RESUMO
Critical agency (CA) refers to an individual's feeling of power in relation to social inequalities. Research has demonstrated that high CA is associated with positive adolescent outcomes, however, less is known about what supports are important for its development. Moreover, a large majority of the literature is based on studies from the US and various countries in Africa; although the UK is saturated with inequalities there is little research within a UK context. In this paper we examine (a) the validity of using an existing measure of CA with a sample of UK adolescents and (b) the extent to which resilience supports account for variance in CA. Our analysis identified two distinct factors of CA: justice-oriented and community-oriented. High CA in both factors was explained by resilience supports associated with peer relationships (p < 0.01). Our findings push us towards new relational, ecological ways of understanding adolescent CA. We close by instantiating a translational framework for those devising policies in support of youth resilience and CA. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-023-04578-1.
RESUMO
BACKGROUND: IncP-1 plasmids are broad host range plasmids that have been found in clinical and environmental bacteria. They often carry genes for antibiotic resistance or catabolic pathways. The archetypal IncP-1 plasmid RK2 is a well-characterized biological system, with a fully sequenced and annotated genome and wide range of experimental measurements. Its central control operon, encoding two global regulators KorA and KorB, is a natural example of a negatively self-regulated operon. To increase our understanding of the regulation of this operon, we have constructed a dynamical mathematical model using Ordinary Differential Equations, and employed a Bayesian inference scheme, Markov Chain Monte Carlo (MCMC) using the Metropolis-Hastings algorithm, as a way of integrating experimental measurements and a priori knowledge. We also compared MCMC and Metabolic Control Analysis (MCA) as approaches for determining the sensitivity of model parameters. RESULTS: We identified two distinct sets of parameter values, with different biological interpretations, that fit and explain the experimental data. This allowed us to highlight the proportion of repressor protein as dimers as a key experimental measurement defining the dynamics of the system. Analysis of joint posterior distributions led to the identification of correlations between parameters for protein synthesis and partial repression by KorA or KorB dimers, indicating the necessary use of joint posteriors for correct parameter estimation. Using MCA, we demonstrated that the system is highly sensitive to the growth rate but insensitive to repressor monomerization rates in their selected value regions; the latter outcome was also confirmed by MCMC. Finally, by examining a series of different model refinements for partial repression by KorA or KorB dimers alone, we showed that a model including partial repression by KorA and KorB was most compatible with existing experimental data. CONCLUSIONS: We have demonstrated that the combination of dynamical mathematical models with Bayesian inference is valuable in integrating diverse experimental data and identifying key determinants and parameters for the IncP-1 central control operon. Moreover, we have shown that Bayesian inference and MCA are complementary methods for identification of sensitive parameters. We propose that this demonstrates generic value in applying this combination of approaches to systems biology dynamical modelling.