Sentence examples for strength of constraints from inspiring English sources

Conducting the survey made it possible to extract more fine grained knowledge with respect to the perceived constraints by analyzing the perceived strength of constraints and differences between users and non-users.

As a result, laboratory experiments may underestimate the strength of constraints on evolutionary responses to global change.

The plausibility of the convergence hypothesis depends on the strength of constraints that affect evolutionary trajectories of isofunctional proteins [ 11].

For example, we showed that in signaling networks different types of interactions have different strength of constraints on protein co-evolution, in which proteins linked by physical interactions tend to be more co-evolved.

However, in our analysis, we further found that in signaling networks different types of interactions have different strength of constraints on protein co-evolution, in which proteins linked by physical interactions tend to be more co-evolved.

The absence of a strong correlation between heteropecilly and heterotachy makes sense because the two heterogeneities do not apply on the same criteria: heterotachy is mainly due to loss or gain of functional constraints (i.e. variable strength of constraints), whereas heteropecilly is rather due to variation in the nature of functional constraints, not in their strength.

Branch-site models assume a very specific form of functional divergence that is, a punctuated burst of adaptive sequence turnover but functional divergence can instead manifest as variation in the overall strength of constraint or residue conservation between clades [ 99].

We adopted a generalized inversion method in which the solutions are determined under different a priori strengths of constraints on initial unknown parameters.

The penalty function Q represents the strength of the constraints against the variability in the first derivative of the ϕ-functions as follows: (9 where the index j runs across all the Delaunay triangles with areas Δ j ; and and is the function value of the vertex coordinate and, respectively.

To estimate the parameters; we often use the penalized log likelihood (Good and Gaskins, 1971) (3)where the function Q represents a positive valued penalty function, and τ = (τ1,⋯, τ K ) is a vector of the hyper-parameters that control the strength of some constraints between the parameters θ.

We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene.