Consistent with this basic prediction, our study showed that ΔmtDNAs can persist and diversify at small nematode population sizes where drift is strong but selection is weak (e.g., N = 1 in HK104), but can be subject to selective elimination in larger nematode populations (e.g., N = 1,000 in EG4181)—see figures 5 and 6 and table 3.
However, when the overall number of predictions in our study is taken into account, this prediction is not significant: closeness must have been observed at least 4 times as a reliable predictor in these 18 similar networks.
However, predictive models obtained by discriminant analysis including additional variables showed better AUC values and more accurate predictions in our study population (90.5 95.2% sensitivity and 95.5 100% specificity).
However, mir-1 was also reported to show high specificity in both heart and skeletal in several other studies, which indicates that all the above 12 predictions in our study are right [24], [25].
Also supporting the initial predictions of our study was the observation of reduced functional connectivity in midline regions of the 'default mode network' when comparing the sad recall and neutral recall conditions.
The validity of this strategy is underlined by the fact that several predictions from our study have recently been validated in cell lines.
However, such factors would not be able to explain a cross-over interaction or double dissociation for the factors location and word category, which are the crucial predictions of our study (see Henson, 2006 for a discussion of possible inferences that can be drawn from functional imaging data).
Junker et al. [ 7] concluded that from the 1990s to the 2000s there had not been significant reductions in suitable environmental conditions for this subspecies, but the future predictions of our study indicate a drastic loss of suitable habitat by year 2020 followed by progressive degradation of suitable habitat for half of the range of P. t. ellioti due to climate change.
The network trained for RE events and calibrated with the RE-ANN calibration method delivered the best prediction for our study area and period.
