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The discrepancies between these data could be due to differences between mammalian and chicken PrPC sequences (∼30% homology) alternatively used in the different studies and/or to the overexpression of exogenous PrPC molecules in neuroblastoma cells.
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These data could explain the link between ageing and respiratory control as well as ROS generation.
The discrepancy between these ELISA data could be attributed to blockage of the monoclonal capture antibody (mAb) epitope due to MIF interaction with other serum proteins.
Comparison between expression and phylogenetic data could lead to a better understanding of the role(s) of OBP family in mosquitoes.
Most discrepancies between morphological and molecular data could be resolved by adopting a classification modified from Montecchi & Sarasini [16] by incorporating some of Soehner's [26] views.
Chi square tests were used to examine differences in demographic variables between women whose questionnaire data could and could not be linked to EHRs.
The disconnect between transcript and metabolite data could be, therefore, explained by the dynamic and/or synergistic regulation of phenolamide biosynthesis; developmentally and by herbivore/pathogen attack.
The connection between methylomics and transcriptomics data could reveal methylation changes in ALS tissues, such as the spinal cord or blood.
The discrepancy between this and our data could be explicated by differences in substrains used and protocols of testing anxiety-like behaviours.
This difference between Genevestigator and experimental data could be a consequence of different levels of detection in RT-PCR and microarray experiments or different experimental conditions.
The same exact tissue samples (from a common grind) were used for RNA and protein isolation for all procedures to ensure that direct comparisons between RNA and protein data could be made.
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