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The above simulation experiments illustrated the performance of the various dissimilarity measures for microbial communities of relatively low complexity.
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A subset of these samples was measured for microbial cleanliness using both the NSA (n = 272) and ATP assay (n = 249).
Vials were recapped for 2 h and the hourly rate measured for microbial respiration.
This novel approach in reporting measured results for microbial fuel cells elucidates specific electrochemical parameters for appropriate comparison between systems and laboratories.
Addition of stoichiometric measures and other soil attributes (e.g. soil C N, C P, δ15N) in a multiple regression model explained more of the variation than a single factor plus the land use effect (though soil order still explained a small, but significant amount of variance for measures of microbial biomass).
These studies also lacked measures of microbial abundance.
A standard procedure for measuring microbial DNA change (mutation) is to place the microbes in a petri dish where they cannot grow into colonies, count the number of cells deposited, incubate them for a period of time, and count the number of colonies that appear.
Microbial habitat models were subsequently used to estimate microbial measures for a larger regional headwater riparian wetland dataset (n = 87), where edaphic property information was compiled.
The microbial strains inoculated in appropriate growth medium and treated with different concentrations of Ag NPs were incubated for adequate time and optical density was recorded at 600 nm giving direct measure of microbial cell density.
The slope of the microbial growth curves representing microbial growth ability in C, CN, CP and CNP amended samples was taken as a measure of microbial nutrient limitation.
In contrast, the hyper-variable V6 was shown to be the least optimal region for taxonomy assignments, while it was more appropriate for measuring microbial diversity due to its high variability.
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