Sentence examples for mild deviations from from inspiring English sources

Mixtures of polar molecules often exhibit only mild deviations from ideality, but mixtures containing polar and nonpolar molecules are frequently strongly nonideal.

Although EnKF is optimal for multivariate Gaussian model variables and linear data model, acceptable history matching results have been obtained for mild deviations from these optimality conditions.

However, new evidence suggests that even mild deviations from normal and simple variability of sodium values may also be significant.

16 Standardization is known to summarize risk trends and differentials well under wide conditions, and to be robust against mild deviations from those conditions.

When tested in the physiological context of zebrafish embryogenesis, F331A, W333A and C335A mutations in Wnt3a showed only mild deviations from the wild-type Wnt3a, retaining most of their signaling capacity, which might be due to compensatory effects by a different receptor context and responsiveness of the tissue.

All in all, perhaps there is only a mild deviation from the requirement of a stable age profile in Bulgaria, Russia, and Romania.

We refer to this as weak perturbation RNAi experiments as we aim to identify principle phenotypical alterations upon a mild deviation from non-RNAi conditions, similar to determining the linear response to a small perturbation.

It would seem that mild deviations in the shape of the firing rate from Heaviside (to a steep sigmoidal form) are reflected more in temporal speed than in spatial shape changes.

Perceptual and acoustic analyses showed mild deviations whereas laryngeal visual examination revealed a complete right vocal fold detachment from the anterior commissure to the vocal process, with generalized hyperemia.

The simulation results suggest that the proposed Bayesian approach is not sensitive to mild or moderate deviations from the normality assumption for the expression intensities.

Simulation results show that the proposed method can improve the power of gene set enrichment meta-analysis, as opposed to existing methods developed by Shen and Tseng (2010, Bioinformatics, 26, 1316 1323), and it is not sensitive to mild or moderate deviations from the distributional assumption for gene expression data.