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In contrast to classical index selection, this approach to mate selection can optimize long-term responses.
Given appropriate and targeted topic and country selection, this approach is likely to meet the purpose of using structured methods to provide examples of effective research utilisation.
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However, loci detected as being under selection using this approach may be an underrepresentative sample of all truly selected loci; in particular, selection on standing variation and recessive loci are likely underrepresented (Teshima et al. 2006).
However, caution is required when measuring selection using this approach because the stability of RNA molecule secondary structure as well as translational selection may impose constraints on synonymous sites leading to lower dS values and consequently to inflated ω estimates [ 61- 64].
Comparisons of population divergence for different sets of loci are useful for identifying loci under directional selection [ 44], although the large confidence intervals which sometimes overlap zero can make it difficult to infer stabilizing selection with this approach [ 81].
Despite these encouraging data, the low number of cells that can be isolated by clonal selection make this approach hardly applicable to cell-based therapy, until the identification of growth factors able to enhance the cell yield from isolated clones.
However, the importance of patient and tumor selection for this approach must be emphasized.
However, model specification and selection with this approach is cumbersome and requires the estimation of numerous parameters.
However, the increased detection of positive selection using this approach may be linked to regions having undergone true functional change.
In addition, patient selection for this approach is difficult because of the heterogeneous course of metastatic RCC.
Importantly, aptamers were retrieved in only a few (three) rounds of selection by this approach and did not require further optimization, significantly streamlining the process of beacon development.
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