While methods for interpreting population genomic data are still in their infancy, we discuss current interpretations of existing datasets in the light of evolutionary processes and models.
Despite the simplifying approximations involved in our derivations, especially in the extrapolation to higher spatial dimensions, the models demonstrate a satisfactory and very useful ability to quantitatively interpret population assays for bacterial and leukocyte chemotactic migration.
We used SCD slopes to interpret population structures.
Refinements that build on the Lotka-Volterra approach, which itself is neutral on whether to interpret population density in terms of biomass or numbers, remain overwhelmingly dominant today [7], [8], [29].
Complementary single-cohort analysis was also helpful when interpreting population level effects.
Population history, including whether the population is recovering or declining, and genetic patterns are important factors when interpreting population diversity levels.
Finally, to interpret population structure in relation to both cultivar use and history, we characterized the clusters using the geographic origin of cultivars and their phenotypic characteristics.
These findings highlight the importance of drug factors in predicting antimicrobial drug PK during ECMO and should be a consideration when performing and interpreting population PK studies.
These findings highlight the importance of drug factors in predicting antibiotic drug PK during ECMO and should be a consideration when performing and interpreting population PK studies.
These results illustrate the challenges faced when interpreting population dynamics estimates based on genetic data for the purpose of species management.
Given the policy, research and administrative implications of interpreting population data, selecting an accurate, yet practical comorbidity index for risk prediction is crucial.
