Characterization of confined and unconfined conditions in a dual-aquifer system using physical and chemical indicators: a case study of the Nakdong River delta region, Korea

Principal component analysis and hierarchical cluster analysis were applied to investigate physicochemical and instrumental textural properties of fresh kashar cheese. Four different principal components sufficiently explained the variability in the cheese samples. In addition, hierarchical cluster analysis was performed to group the kashar cheese samples regarding physicochemical and instrumental textural properties. Instrumental textural properties indicated greater variability than chemical composition of cheese samples. Principal component analysis revealed that color parameters were positively correlated with textural and chemical parameters. The results of this study revealed that other parameters rather than chemical composition would be effective on the instrumental textural properties. It was proved that principal component analysis was a very effective statistical tool to determine quality of cheese samples. According to the principal component analysis and hierarchical cluster analysis results, the attributes defining the kashar cheese samples were determined to be primarily the texture profile analysis parameters.

A chemometric approach coupled with capillary electrophoresis based on the hierarchical cluster analysis and principal component analysis has been applied for the investigation of the water quality in the Golcuk-Isparta region (Lake District of Turkey). In the research area, Egirdir Lake, Golcuk Lake and surrounding ground and domestic waters have been utilized as drinking water resources. Golcuk Lake is distinctive in terms of high fluoride content (3.50 ± 0.21 mg/mL) which is endemic in volcanic areas where the water flow through volcanic rocks and sediments. Based on the analysis of major anions chloride, sulfate, nitrate and fluoride with capillary electrophoresis, twenty-four drinking water sampling sites in the research area were classified into four classes using the hierarchical cluster and principal component analysis. Combining the research area investigation results of hierarchical cluster and principal component analysis, it was found that fluoride concentration is the major diagnostic variable to determine the quality of drinking waters, and all the other anions are the important classification factors to predict the resources of the drinking water samples, individually. To sum up, this study reveals the potential of the use of capillary electrophoresis in combination with chemometric techniques for the determination of the quality and origin of drinking waters.

Hepatic Gene Expression Discriminates Responders and Nonresponders in Treatment of Chronic Hepatitis C Viral Infection

We investigated early to middle Holocene benthic foraminifera from four borehole cores in the Nakdong River Delta (southeast Korea) to document faunal associations and the transition of benthic foraminifera in coastal areas along the Tsushima Warm Current. We recognized four varimax factor assemblages. The varimax factor 1 assemblage (characterized by Pseudoparrella naraensis with Eilohedra nipponica) is common throughout core ND-02, which is seaward in the delta, whereas the varimax factor 2 assemblage (characterized by Haynesina sp. A) is dominated by low evenness in core 16ND-C02, which is landward in the delta. The varimax factor 4 assemblage (characterized by Buccella frigida) is generally common at the bottom and/or top part of the studied cores, whereas the varimax factor 3 assemblage (characterized by Elphidium somaense) tends to be common in the upper part of the three cores in the delta's seaward area. Both the contrasting high diversity and low diversity with low evenness of benthic foraminifera (varimax factor 1 and 2 assemblages, respectively) were present between the seaward and landward portions of the delta during the same period (∼7–6 ka), respectively. The combination of these contrasting faunas tended to appear in the delta with the intensification of the Tsushima Warm Current during ∼8–6 ka in addition to the sea-level rise. Common taxa in the Nakdong River Delta are largely neritic species of the temperate region in the East Asian margin, whereas some upper bathyal species, such as Angulogerina ikebei, Bolivina decussata, and E. nipponica, were subordinated in the delta's seaward portion. Such faunal features in the Nakdong River Delta are distinguishable from other coastal areas in the Japanese Islands.

Holocene benthic foraminiferal faunas in coastal deposits of the Nakdong River delta (Korea) and Izumo Plain (Japan)

Benthic foraminifera in the Nakdong River Delta (southeast Korea) and their response to middle Holocene climatic change in the coastal environment of the East Asian margin

Rheumatoid arthritis (RA) is one of the most prevalent chronic inflammatory and angiogenic diseases. The aim of this study was to evaluate the anti-inflammatory and anti-angiogenic activities in vitro of eight diterpenoids isolated from Daphne genkwa. LC-MS was used to identify diterpenes isolated from D. genkwa. The anti-inflammatory and anti-angiogenic activities of eight diterpenoids were evaluated on LPS-induced macrophage RAW264.7 cells and TNF-α-stimulated human umbilical vein endothelial cells (HUVECs) using hierarchical cluster analysis (HCA) and principal component analysis (PCA). The eight diterpenes isolated from D. genkwa were identified as yuanhuaphnin, isoyuanhuacine, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuagine, isoyuanhuadine, yuanhuadine, yuanhuaoate C and yuanhuacine. All the eight diterpenes significantly down-regulated the excessive secretion of TNF-α, IL-6, IL-1β and NO in LPS-induced RAW264.7 macrophages. However, only 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl markedly reduced production of VEGF, MMP-3, ICAM and VCAM in TNF-α-stimulated HUVECs. HCA obtained 4 clusters, containing 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, isoyuanhuacine, isoyuanhuadine and five other compounds. PCA showed that the ranking of diterpenes sorted by efficacy from highest to lowest was 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuaphnin, isoyuanhuacine, yuanhuacine, yuanhuaoate C, yuanhuagine, isoyuanhuadine, yuanhuadine. In conclusion, eight diterpenes isolated from D. genkwa showed different levels of activity in LPS-induced RAW264.7 cells and TNF-α-stimulated HUVECs. The comprehensive evaluation of activity by HCA and PCA indicated that of the eight diterpenes, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl was the best, and can be developed as a new drug for RA therapy.

Bioactivity herbal plant is influenced by its active compounds and consistency. Stevia rebaudiana contains bioactive compounds, diterpene glycosides which have antidiabetes activity. The goal of this study was to develop fingerprints analysis of S. rebaudiana based on High Performance Liquid Chromatography (HPLC) chromatogram. S. rebaudina leaves were taken from different planting area, leave ages, and seeds source. S. rebaudiana leaves were analyzed using isocratic Reversed Phase High Performance Liquid Chromatography (RP-HPLC). Fingerprints analysis of S. rebaudiana was done using chemometrics of Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). Peak marker “common peak” were identify using Cluster Observation in HCA analysis at each peaks retention time formed in chromatogram. Retention times giving similarity value more than 0,90 were identified as “common peak”. HCA analysis resulted in 5 “common peak” identification as peaks marker particularly at peak no 1, 2, 4, 6 and 7. HCA analysis was also clustered samples into 3 main cluster. PCA analysis was optimized by calculated peak area whose N > 2000 particularly peak no 4, 6, and 7. PCA analysis result can be used to classify chromatogram based on original seeds, planting area and leave ages. Fingerprints analysis developed can be used an alternative method for quality control of S. rebaudiana herbal plants based on its bioactive compounds systemic characteristics.

Density functional theory (DFT) was used to calculate the properties of a set of molecular descriptors for 14 fluoroquinolone with anti-Pseudomonas aeruginosa activity. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed in order to reduce dimensionality and investigate the effectiveness of variables, i. e., which subset of variables should be more effective for classifying fluoroquinolones according to their antibacterial activities against P. aeruginosa. The PCA results showed that the variables E LUMO, ΔE HL, Q 5, Q 6, logP, MR, and MP are responsible for the separation between compounds with higher and lower anti-P. aeruginosa activity. The HCA results were similar to those obtained using PCA. By using the chemometric results, four synthetic compounds were analyzed through the PCA and HCA. Two of them are proposed as active molecules against P. aeruginosa. The result is consistent with the observations of clinic experiments. The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with anti-P. aeruginosa activity.

Density functional theory (DFT) was used to calculate molecular descriptors (properties) for 12 fluoroquinolone with anti-S.pneumoniae activity. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce dimensionality and investigate in which variables should be more effective for classifying fluoroquinolones according to their degree of an-S.pneumoniae activity. The PCA results showed that the variables ELUMO, Q3, Q5, QA, logP, MR, VOL and EHL of these compounds were responsible for the anti-S.pneumoniae activity. The HCA results were similar to those obtained with PCA. The methodologies of PCA and HCA provide a reliable rule for classifying new fluoroquinolones with anti-S.pneumoniae activity. By using the chemometric results, 6 synthetic compounds were analyzed through the PCA and HCA and two of them are proposed as active molecules with anti-S.pneumoniae, which is consistent with the results of clinic experiments.

The conservation of heritage buildings requires non-invasive tools that can predict material performance while maintaining historical integrity and structural safety. This study introduces a multivariate statistical framework that integrates regression analysis, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA) to classify seven traditional materials adobe, lime mortar, limestone, sandstone, marble, volcanic stone, and wood based on their mechanical, thermal, and moisture-related properties. This study aims to develop a validated multivariate framework for classifying traditional heritage materials based on their mechanical, thermal, and moisture-related properties to support sustainable restoration and retrofit design for classifying traditional materials based on their mechanical, thermal, and moisture-related properties to support sustainable restoration and retrofit design. Unlike prior research limited to single-material assessments, this study standardizes and analyzes data from fourteen peer-reviewed sources using regression models, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA), complemented by pilot non-destructive validation tests on lime mortar, sandstone, limestone, and marble. The framework compiles and standardizes datasets from fourteen peer-reviewed sources into a unified predictive model. The framework was validated through pilot testing using non-invasive methods (density, ultrasonic pulse velocity, rebound hardness), which confirmed the statistical predictions of robustness versus moisture vulnerability. Advanced cluster solutions identified conservation-relevant subgroups, enabling engineers to distinguish between moisture-sensitive low-density materials and durable lithic stones, with direct implications for sustainable restoration and retrofit practices. The originality of this study lies in transforming fragmented datasets into a validated, decision-support tool that can be embedded into Historic Building Information Modeling (HBIM) platforms for predictive diagnostics, compatibility assessment, and energy-efficient retrofit planning in heritage structures. This study provides the first validated cross-material statistical framework linking traditional conservation materials with predictive digital-modeling tools. This framework further demonstrates that the application of regression, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA) enables quantitative prediction of material performance through non-destructive parameters. The integration of these techniques provides interpretive value beyond descriptive classification, facilitating preventive diagnostics, compatibility assessments, and energy-oriented retrofit planning within HBIM systems.

Multivariate analysis of fresh-cut carambola slices stored under different temperatures

It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed.Main points Microglia undergo a quantifiable morphological change upon neuraminidase induced inflammation.Hierarchical cluster and principal components analysis allow morphological classification of microglia.Brain location of microglia is a relevant factor.

The study of spatial variability of soil and plants attributes, or precision agriculture, a technique that aims the rational use of natural resources, is expanding commercially in Brazil. Nevertheless, there is a lack of mathematical analysis that supports the correlation of these independent variables and their interactions with the productivity, identifying scientific standards technologically applicable. The aim of this study was to identify patterns of soil variability according to the eleven physical and seven chemical indicators in an agricultural area. It was used two multivariate techniques: the hierarchical cluster analysis (HCA) and the principal component analysis (PCA). According to the HCA, the area was divided into five management zones: zone 1 with 2.87ha, zone 2 with 0.8ha, zone 3 with 1.84ha, zone 4 with 1.33ha and zone 5 with 2.76ha. By the PCA, it was identified the most important variables within each zone: V% for the zone 1, CTC in the zone 2, levels of H+Al in the zone 4 and sand content and altitude in the zone 5. The zone 3 was classified as an intermediate zone with characteristics of all others. According to the results it is concluded that it is possible to separate into groups (management zones) samples with the same patterns of variability by the multivariate statistical techniques.

The aim of this work was to determine which parameters are sufficient to measure in order to describe the water quality of a pit lake and to identify patterns in the data among different kind of pit lakes. The data consisted of ambient dose equivalent rate, elemental and radionuclide concentration, pH, and specific conductance in surface water and sediment samples collected from different types of mines. Data were tested for normality and log-normality and used in principal component analysis (PCA) and hierarchical cluster analysis (HCA). The normality tests indicated that only 40K was normally distributed, while only the 234,238U isotopes were log-normally distributed. HCA performed on parameters measured in surface water provided clusters that in most cases separated the elements according to their chemical groups. However, when HCA was performed on pit lakes, the clustering seemed to indicate that surface water might not be the preferred sample to differentiate between different types of pit lakes. PCA of surface water data resulted in three components that explained 72% of the variance when pH, SC, concentration of the elements Mg, K, Ca, Cu, Zn, Sr, Pb, activity concentration of 234,238U and 210Po, and ambient dose equivalent rate were included. For surface sediment data, the PCA resulted in three components explaining 83% of the variance when the concentration of Na, Mg, Al, P, K, Ca, Rb, Sr, Y, Tl, activity concentration of 234Th, 226Ra, 210Pb, 232Th (series average), and 40K, and ambient dose equivalent rate were included.