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The cumulative effects of individual variations mount seriously.
The present study also identified effects of individual variations in trait anxiety and WM performance on brain activity in response to anxiety-inducing distraction.
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Methods must therefore account for the random effects of individual variation in a way not otherwise necessary.
For RNA sequencing, we attempted to minimise the effects of individual variation between animals by pooling RNA, as described by Paten et al. [ 35].
Rather than eliminate the effects of individual variation, its presence was statistically modeled and the variance partialed into variability that was due to differences between work groups and within the work group across individuals.
Measurement of total arsenic in drinking-water supplies is often used to assess arsenic exposure, but this approach is not appropriate for research questions pertaining to individual exposures, including research concerning the effects of individual variation in arsenic metabolism on internal dose.
The present study addressed these issues by using an adapted version of our WM task with distraction [18] that allowed investigation of the neural mechanisms that mediate cognitive interference by specific transient anxiety-inducing distracters, in conjunction with behavioural measures that assessed the effect of individual variations in both trait anxiety and cognitive performance.
For gene expression microarray analysis, we used pooled RNA to sample a larger number of individuals and to reduce the effect of individual variation.
We do not describe any further characteristics of the scanning control model, as this model only served to show the structuring effect of individual variation in scanning tendency.
Therefore, to assess the isolated effect of social vigilance (specifically scanning), we implemented a fourth model (scanning control model), where we eliminated the structuring effect of individual variation in fleeing and avoidance.
To test the effect of individual variation in the HIV infectiousness generated by co-infection, we developed and analyzed a mathematical sexual network model that simulates the behavioral components of a population from Malawi, as well as the dynamics of HIV and the co-infection effect caused by other infectious diseases, including herpes simplex virus type-2, gonorrhea, syphilis and malaria.
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