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Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay.
Other models have analyzed a single vaccine applied to different age categories [ 2, 30, 31], while some models compare vaccine types [ 2].
Although many mathematical models have analyzed apoptosis, they have been limited to partial signaling pathways or to proteins related to apoptosis.
Some population genetic models have analyzed the general spatial spread of underdominant alleles (e.g., Barton 1979; Schofield 2002; Soboleva et al. 2003; Turelli 2010), but they include very simple or even no population dynamics.
Although few studies (all of them in animal models) have analyzed the effect of fish oil on DNA methylation, it has been observed that the epigenetic effects of n-3 PUFA depend on the gene and the tissue and are far more relevant during pregnancy and lactation than during adult life [ 14– 17].
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However, our data suggest that the assumption that substrates produce brighter signal as the result of signal amplification is not valid, at least in the tumor models we have analyzed here.
For both models, we have analyzed the time-course data and results were imported to MS Excel, and graphs have been generated using MS Excel.
In light of the new findings in NMO and its experimental models, we have analyzed the astrocytic response to LPS both in vitro and in vivo.
Using these metabolic models we have analyzed the metabolic capacity for carbon, nitrogen, phosphorous, sulfur, and iron utilization in aerobic and anaerobic conditions and have identified conserved and differentiating catabolic phenotypes and validated these predictions by comparison to experimental data.
Based on this model, we have analyzed the role of some key parameters involved in the chemotactic behavior to unravel the underlying design principles.
Based on a novel theoretical model, we have analyzed the reconfiguration efficiency of MARCHES and compared it with those of peer CARM frameworks: MobiPADS and CARISMA.
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