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Our key results lie in the fact that we see an expression pattern for key markers for adult neurogenesis also in the human brain and that this expression pattern changes with time both qualitatively and quantitatively.
Expression pattern for key biomarkers in this study is similar to that reported in human sepsis, indicating the suitability of this model for future studies of sepsis, and the observed differences in gene expression suggest species differences or differences in the response of blood leukocytes and peritoneal leukocytes.
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Abridged gene expression patterns for key regulatory genes involved in both sucrose and starch metabolism are shown.
Direct identification of the hydrogen atoms improves the description of the hydrogen bonding patterns for key residues.
In addition, we have identified novel coexpression patterns for key transcription factors involved in hematopoiesis (eg, GATA3-GFI1 and GATA2-KLF1).
Having identified predominantly cell type-specific binding patterns for key regulatory TFs raised the question as to whether TFs are passively recruited to cell type-specific regions of open chromatin with no major regulatory impact, or whether they actively participate in two different transcriptional programmes.
DOI: http://dx.doi.org/10.7554/eLife.04660.010 We next analyzed gene expression patterns for 36 key known functional mediators of somatosensation.
The patterns for the key variables of interest remained the same in Model 2 and there was no significant interaction between decision-making autonomy and community-level attitudes.
Comparison of these results with the expression data published by Blenkiron and colleagues [ 14] revealed remarkably similar expression patterns for several key miRNAs.
Concerted mathematical and experimental analysis of global binding patterns for 10 key haematopoietic TFs in both cell types suggests that cell type-specific binding is not opportunistic, but instead makes meaningful contributions to cell type-specific transcriptional control.
To conduct a systematic review and meta-analysis of prices of healthier versus less healthy foods/diet patterns while accounting for key sources of heterogeneity.
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CEO of Professional Science Editing for Scientists @ prosciediting.com