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The non-uniform deformation near the clamped area contributed to the discrepancy between the stress-stretch curves.
Two of the studies included subjects with mean migraine frequency close to chronic migraine (we had none), and this could have contributed to the discrepancy between their and our results [21, 24].
Although this could have partially contributed to the discrepancy between the computed and measured flow velocities and the super-elevations, the obtained numerical results reflect the trend of the real event.
Such difference in thyroid uptake may indicate a slow and steady pace of tracer degradation for 123I-CMICE-013, albeit the different evaluation methods (imaging versus tissue) would have also contributed to the discrepancy in the results between the two studies.
Multiple factors, such as the difference between cellular- and tissue-specific responses, may have contributed to the discrepancy.
Although a number of factors may have contributed to the discrepancy between the data and figures shown in Sandin et al. [9] (e.g. site selection, fish behaviour, schooling), our results suggest that the difference could be explained by fish speed alone if non-instantaneous surveys were used.
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Differences in populations, interventions, perspectives, and time horizons have all contributed to the discrepancies between studies.
Although the datasets we compared both represented urban land, different types of land use/land cover included in the definition of urban land and different minimum mapping units contributed to the discrepancies.
Alternatively, differences in immunostaining methods and scoring protocols may have contributed to the discrepancies.
This methodological issue may have contributed to the discrepancies regarding circulating HMGB1 and disease activity between the two studies.
Our exploration of data quality issues, however, revealed potential errors in both databases that may have contributed to the discrepancies.
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