Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis by Yong-Li Zhao, Yi-Ning Hao, Yi-Jun Ge, Yi Zhang, Lang-Yu Huang, Yan Fu, Dan-Dan Zhang, Ya-Nan Ou, Xi-Peng Cao, Jian-Feng Feng, Wei Cheng, Lan Tan, Jin-Tai Yu

“…And LASSO, rigde, and principal component analysis indicated the robustness of the primary results. …”
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T-cell receptor beta variable gene polymorphism predicts immune-related adverse events during checkpoint blockade immunotherapy by Linghua Wang, Siqing Fu, Aung Naing, Funda Meric-Bernstam, Carl Morrison, Joud Hajjar, Mingxuan Xu, Anas Alshawa, Bettzy Stephen, Ying Yuan, Jordi Rodon Ahnert, Abdulrazzak Zarifa, Shrutii Sarda, Timothy Looney, Sapna P Patel, Geoffrey M Lowman, Dzifa Yawa Duose, Jeffrey M Conroy, Evan Kwiatkowski

“…Resultant amplicons were sequenced via the Ion Torrent and TRBV allele profiles constructed for each individual was correlated with irAE annotations to identify haplotypes associated with severe irAEs (≥ grade 3).Results Our study included 81 patients who had irAEs when treated with immunotherapy for cancer. By using principal component analysis of the 81 TRBV allele profiles followed by k-means clustering, we identified six major TRBV haplotypes. …”
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Application of Kansei Engineering in Various Train Compartment Designs to Determine the User's Affective Response by Dinda Karina Yohanny, Lu'Lu' Purwaningrum, Ambar Mulyono

“…This research consists of 5 steps: (1) Determining product semantics (Kansei Words assignment); (2) Define product properties (items and categories), as many as eight design samples; (3) Distributing questionnaires to 150 respondents (75 men and 75 women); (4) Data analysis with multivariate statistics, KMO, Barlett's Test, Principal Component Analysis, and clustering of respondents; (5) Evaluate the results of the most optimal design specifications. …”
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Sequence of episodic memory-related behavioral and brain-imaging abnormalities in type 2 diabetes by Bo Hu, Ying Yu, Xin-Wen Yu, Min-Hua Ni, Yan-Yan Cui, Xin-Yu Cao, Ai-Li Yang, Yu-Xin Jin, Sheng-Ru Liang, Si-Ning Li, Pan Dai, Ke Wu, Lin-Feng Yan, Bin Gao, Guang-Bin Cui

“…The California Verbal Learning Test, Montreal cognitive assessment, and Stroop color word test was used to assess the episodic memory, general cognitive function, and executive function. Principal component analysis was applied to extract behavioral biomarkers. …”
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Eye Collateral Channel Characteristic Analysis and Identification Model Construction of Mild Cognitive Impairment by WU Tiecheng, CAO Lei, YIN Lianhua, HE Youze, LIU Zhizhen, YANG Minguang, XU Ying, WU Jinsong

“…Frequency analysis and principal component analysis were used to explore the distribution characteristics of TCM symptom elements in MCI patients. …”
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Association between echocardiographic parameters of cardiac structure and function and mild cognitive impairment by Kai Zhang, Xiaoxia Liu, Siyu Huang, Xinrui Liu, Meiqi Zhao, Chao Xue, Shijun Xia, Jianzeng Dong, Yu Kong, Changsheng Ma

“…The echocardiographic cardiac parameters and cognitive status were retrieved through the clinical electronic database from May 2021 to August 2023. Principal component analysis (PCA), negative binomial, and conditional multivariate regression were performed. …”
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Genomic insights into genetic diversity and seed coat color change in common bean composite populations by Eva Plestenjak, Eva Plestenjak, Mohamed Neji, Lovro Sinkovič, Vladimir Meglič, Barbara Pipan

“…Population structure was analyzed using Fst, principal component analysis (PCA) and clustering. Cross-population statistics (XP-CLR and XP-EHH) were used to identify selection signals associated with seed coat color change. …”
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Implementation of a genotyped African population cohort, with virtual follow-up: A feasibility study in the Western Cape Province, South Africa [version 2; peer review: 2 approved,... by Robert J Wilkinson, Francisco Lakay, Nicki Tiffin, Joel A Dave, Catherine Riou, Peter Raubenheimer, Anna K Coussens, Tsaone Tamuhla, Melissa J Blumenthal, Maleeka Abrahams

“…Results 2267346 variants were analysed in 459 participant samples, of which 229 (66.8%) are female. 78.6% of SNPs and 74% of samples passed quality control (QC). Principal component analysis showed extensive ancestry admixture in study participants. …”
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Mitigation of polycyclic aromatic hydrocarbons (PAHs) in roasted beef patties by cold plasma treatment and products quality evaluation by Yuke Hou, Yangjian Hu, Min Li, Jiahui Nong, Fengyuan Xie, Yuhan Fan, Jianhao Zhang, Xianming Zeng, Minyi Han, Xinglian Xu, Xia Wang

“…The results of principal component analysis showed that the first two principal components accounted for more than 80% of the total variation in the original data, indicating that the content of saturated fatty acids was significantly positively correlated with the formation of PAHs. …”
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Exploring agronomic diversity and disease tolerance in soybeans to combat Cowpea mild mottle virus by H. Kuswantoro, S. Zubaidah, E. Uge, K. Rozana, P. Purwantoro, J. Purnomo

“…The attributes that had a substantial impact on the principal component analysis biplot and their correlation with seed yield per plot were initially identified  as selection criteria.FINDINGS: Soybean plants displayed diverse agronomic characteristics and disease ratings. …”
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Screening and identification of evaluation indicators of low phosphorus tolerant germplasm in Gleditsia sinensis Lam. by Chunyun Lu, Xiurong Wang, Yang Zhao, Rong Zou, Feng Xiao

“…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%. …”
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Spatiotemporal analysis of rainfall and temperature variability and trends for a mixed crop-livestock production system: its implications for developing adaptation strategies by Sintayehu Alemayehu, Daniel Olago, Opere Alfred, Tadesse Terefe Zeleke, Sintayehu W. Dejene

“…The research uses advanced statistical techniques, including rotated empirical orthogonal function (EOF) and rotated principal component analysis (RPCA), to identify and quantify significant patterns in seasonal rainfall, temperature and drought indices over the period from 1981 to 2022. …”
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Global, regional, and national survey on burden and quality of care index (QCI) of nasopharyngeal cancer: A systematic analysis of the Global Burden of disease study 1990–2019 by Saba Ilkhani, Sahar Saeedi Moghaddam, Delaram Sakhaei, Mohammad-Mahdi Rashidi, Sina Azadnajafabad, Mohammadreza Azangou-Khyavy, Naser Ahmadi, Ozra Tabatabaei-Malazy, Mohammadreza Naderian, Erfan Ghasemi, Parnian Shobeiri, Sahar Mohammadi Fateh, Farzad Kompani, Bagher Larijani, Farshad Farzadfar

“…The quality-of-care index (QCI) was rescaled to 0–100 using principal component analysis (PCA). Results: Global NPC incidence rose 161.4 % from 1990 to 2019. …”
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Factors associated with physical fitness and disparities in population segments among older adults: A cross-sectional study by Yuqin Rao, Jingjing Wang, Kai Li, Fanhui Zheng, Ying Chen, Bo Zhai, Jintao Hong, Chen Wang, Bao Liu, Dao Wang

“…A profile analysis model was constructed by feature extraction, label design and clustering using principal component analysis and k-prototypes. Factors associated with physical fitness were analyzed using ordered probit regression, and within-group differences were evaluated through the bootstrap method. …”
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Flood susceptibility mapping based on watershed geomorphometric characteristics and land use/land cover on a small island by B.H. Narendra, O. Setiawan, R.A. Hasan, C.A. Siregar, , Pratiwi, N. Sari, A. Sukmana, I.W.S. Dharmawan, R. Nandini

“…Watershed classification was carried out using 24 geomorphometric variables and land use/land cover, representing aspects of the river network, geometry, texture, and watershed relief. The principal component analysis approach was carried out to determine the most significant variable, and the weight of each variable was determined using the weighted sum approach method. …”
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Macrofaunal biopores: Diversity and regeneration rates across diverse pedoclimatic conditions studied with repacked soil cores by Charlotte Védère, Hanane Aroui Boukbida, Yvan Capowiez, Sougueh Cheik, Guillaume Coulouma, Rinh Pham Dinh, Séraphine Grellier, Claude Hammecker, Thierry Henry Des Tureaux, Ajay Harit, Jean Louis Janeau, Pascal Jouquet, Jean Luc Maeght, Pascal Podwojewski, Cornelia Rumpel, Stéphane Sammartino, Norbert Silvera, Siwaporn Siltecho, Lotfi Smaili, Bounsamay Soulileuth, Nicolas Bottinelli

“…After 12 months, biopores were characterized by X-ray computed tomography and grouped according to their diameter, length and sphericity index using principal component analysis followed by K-means clustering. …”
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Research on Characteristics and Influencing Factors of High Temperature Disaster Risk in Wuhan Based on Local Climate Zone by Shujing GUO, Li ZHANG

“…Then, a combination of the analytic hierarchy process and principal component analysis is adopted to assign weights to the indicators, with such weights being finally superimposed to obtain the hazard map, exposure map and vulnerability map, respectively. …”
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Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function by Si-yu Wang, Yu-xin Wang, Lu-shun Guan, Ao Shen, Run-jie Huang, Shu-qiang Yuan, Yu-long Xiao, Li-shuai Wang, Dan Lei, Yin Zhao, Chuan Lin, Chang-ping Wang, Zhi-ping Yuan

“…Methods Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. …”
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Inflammation severity, rather than respiratory failure, is strongly associated with mortality of ARDS patients in high-altitude ICUs by Daniel Molano-Franco, Daniel Molano-Franco, Daniel Molano-Franco, Daniel Molano-Franco, Daniel Molano-Franco, Joan Ramon Masclans Enviz, Joan Ramon Masclans Enviz, Antonio Viruez-Soto, Antonio Viruez-Soto, Antonio Viruez-Soto, Antonio Viruez-Soto, Mario Gomez, Harvey Rojas, Edgar Beltran, Victor Nieto, Fernanda Aliaga-Raduan, Fernanda Aliaga-Raduan, Pablo Iturri, Christian Arias-Reyes, Christian Arias-Reyes, Christian Arias-Reyes, Jorge Soliz, Jorge Soliz

“…Advanced statistical analyses, such as multivariate logistic regression and principal component analysis, were utilized to identify key clinical predictors of ARDS-related mortality.ResultsOur findings indicate that in high-altitude ICUs, monitoring inflammatory markers may be more beneficial for improving ARDS survival rates than emphasizing respiratory failure markers. …”
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Harnessing root phenotyping and root growth plasticity of landraces of maize (Zea mays L.) for enhancing waterlogging tolerance in fragile and challenging agro-ecologies of Eastern... by E. Lamalakshmi Devi, Krishnappa Rangappa, Ayam Gangarani Devi, S.K. Chongtham, Ingudam Bhupenchandra, Konsam Sarika, Harendra Verma, Rumi Narzari, Samarendra Hazarika, Sankar Prasad Das, B.U. Choudhury, Anil Kumar Choudhury, T.L. Bhutia, V.K. Mishra

“…Phenotyping of key root traits (brace root angle (BA1), branching, and crown root number) revealed their substantial contribution to stress resilience, as confirmed by principal component analysis (PCA) and regression models. PCA indicates, for root tissue density (RTD) and root fineness (RF) at seedling stage, genotypes like RCM-34-19, RCM-10-19, RCM-23-19, RCM-42-19, and RCM-31-19 are closely associated with the second principal component (PC2). …”
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