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Temporal variability, which is the result of changes in community structure over time, may describe changes within years (i.e., seasonal variability) or among years (i.e., annual variability).
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Although birth death model and other models may satisfactorily describe changes in gene number over evolutionary time (Reed and Hughes 2004; Novozhilov et al. 2006; Hahn 2009; Ames et al. 2012), they do not explicitly address the role of adaptation in gene family proliferation.
Therefore, they may only partly describe changes that occur in vivo in platelets eventually entrapped in the microcirculation.
We may then describe change in TA levels x t) using a stochastic model: <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0000962.e005.PNG" class= inline-graphic"/> Loss is defined as a random variable L(t) = inline-graphic loss rate θ plus a mean zero random component εL, while εt is a second, independent mean zero random variable.
In addition, further immunological research may describe the underlying causes for changes in antibody composition over time, which could help inform the shape of the BED response function.
This may describe the natural history of microvascular changes in diabetes: 1) early impairment of microvascular autoregulation with inappropriate vasoconstriction, 2) subsequent compensatory dilation, 3) loss of smooth muscle cells and pericytes (or podocytes in the kidney), and 4) loss of wall structural integrity and, finally, irreversible dilation.
These described changes may trigger cell damage and death.
Furthermore, analysis of joint arthrokinematics, such as those described, may identify changes in joint mechanics associated with joint degeneration.
These signatures may therefore describe adaptive changes that occur in response to long-term depletion of NKX3.1 in addition to its immediate effects on gene expression.
The reason for reduction of drug loading efficiency by changing the ion type from Zn+2 to Fe+2 may be described by the changes in the particle size of the nanoparticles.
While our model may not fully describe pregnancy-related changes in drug distribution, this simplification allows us to focus on the roles of the relevant organs during this initial stage of model development.
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