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Accuracy could also be improved by detecting synapses in 3D, though this would require many more images and more sophisticated computational techniques that can automatically segment, align and reconstruct synapses across serial sections.
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The accuracy of population haplotyping can be improved by modeling population substructure and detecting cryptic relatedness.
The comprehensiveness of a genome-scale metabolic reconstruction can be improved by using the model to detect and fill network gaps (Rolfsson et al., 2011).
The validity of these analyses could be improved by: Longer term studies to detect potential longer term effects, such as decrease in health services utilisation that may occur if patient outcomes are improved.
Similarly, reconstruction fidelity can be improved by using the model to detect reconstruction stoichiometry inconsistent with biochemistry (Gevorgyan et al., 2008) or reactions inconsistent with steady state flux (Vlassis et al., 2014).
The sensitivity of the search strategies will be improved by including keywords from relevant trials detected by earlier searches.
Incomplete PCR amplification can be identified by testing a range of annealing temperatures, performing repeat PCRs and/or the typing of a pedigree (if available), and, if detected, can be improved by PCR optimisation methods.
The ability to detect change can be improved by including an additional explanatory variable such as paired watershed measurements.
Statistical power to detect longevity genes could be improved by excluding accidental, unrelated to aging mortality.
The diagnostic use of the TST may be improved by modifying the 'cut off' values used to detect M.tb infection.
What if clinician imperfection could be improved by a form of artificial intelligence that continuously detects, and prompts correction of, defects in bedside care?
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