Evaluation of hydro-chemical facies and surface water quality dynamics using multivariate statistical approaches in Southern Nigeria by E. A Ubuoh, F. U Nwogu, E. Q Ossai-Abeh, J. C Ikwuemesi, A. O Oke, J. D Umoh

“…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. …”
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Adjunctive diagnostic tool for histopathological classification of congenital mesoblastic nephroma based in molecular genetic findings by Hiroshi Hamada, Kenichi Kohashi, Takeshi Iwasaki, Mikiko Hashisako, Yuko Hino, Masahiro Fukuhara, Amane Kamouchi, Naonori Kawakubo, Tatsuro Tajiri, Yoshinao Oda

“…Comprehensive mRNA analysis was performed using the nCounter® Gene Expression Assay. Principal component analysis (PCA) was performed based on the gene expression levels. …”
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Nature's defense against emerging neurodegenerative threats: Dynamic simulation, PCA, DCCM identified potential plant-based antiviral lead targeting borna disease virus nucleoprote... by Noimul Hasan Siddiquee, Md Ifteker Hossain, Farhana Mansoor Priya, Sakia Binte Azam, Md Enamul Kabir Talukder, Durjoy Barua, Salina Malek, Niloy Saha, Sidratul Muntaha, Ridoy Paul, Israt Jahan Ritu, Farjana Islam Tuly, Abir Hossain

“…MD simulations found that the protein binding site is highly stable when complexed with CID 20871246 and has a higher negative binding free energy value, indicating a strong interaction between the compound and the protein. Principal component analysis (PCA) identified three main components (PC1, PC2, and PC3) that accounted for 53.43%, 12.31%, and 5.97% of the variance, respectively. …”
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Evaluation of the Effectiveness of Soil Improvement Measures in Apple Orchards on the Loess Plateau: A Meta-Analysis by DUAN Shunxia, LIU Jiayuan, ZHAO Hongxiang, LI Yangyang

“…[Methods] Based on 75 publications and 1 302 field trial datasets on soil improvement in the apple orchards of the Loess Plateau, meta-analysis and principal component analysis (PCA) were used to quantitatively evaluate the effects of different improvement measures, including mulching, fertilization, and soil conditioners, on soil quality. …”
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A Study on the Infrageneric Classification of <i>Hordeum</i> Using Multiple Methods: Based on Morphological Data by Nayoung Ro, Pilmo Sung, Mesfin Haile, Hyemyeong Yoon, Dong-Su Yu, Ho-Cheol Ko, Gyu-Taek Cho, Hee-Jong Woo, Nam-Jin Chung

“…This study addresses these limitations by employing an integrative approach combining hierarchical clustering, Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), and Random Forest (RF) to analyze the compiled morphological datasets. …”
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A Comprehensive Evaluation of Nutritional Quality and Antioxidant Capacity of Different Chinese Eggplant Varieties Based on Multivariate Statistical Analysis by Jian Lyu, Ning Jin, Xianglan Ma, Xueyun Yin, Li Jin, Shuya Wang, Xuemei Xiao, Jihua Yu

“…A classification model with principal component analysis classified 30 Chinese eggplant varieties into two categories: high and low antioxidant capacities. …”
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Soil fertility in agricultural production units of tropical areas by S. Rodelo-Torrente, A.C. Torregroza-Espinosa, M. Moreno Pallares, D. Pinto Osorio, A. Corrales Paternina, A. Echeverría-González

“…Correlation analysis and principal component analysis were performed on the resulting data set, and a soil quality index was calculated.FINDING: Macronutrients N, P, K, S, Ca, Mg, and Na displayed average values of 21.65 ± 10.65 part per million, 40.35 ± 67.21 part per million, 0.46 ± 0.43 meq/100g, 7.94 ± 28.35 part per million, 15.63 ± 17.30 meq/100 g, 5.63 ± 3.58 meq/100g, 0.19 ± 0.20 meq/100g, respectively. …”
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Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models by Khabat Khosravi, Aitazaz A. Farooque, Amir Naghibi, Salim Heddam, Ahmad Sharafati, Javad Hatamiafkoueieh, Soroush Abolfathi

“…Eight input scenarios were developed using both manual and automated feature selection techniques, including correlation-based subset selection evaluation (CfsSubsetEval or CSE), Principal Component Analysis (PCA), and the Relief Attribute Evaluator (RAE). …”
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Biofilmed-PGPR: Next-Generation Bioinoculant for Plant Growth Promotion in Rice under Changing Climate by Jeberlin Prabina Bright, Hemant S. Maheshwari, Sugitha Thangappan, Kahkashan Perveen, Najat A. Bukhari, Debasis Mitra, Riyaz Sayyed, Andrea Mastinu

“…Therefore, a pot culture experiment with the CBB formulations was carried out, and different growth and yield parameters were recorded. Principal component analysis showed that plant growth, yield, soil dehydrogenase activity, and soil chlorophyll content were positively correlated with rice plants amended with vermiculite-based CBB at 2 kg/hm2 followed by a spray with aqueous CBB formulation at 5 mL/L at 15 and 30 d after rice transplanting grown with a 25% reduced level of nitrogen/phosphorus/potassium chemical fertilizers than the recommended dose. …”
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Effects of biochar on the chemical properties of soils and the volume of wood in a plantation of Acacia mangium Willd in the Colombian Orinoquía (highlands) by Giovanni Reyes-Moreno, Aquiles Enrique Darghan, Carlos Rivera-Moreno

“…We used a Bayesian principal component analysis to reduce dimensionality, and the two extracted dimensions were labeled by treatment to visualize their grouping. …”
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Population structure and genetic diversity of Toona sinensis revealed by whole-genome resequencing by Lei Wang, Chang Lu, Zhi-Gang Bao, Meng Li, Fusheng Wu, Yi-Zeng Lu, Bo-Qiang Tong, Mei Yu, Yong-Jun Zhao

“…Population structure analyses were then conducted using the identified SNPs, including principal component analysis (PCA), structure analysis, and phylogenetic tree construction. …”
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The Level of Heavy Metal Contamination in Selected Vegetables and Animal Feed Grasses Grown in Wastewater Irrigated Area, around Asmara, Eritrea by Goitom Kfle, Ghebray Asgedom, Tedros Goje, Felema Abbebe, Lula Habtom, Hagos Hanes

“…Multivariate analysis, such as principal component analysis (PCA) and cluster analysis (CA), was performed on the distribution of elements in plant species to identify the source of the heavy metals. …”
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Adult dietary patterns and their association with iodine nutrition levels and thyroid function: a cross-sectional study by Gulinaizeer Abuduwaili, Jia Huang, Yan Ma, Hongguang Sun

“…Dietary patterns were extracted by principal component analysis, and the relationship between dietary patterns and iodine nutrition levels and thyroid function was explored. …”
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Associations between health, productive performance and oral fluid biomarkers in commercial pig farms by Mario Andre S. Ornelas, Edgar Garcia Manzanilla, José J. Cerón, Alba Ortín-Bustillo, María José López-Martínez, Carla Correia-Gomes, Finola C. Leonard, Lorena Franco-Martínez

“…Results Using principal component analysis, three clusters of farms were identified, differing mostly in weaner and finisher mortality, daily gain and antimicrobial resistance. …”
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Isolation of competent actinomycetes strains from soil planted with three species of mint irrigated with olive mill wastewater by Oumaima Beroigui, Lahsen El Ghadraoui, Faouzi Errachidi

“…Dimensional analysis (principal component analysis) is the best method for studying soil-microorganism-plant associations.…”
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Elucidation of genetic diversity in hawthorn (Crataegus azarolus L.) accessions naturally growing in the Eastern Mediterranean region of Türkiye using morphological, pomological, a... by Yazgan Tunç, Mehmet Yaman, Kadir Uğurtan Yılmaz, Ali Khadivi, Somayeh Goodarzi

“…Among the accessions, the pH varied from 3.02 (‘G75’) to 3.99 (‘G55’), the soluble solids content changed between 8.19 (‘G108’) and 21.98 °Brix (‘G1’), the titratable acidity ranged from 0.69 (‘G3’) to 2.29% (‘G37’), and the ripening index varied from 4.33 (‘G107’) to 29.90 (‘G3’). Principal component analysis (PCA) indicated that fruit traits made the greatest contribution to the diversity among accessions, followed by leaf traits. …”
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Discrimination of Selected Cold-Pressed and Refined Oils by Untargeted Profiling of Phase Transition Curves of Differential Scanning Calorimetry by Mahbuba Islam, Montowska Magdalena, Fornal Emilia, Tomaszewska-Gras Jolanta

“…Thermodynamic parameters, such as peak temperature, peak heat flow and enthalpy, differed for each type of oil. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used for processing data consisting of the whole spectrum of heat flow variables of melting phase transition. …”
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VARIETAL ASSESSMENT, HERITABILITY AND CORRELATION ANALYSIS OF ADAPTATION- AND PRODUCTION-RELATED TRAITS IN BREAD WHEAT LANDRACES OF THE ALGERIAN SAHARA OASES by M. Boulacel, T. Hadji, A. Ghennai, M. Hadji, A. Benlahbib, N. Souilah, H. Bendif

“…Data were analyzed for correlation coefficient, path coefficient analysis, principal component analysis and cluster analysis. The results revealed the existence of significant variations in all measured traits. …”
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Ornithine enantiomers modulate essential oil yield and constituents and gene expression of monoterpenes synthase in Salvia officinalis under well-watered and drought stress conditi... by Maryam Mohammadi-Cheraghabadi, Faezeh Ghanati, Naser Karimi, Mansour Ghorbanpour, Saeid Hazrati

“…These improvements were strongly linked to growth enhancement, as evidenced by principal component analysis (PCA). The EO extracted from S. officinalis consisted of 22 compounds, primarily monoterpenes, including α-thujone (18.47–41.65%), camphor (15.05–25.17%), 1,8-cineole (10.12–21.6%), and β-thujone (6.23–21.2%). …”
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Morphological variation of Ficus johannis subsp. afghanistanica (Warb.) Browicz in Sistan-va-Baluchestan province, Iran by Ali Khadivi, Farhad Mirheidari, Abdolvahid Saeidifar, Younes Moradi, Yazgan Tunç

“…The results of principal component analysis showed that PC1 (6.63%), PC2 (5.06%), and PC3 (3.64%) highlighted leaf sizes, fruit sizes, and branch characteristics, respectively. …”
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