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Accurate identification of cell subsets in complex populations is key to discovering novelty in multidimensional single-cell experiments.
Several groups have successfully used microarrays to measure the frequency of known single nucleotide polymorphisms (SNP) in complex populations.
The response of immune cells to stimuli in complex populations is the product of direct and indirect effects, and of homotypic and heterotypic cell interactions.
This makes the performance of genetic studies of dogs easier than those conducted in complex populations.
Therefore, meta3C paves the way to the integrated characterization and analysis of metagenomes in complex populations.
Lee and colleagues showed that subcellular resolution can be achieved with their method, an interesting feature that would allow the study of regulatory elements such as non-coding RNA species in complex populations of cells, for example, in brain tissue.
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We present an extended methodology for parametric inference in complex population balance models.
However, although Wolbachia shortens the lifespan, it also gives a breeding advantage which results in complex population dynamics.
Here, we introduce a new approach for simulating positive selection in complex population histories with subdivision, migration, bottlenecks, and expansions in a coalescent framework.
Our approach can be extended to understand additional factors that affect imputation accuracy in complex population-genetic settings, and the results can ultimately facilitate improvements in imputation study designs.
Even fewer studies have investigated simultaneously the density-dependent (intrinsic) and density independent (extrinsic) influences on cyclic dynamics (but see [ 25, 26]), although such joint analyses are crucial to understand variation in complex population dynamics, since failure to account for one type of effects may bias estimates of the other.
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