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. 2020 Sep 25;10:15790. doi: 10.1038/s41598-020-72801-1

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Principal Component Analysis of the CCl₄ group. (A) Variance contributions of factors in % in the first dimension. (B) Contribution of factors in % in the second dimension. (C) Variable correlation plot showing the relationships of all variables in the experiment. Positively correlated variables are grouped, negative ones show in different directions. The vector lengths represent the quality in the factor space. The longer they are the better the variables are represented in the shown dimensions. (D) Projection of individual animals into the two-dimensional PCA factor space. Group centroids are characterized by the 95% confidence ellipses and the experimental times are color-coded as weeks. The graphs were created using the R software version (3.6.2) (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/).