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Mantel tests between the genetic and geographical distance matrices were significant (r = 0.12, P < 0.001).
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Therefore, the interaction of ENPs in environmental matrices is significant in understanding the risk of potential exposure and/or uptake by biota.
How to quickly compute eigenvalues and eigenvectors of a matrix, especially, a general real matrix, is significant in engineering.
The many possible pairwise comparisons of overlapping and non-overlapping coefficients showed that as a rule of thumb differences greater than.07 between two coefficients in the matrix are significant.
As the efficient calculation of eigenpairs of a matrix, especially, a general real matrix, is significant in engineering, and neural networks run asynchronously and can achieve high performance in calculation, this paper introduces a recurrent neural network (RNN) to extract some eigenpair.
Thus, interactions between chondrocytes and their matrix are significant, so it is important to study these cells in their native environment.
Correlations having P-value ≤0.05 after 9999 permutations of matrices were considered significant.
Mantel tests comparing genetic differentiation (FST) and geographical distance matrices were not significant (data not shown).
As bone resorption occurs, cytokines and growth factors would be released into the surrounding matrixes that are significant for regulating new bone development.
Directing cells towards a chondrogenic phenotype and promoting new matrix formation are significant challenges that must be overcome for the successful application of hydrogels in cartilage tissue therapies.
The differences in growth rates on the 3D matrices were also highly significant (P < 0.0001).
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