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As large sample volumes (up to 1000 µL) of real-world samples including waste and surface waters, some of which were untreated had to be injected in order to achieve the desired LOD and LOQ, it is to be expected that larger amounts of other matrix components, like non-polar organics, humic acids, or transition metals, were co-injected, leading to the decrease in retention time.
Due to the limited size of this training dataset, it is to be expected that larger training datasets will further improve the prediction performance.
Moreover, it might be expected that larger plasmids possess a higher number of "entry points" for exogeneous DNA in respect to smaller ones, thus somehow promoting the acquisition of novel genetic material.
If size fluctuations are indeed an effect of the indirect evolution of robustness, it could be expected that larger population sizes enhance both the streamlining and the fluctuation pattern.
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Although DNA uptake from the environment and distantly related organisms occur in genus Brucella [ 24, 25], it may be relatively rare otherwise it is expected that larger rearrangements would have been observed in the cloud tree [ 23].
It is expected that large solar systems will be used more often in the coming years.
It is expected that large fractions of these ENMs will end up in landfills for final disposal.
It is expected that large scale disturbance events may increasingly affect forest C stocks in the face of climate change [17].
It is expected that large window sizes for baseline DC estimation would improve the DC estimation in cases where the background changes monotonically (as for signals of types 2-4).
Thus, it was expected that large tumour mass were insensitive to the treatment.
It is expected that large-scale ChIP analysis of general TFs contributes more confident TFBSs by observing the aggregated peaks within promoter regions of human miRNAs.
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Justyna Jupowicz-Kozak
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