Physical activity in deprived communities in London: examining individual and neighbourhood-level factors

Introduction: The objectives of this study were to examine relationships between neighbourhood-level and individual-level characteristics and physical activity in deprived London neighbourhoods. Methods: In 40 of the most deprived neighbourhoods in London (ranked in top 11% in London by Index of Multiple Deprivation) a cross-sectional survey (n = 4107 adults aged > = 16 years), neighbourhood audit tool, GIS measures and routine data measured neighbourhood and individual-level characteristics. The binary outcome was meeting the minimum recommended (CMO, UK) 5×30 mins moderate physical activity per week. Multilevel modelling was used to examine associations between physical activity and individual and neighbourhood-level characteristics. Results: Respondents living more than 300 m away from accessible greenspace had lower odds of achieving recommended physical activity levels than those who lived within 300 m; from 301–600 m (OR = 0.7; 95% CI 0.5–0.9) and from 601–900 m (OR = 0.6; 95% CI 0.4–0.8). There was substantial residual between-neighbourhood variance in physical activity (median odds ratio = 1.7). Other objectively measured neighbourhood-level characteristics were not associated with physical activity levels. Conclusions Distance to nearest greenspace is associated with meeting recommended physical activity levels in deprived London neighbourhoods. Despite residual variance in physical activity levels between neighbourhoods, we found little evidence for the influence of other measured neighbourhood-level characteristics.

Brain responses to audiovisual speech mismatch in infants are associated with individual differences in looking behaviour

Research on audiovisual speech integration has reported high levels of individual variability, especially among young infants. In the present study we tested the hypothesis that this variability results from individual differences in the maturation of audiovisual speech processing during infancy. A developmental shift in selective attention to audiovisual speech has been demonstrated between 6 and 9 months with an increase in the time spent looking to articulating mouths as compared to eyes (Lewkowicz & Hansen-Tift. (2012) Proc. Natl Acad. Sci. USA, 109, 1431–1436; Tomalski et al. (2012) Eur. J. Dev. Psychol., 1–14). In the present study we tested whether these changes in behavioural maturational level are associated with differences in brain responses to audiovisual speech across this age range. We measured high-density event-related potentials (ERPs) in response to videos of audiovisually matching and mismatched syllables /ba/ and /ga/, and subsequently examined visual scanning of the same stimuli with eye-tracking. There were no clear age-specific changes in ERPs, but the amplitude of audiovisual mismatch response (AVMMR) to the combination of visual /ba/ and auditory /ga/ was strongly negatively associated with looking time to the mouth in the same condition. These results have significant implications for our understanding of individual differences in neural signatures of audiovisual speech processing in infants, suggesting that they are not strictly related to chronological age but instead associated with the maturation of looking behaviour, and develop at individual rates in the second half of the first year of life.

Physical Activity in Deprived Communities in London: Examining Individual and Neighbourhood-Level Factors

Introduction The objectives of this study were to examine relationships between neighbourhood-level and individual-level characteristics and physical activity in deprived London neighbourhoods. Methods In 40 of the most deprived neighbourhoods in London (ranked in top 11% in London by Index of Multiple Deprivation) a cross-sectional survey (n = 4107 adults aged > = 16 years), neighbourhood audit tool, GIS measures and routine data measured neighbourhood and individual-level characteristics. The binary outcome was meeting the minimum recommended (CMO, UK) 5×30 mins moderate physical activity per week. Multilevel modelling was used to examine associations between physical activity and individual and neighbourhood-level characteristics. Results Respondents living more than 300 m away from accessible greenspace had lower odds of achieving recommended physical activity levels than those who lived within 300 m; from 301–600 m (OR = 0.7; 95% CI 0.5–0.9) and from 601–900 m (OR = 0.6; 95% CI 0.4–0.8). There was substantial residual between-neighbourhood variance in physical activity (median odds ratio = 1.7). Other objectively measured neighbourhood-level characteristics were not associated with physical activity levels. Conclusions Distance to nearest greenspace is associated with meeting recommended physical activity levels in deprived London neighbourhoods. Despite residual variance in physical activity levels between neighbourhoods, we found little evidence for the influence of other measured neighbourhood-level characteristics.

Uncovering Dependence Clusters and Linchpin Functions

Dependence clusters are (maximal) collections of mutually dependent source code entities according to some dependence relation. Their presence in software complicates many maintenance activities including testing, refactoring, and feature extraction. Despite several studies finding them common in production code, their formation, identification, and overall structure are not well understood, partly because of challenges in approximating true dependences between program entities. Previous research has considered two approximate dependence relations: a fine-grained statement-level relation using control and data dependences from a program’s System Dependence Graph and a coarser relation based on function-level controlflow reachability. In principal, the first is more expensive and more precise than the second. Using a collection of twenty programs, we present an empirical investigation of the clusters identified by these two approaches. In support of the analysis, we consider hybrid cluster types that works at the coarser function-level but is based on the higher-precision statement-level dependences. The three types of clusters are compared based on their slice sets using two clustering metrics. We also perform extensive analysis of the programs to identify linchpin functions – functions primarily responsible for holding a cluster together. Results include evidence that the less expensive, coarser approaches can often be used as e�ective proxies for the more expensive, finer-grained approaches. Finally, the linchpin analysis shows that linchpin functions can be e�ectively and automatically identified.