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Some variability in aging features was seen that was informative.
Accumulated across the lifespan, such events could have a profound impact on individual variability in aging.
We speculate that these differences in synaptic transmission also explain some of the inter-individual variability in aging phenotypes (Pinto et al., 2014; Vijg, 2014).
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This result explains much of the variability in age associated pain threshold in the literature.
An important investigation of individual variability in ageing, this study found that neuropsychological measures of frontal, but not hippocampal, function predict individual differences in source memory preformance.
The Old World almond cultivation was characterized by small plantings mainly for family use; trees interplanted with other crops; variability in age, condition, and bearing capacity of individual trees; and hand labour, often with crude implements.
There is great variability in age of onset and progression of disease, only about 40% of which can be explained by the number of CAG repeats – and lifestyle choices might account for some of that variability.
However some significant points remain unclear, since the great variability in age and general conditions of the affected people makes it difficult to find a reliable interpretation of the exact nature of this phenomenon which is reasonably considered to have a multifactorial origin.
The purpose of the present investigation was to replicate and extend our earlier findings and to use a method that would result in a greater variability in ages of memories and provide different indices of measuring spreading activation.
The model explained about 86% of the variability in age at onset of PD.
However, we also observed variability in age-dependent phenotypic expression of Aβ molecular pathology in the lens that comports with analogous variability in the brain of subjects with DS [34], [51].
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