Sentence examples for minimize systematic biases from inspiring English sources

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Here, we develop and apply methods to minimize systematic biases, uncover the biological mechanisms underlying phylogenetic conflict, and outline data requirements for future phylogenomic and morphological data collection.

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To minimize systematic bias in library preparation, for each genome we prepared two paired-end DNA libraries with a read length of 90 bp and insert sizes of 475 504 bp (Table S1).

Motility parameters measurements have been carried out in triplicate in order to minimize systematic bias; thus, spermatozoa in slide chambers have been immobilized by heat with a specific soldering iron, then a morphology assessment has been performed.

To minimize systematic bias from transcriptome sampling and Illumina sequencing, and to enhance the accuracy of detecting low-abundance transcripts, three cDNA libraries from the same pooled RNA sample were constructed and sequenced separately using an Illumina HiSeq2000 genome analyzer.

Does the study avoid or minimise systematic bias?       6.

High-quality randomized controlled trials (RCTs) are the preferred method to establish the effects on efficacy and safety outcomes, since they minimize systematic error (bias).

Given differences in the experiments, we used Distance Weighted Discrimination (DWD) [ 17] to minimize systematic microarray data biases attributable to RNA source, different analysis laboratories, different microarrays, or other systematic differences which include sex and strain in this case.

Therefore, researchers should minimize systematic avoidance and availability related biases (i.e., vessel and day-night period) to improve population abundance estimates.

To minimize any systematic bias from transcriptome sampling, and to improve our accuracy in detecting low-abundance transcripts, we constructed and sequenced three cDNA libraries from the same pooled RNA sample.

A commonly recommended way to minimize bias from non-participation is to increase participation, in the hope of minimizing systematic differences between participants and non-participants.

Prior to the samples being analyzed they were subjected to a blocking and randomization treatment to minimize the effects of systematic biases and ensure the even distribution of known and unknown confounding factors across the entire experimental dataset.

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