Sentence examples for run-to-run variability from inspiring English sources

There was greater run-to-run variability for MS patients than for HC.

Considerable run-to-run variability was observed both in exposure concentrations and molecular responses of exposed fish across exposure experiments.

To address run-to-run variability, slides were stained and analyzed on the same day using the same protocol.

This work describes the UM-RCF and its operation, establishes obtainable pressures and temperatures (over 1900 kPa and 970 K for predominantly N2 gas mixtures and over 785 kPa and 2000 K for Ar gas mixtures), and demonstrates the repeatability of the UM-RCF experiments (<3% run-to-run variability in peak pressure) for combustion studies.

The analytical variability on quantification is reported to significantly reduced in this experimental procedure in comparison the label-free approach where samples are measured separately, and differences in sample preparation as well as run-to-run variability can affect the results.

This may be because smoke delivery from the smoking machine is not always the same or it may originate from the assay technology, because WST-1 reduction continues post-exposure and small time differences in sample reading may contribute to run-to-run variability.

Serial sections of a smaller specialized TMA (control array) were stained aside both cohorts to confirm assay reproducibility; this allows for the construction of a normalization standard curve to adjust for run to run variability.

Bcl-2 AQUA scores for specimens on the validation cohort were converted according to the cell line standard curve (y = 0.3197x+8.8; Figure 1A), in order to normalize for run to run variability.

Here, we employed AQUA[ 39], a fully quantitative method to calculate Bcl-2 concentration within subcellular compartments and construct a cell line-based standard curve in order to normalize for run to run variability.

The difference of the measured and predicted MQW parameters is recorded run-to-run over time, which allows process variability to be analyzed across a number of process parameters with intentional variations to grow different MQW designs.

Furthermore, variability in host metabolic phenotypes from run-to-run can affect product yield and quality in unexpected ways.