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In our analysis we utilized a sample that covered in 2007 approximately 69% (14 ASHIPs) and in 2008 approximately 52% (eleven ASHIPs) of the total German population, respectively.
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One of the limitations of the study was that this study utilized a sample with an overall mean GIS-estimated arsenic exposure level below that of the current acceptable exposure level of 10 ppb set by the EPA.
Where possible, future research should utilize a sampling strategy that better represents coverage of facilities of certain types and facilitates comparisons.
Previous studies of genome-wide power using arrays have utilized a finding that the sample size required to achieve a given power is N/ r when using a tag marker compared with N when using the causal locus genotypes, where r is the squared correlation between the discrete allelic dosages at each locus (Pritchard and Przeworski 2001).
Instead we utilize a sampling method to approximate the expectation.
The difference in our study is firstly that our prospective study utilized blood samples that did not come from the same patients as our tumour samples.
Slow pyrolysis at a temperature of 400 °C was utilized to generate a biochar sample that still retained some oxygen functionality (which is generally lost at higher temperatures), while ensuring that full pyrolysis occurred, instead of torrefaction which happens at lower (~ 300 °C) temperatures (Shankar Tumuluru et al. 2011).
In the proposed method, Markov chain simulation is utilized to generate samples that distribute asymptotically to the optimal importance sampling density.
First, these studies typically utilized male-only samples that had played collision sports such as football and ice hockey (De Beaumont et al. [2009]; DeKosky et al. [2010]; Didehbani et al. [2013]; Guskiewicz et al. [2005], [2007]; Kerr et al. [2012b]).
Typically, studies have utilized surgically dissected samples that included mixtures of cell types [ 9, 10].
We were able to utilize a large colorectal cancer sample that has been examined for multiple molecular events, and appropriate biostatistical methods.
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