were stable and high-yielding across different environments, while 'Novosadska', 'Darja' and 'Prekumurska' exhibited positive interactions with the higher elevation environment (830 m) during the dry 2015 season. Heatmap cluster analysis indicated that genotypes 'Darja' and 'La Harpe' demonstrated broad adaptability for the analyzed traits, while 'Heljda 1' and 'Heljda 2' were particularly suited to high-rainfall conditions, achieving high grain yield or grain quality.

To determine the differences in the characteristic volatile compounds between winemaking areas in the Xinjiang region, this study was conducted by sampling Cabernet Sauvignon grapes from four winemaking areas in Xinjiang, named Tianshanbeilu, Yili, Yanqi, and Hami. After undergoing the same alcoholic fermentation treatment, the wines from the four areas were subjected to GC–MS and sensory analysis. The results showed that fifty aromatic compounds (including higher alcohols, esters, acids, terpenes, aldehydes/ketones, et al.) were identified and quantified. Interestingly, the terpene and phenylalanine derivative contents of the wines from northern Xinjiang were higher than those from the south. Additionally, four vineyards highly contributed to the development of key volatile compounds in the Xinjiang region. Sensory analysis showed that the wines from northern Xinjiang were impressive with a flowery and fruity aroma and the wines from southern Xinjiang had a stronger wine body and astringency.

A study using a (4 × 4) F 1 full diallel population of four parents: BARI Gom‐25 (P1), BARI Gom‐26 (P2), BARI Gom‐33 (P3), and Pavan (P4) was conducted following randomized complete block design with three replications under optimum and late sowing conditions to induce terminal heat stress to assess wheat ( Triticum aestivum L.) genotypes for heat tolerance and yield stability under normal and terminal heat‐stress conditions. Ten traits were evaluated for combining ability and heterosis analysis. Significant genetic variation was found among the genotypes for all traits. Combining ability analysis showed significant variations in both general and specific combining abilities. P1 and P4 under normal condition and P1 and P3 under terminal heat stress had the best general combining ability, while P1 × P2, P2 × P1, P3 × P4 under normal condition and P1 × P4, P2 × P3, P4 × P3 under terminal heat stress showed the best specific combining ability effects for yield per plant. The highest significant heterosis for yield per plant was exhibited by P1 × P2, P3 × P2, P3 × P4, P4 × P1, suggesting these combinations can be exploited for developing high‐yielding, heat‐tolerant wheat varieties. Based on heat tolerance indices, P1 × P3 and P1 × P2 were the most heat‐tolerant genotypes. Overall, the study highlights P1 and P3 as the most promising parents for use as genomic sources in developing heat stress–tolerant genotypes, while the combinations P1 × P3 and P1 × P2 emerged as the best crosses, offering both heat tolerance and stable productivity.

Background Hierarchical clustering is an exploratory data analysis method that reveals the groups (clusters) of similar objects. The result of the hierarchical clustering is a tree structure called dendrogram that shows the arrangement of individual clusters. To investigate the row/column hierarchical cluster structure of a data matrix, a visualization tool called ‘cluster heatmap’ is commonly employed. In the cluster heatmap, the data matrix is displayed as a heatmap, a 2-dimensional array in which the colour of each element corresponds to its value. The rows/columns of the matrix are ordered such that similar rows/columns are near each other. The ordering is given by the dendrogram which is displayed on the side of the heatmap. Results We developed InCHlib (Interactive Cluster Heatmap Library), a highly interactive and lightweight JavaScript library for cluster heatmap visualization and exploration. InCHlib enables the user to select individual or clustered heatmap rows, to zoom in and out of clusters or to flexibly modify heatmap appearance. The cluster heatmap can be augmented with additional metadata displayed in a different colour scale. In addition, to further enhance the visualization, the cluster heatmap can be interconnected with external data sources or analysis tools. Data clustering and the preparation of the input file for InCHlib is facilitated by the Python utility script inchlib_clust . Conclusions The cluster heatmap is one of the most popular visualizations of large chemical and biomedical data sets originating, e.g., in high-throughput screening, genomics or transcriptomics experiments. The presented JavaScript library InCHlib is a client-side solution for cluster heatmap exploration. InCHlib can be easily deployed into any modern web application and configured to cooperate with external tools and data sources. Though InCHlib is primarily intended for the analysis of chemical or biological data, it is a versatile tool which application domain is not limited to the life sciences only.

The purpose of this research is to have a clearer understanding of the resource value of electrolytic Zn anode slime, explore its fine structure and treat it in a classified manner. In this paper, the composition, content and distribution of Pb, Mn and Zn in electrolytic Zn anode slime during one production cycle were studied under typical working conditions of enterprises, by using the method of μ-XRF combined with mm-XRF. Based on heatmap and cluster analysis, the resource value of anode slime at different points in the electrolytic Zn silting area and the conventional area was evaluated. The research results were as follows: (1) There were two different areas of electrolytic Zn anode slime: the silting area and conventional area. Between these, the silting area accounted for approximately 15% of the total number of anode plates, and there were significant differences between these areas in terms of surface element content, surface morphology, slime thickness and water content. (2) In the silting area, 3.9 mm away from the Pb-based anode plate surface, there were lumps with fluorescence counting intensity that was close to that of pure Pb. (3) Anode slime at a thickness of 0.1 mm in the silting area could be classified as a type of resource. The resource attributes were categorized using a complete-linkage algorithm and the actual demand of Zn hydrometallurgy enterprises for recycling anode slime from the leaching process, for those with ≥45% Mn content and <20% Pb content. High-Pb resources include 0–1.5 mm (CAP), 0–1.4 mm (SAP) and 6.3–7.0 mm (SAP). A low-Pb, high-Mn and high-Zn resource was identified at 2.9–6.0 mm (SAP). Low-Pb, high-Mn and low-Zn resources include 1.6–6.0 (CAP), 1.5–2.8 mm (SAP) and 6.1–6.2 mm (SAP).

Multiway chromatin interactions are essential for precise transcriptional regulation. Split-Pool Recognition of Interactions by Tag Extension (SPRITE) captures these interactions, which are typically visualized as pairwise heatmaps, where each bin represents one or more multiway contacts. Accurate representation requires downweighting and additional processing to avoid overrepresentation of pairwise signals. However, existing tools such as Juicebox lack the ability to adjust these parameters, leading to biased visualizations. To address this limitation, we introduce MultiVis, a user-friendly, interactive tool for precise and unbiased three-dimensional (3D) genome visualization. MultiVis also generates gene names without requiring external annotation files and allows users to select regions of interest to retrieve corresponding cluster information, thereby removing technical barriers for wet-lab biologists. By enabling real-time analysis, MultiVis accelerates genomics research and advances the study of 3D genome architecture and gene regulation.