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Conventional graphene patterning methods have typically been based on top-down processes to achieve well-defined nanoscale patterns.
Computational design methods have typically treated the protein backbone as a rigid structure for the sake of computational tractability.
Previous estimates of food waste using these traditional methods have typically concluded that about one third of food mass is wasted [4], [5].
These methods have typically involved direct measurements of the mean fitness of the populations over time.
In terms of performance in predicting functional associations, however, the dedicated flux-based methods have typically been superior.
Therefore, most previous methods have typically ignored cross-hybridization and focused on taking mean or median intensities of probes as the estimated raw CNs [ 15, 31].
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Consequently, the choice between these two methods has typically become an analyst's preference.
In response to such observations, Bader and Shugars [ 53, 56] suggested that comprehensive approaches to improving consistency across the dental profession would improve quality more than traditional methods, which have typically focused on outlier dentists.
Food waste treatment methods have been typically analysed using current energy generation conditions.
Two different classes of methods have been typically applied in traditional meta-analysis of GWAS: P-value-based tests and effect size-based methods, which can be further subdivided into fixed or random effects models [ 64].
Previous methods such as [8] have typically used raw codeword histograms (CW), not PLSA-based class-posteriors (CP), as the input to their patch-level classifiers or their MRF/CRF layer.
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