Clustering of lifestyle risk behaviours among residents of forty deprived neighbourhoods in London: lessons for targeting public health interventions

Background Clustering of lifestyle risk behaviours is very important in predicting premature mortality. Understanding the extent to which risk behaviours are clustered in deprived communities is vital to most effectively target public health interventions. Methods We examined co-occurrence and associations between risk behaviours (smoking, alcohol consumption, poor diet, low physical activity and high sedentary time) reported by adults living in deprived London neighbourhoods. Associations between sociodemographic characteristics and clustered risk behaviours were examined. Latent class analysis was used to identify underlying clustering of behaviours. Results Over 90% of respondents reported at least one risk behaviour. Reporting specific risk behaviours predicted reporting of further risk behaviours. Latent class analyses revealed four underlying classes. Membership of a maximal risk behaviour class was more likely for young, white males who were unable to work. Conclusions Compared with recent national level analysis, there was a weaker relationship between education and clustering of behaviours and a very high prevalence of clustering of risk behaviours in those unable to work. Young, white men who report difficulty managing on income were at high risk of reporting multiple risk behaviours. These groups may be an important target for interventions to reduce premature mortality caused by multiple risk behaviours.

Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

The Role of G Protein In Alcohol Related Behaviours Using Drosophila melanogaster As A Model

Ethanol, classified as a drug, affects the central nervous system, and its consumption has been linked to the development of several behaviours including tolerance and dependence. Alcohol tolerance is defined as the need for higher doses of alcohol to induce the same changes observed in the initial exposure or where repetitive exposures of the same alcohol dose induce a lower response. Ethanol has been shown to interact with numerous targets and ultimately influence both short and long term adaptation at the cellular and molecular level in brain [1]. These adaptation processes are likely to involve signalling molecules: our work has focussed on G proteins gene expression. Using both wild type and several mutant fruit fly (Drosophila melanogaster) as a model for behaviour and molecular studies, we observed significant increases in sedation time (ST50) in response to alcohol (P<0.001) Fig.A. We also observed a consistent and significant decrease of Gq protein mRNA expression in Drosophila dUNC and DopR2 mutants chronically exposed to alcohol (*P<0.05). Fig B. Method: Six male flies were observed in drosophila polystyrene 25 x 95mm transparent vial in between cotton plugs. To the top plug, 500uL of 100% ethanol was added. Time till 50% of the flies were sedated was recorded on each day following the schedule. Fig. C (n=4-6). Using RT-PCR, we also quantified G protein mRNA expression levels one hour post initial 30 minutes of ethanol expression on day 1 and day 3 relative to expression in naïve flies.(n=2) [A] Increase in sedation time indicative of tolerance in different mutant lines and wild type flies. Six male flies were used in each experiment and (n= 4-6. ***P<0.001 unpaired t tests). [B] RT-PCR results showing significant reduction in Gq mRNA in flies chronically exposed to alcohol. (n=2. *P<0.05) [C] Alcohol exposure schedule. (1) Kaun K.R., R. Azanchi, Z. Maung, J. Hirsh, U. Heberlein. (2011). A Drosophila model for alcohol reward. Nature Neuroscience. 14 (5), 612–619.