Young children's food brand knowledge. Early development and associations with television viewing and parent's diet

Brand knowledge is a prerequisite of children's requests and choices for branded foods. We explored the development of young children's brand knowledge of foods highly advertised on television - both healthy and less healthy. Participants were 172 children aged 3-5 years in diverse socio-economic settings, from two jurisdictions on the island of Ireland with different regulatory environments. Results indicated that food brand knowledge (i) did not differ across jurisdictions; (ii) increased significantly between 3 and 4 years; and (iii) children had significantly greater knowledge of unhealthy food brands, compared with similarly advertised healthy brands. In addition, (iv) children's healthy food brand knowledge was not related to their television viewing, their mother's education, or parent or child eating. However, (v) unhealthy brand knowledge was significantly related to all these factors, although only parent eating and children's age were independent predictors. Findings indicate that effects of food marketing for unhealthy foods take place through routes other than television advertising alone, and are present before pre-schoolers develop the concept of healthy eating. Implications are that marketing restrictions of unhealthy foods should extend beyond television advertising; and that family-focused obesity prevention programmes should begin before children are 3 years of age.

Associations between Achievement Goal Orientations and Academic Performance Among Students at a UK Pharmacy School

Objective. To ascertain goal orientations of pharmacy students and establish whether associations exist between academic performance, gender, or year of study. Methods. Goal orientations were assessed using a validated questionnaire. Respondents were categorized as high or low performers based on university grades. Associations and statistical significance were ascertained using parametric and nonparametric tests and linear regression, as appropriate. Results. A response rate of 60.7% was obtained. High performers were more likely to be female than male. The highest mean score was for mastery approach; the lowest for work avoidance. The mean score for work avoidance was significantly greater for low performers than for high performers and for males than for females. First-year students were most likely to have top scores in mastery and performance approaches. Conclusion. It is encouraging that the highest mean score was for mastery approach orientation, as goal orientation may play a role in academic performance of pharmacy students.

Family-based associations in measures of psychological distress and quality of life in a cardiac screening clinic for inheritable cardiac diseases: a cross-sectional study

BACKGROUND: Family-based cardiac screening programmes for persons at risk for genetic cardiac diseases are now recommended. However, the psychological wellbeing and health related quality of life (QoL) of such screened patients is poorly understood, especially in younger patients. We sought to examine wellbeing and QoL in a representative group of adults aged 16 and over in a dedicated family cardiac screening clinic.

METHODS: Prospective survey of consecutive consenting patients attending a cardiac screening clinic, over a 12 month period. Data were collected using two health measurement tools: the Short Form 12 (version 2) and the Hospital Anxiety and Depression Scale (HADS), along with baseline demographic and screening visit-related data. The HADS and SF-12v.2 outcomes were compared by age group. Associations with a higher HADS score were examined using logistic regression, with multi-level modelling used to account for the family-based structure of the data.

RESULTS: There was a study response rate of 86.6%, with n=334 patients providing valid HADS data (valid response rate 79.5%), and data on n=316 retained for analysis. One-fifth of patients were aged under 25 (n=61). Younger patients were less likely than older to describe significant depression on their HADS scale (p<0.0001), although there were overall no difference between the prevalence of a significant HADS score between the younger and older age groups (18.0% vs 20.0%, p=0.73). Significant positive associates of a higher HADS score were having lower educational attainment, being single or separated, and being closely related to the family proband. Between-family variance in anxiety and depression scores was greater than within-family variance.

CONCLUSIONS: High levels of anxiety were seen amongst patients attending a family-based cardiac screening clinic.Younger patients also had high rates of clinically significant anxiety. Higher levels of anxiety and depression tends to run in families, and this has implications for family screening and intervention programmes.

Partitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs () across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of  from imputed SNPs (5.1× enrichment; p = 3.7 × 10⁻¹⁷) and 38% (SE = 4%) of  from genotyped SNPs (1.6× enrichment, p = 1.0 × 10⁻⁴). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of  despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.

Boost your word power: Katherine Rogers and Maggie Bennett describe a tool that can help students prepare their life science assignments

Anatomy and physiology refers to the structure and function of the human body. Students studying this module, known as life science or bioscience, develop knowledge of the structure (anatomy) and function (physiology) of the body that can be applied in clinical practice and support other study courses.

A new statistical framework for the quantification of covariate associations with species distributions

Identifying processes that shape species geographical ranges is a prerequisite for understanding environmental change. Currently, species distribution modelling methods do not offer credible statistical tests of the relative influence of climate factors and typically ignore other processes (e.g. biotic interactions and dispersal limitation). We use a hierarchical model fitted with Markov Chain Monte Carlo to combine ecologically plausible niche structures using regression splines to describe unimodal but potentially skewed response terms. We apply spatially explicit error terms that account for (and may help identify) missing variables. Using three example distributions of European bird species, we map model results to show sensitivity to change in each covariate. We show that the overall strength of climatic association differs between species and that each species has considerable spatial variation in both the strength of the climatic association and the sensitivity to climate change. Our methods are widely applicable to many species distribution modelling problems and enable accurate assessment of the statistical importance of biotic and abiotic influences on distributions.

The idea, the machine, and the art: word and image in the 21st century

Taking in recent advances in neuroscience and digital technology, Gander and Garland assess the state of the inter-arts in America and the Western world, exploring and questioning the primacy of affect in an increasingly hypertextual everyday environment. In this analysis they signal a move beyond W. J. T. Mitchell’s coinage of the ‘imagetext’ to an approach that centres the reader-viewer in a recognition, after John Dewey, of ‘art as experience’. New thinking in cognitive and computer sciences about the relationship between the body and the mind challenges any established definitions of ‘embodiment’, ‘materiality’, ‘virtuality’ and even ‘intelligence, they argue, whilst ‘Extended Mind Theory’, they note, marries our cognitive processes with the material forms with which we engage, confirming and complicating Marshall McLuhan’s insight, decades ago, that ‘all media are “extensions of man”’. In this chapter, Gander and Garland open paths and suggest directions into understandings and critical interpretations of new and emerging imagetext worlds and experiences.

Combining environmental and medical datasets to explore potential associations between environmental factors and health: Policy implications for human health risk assessments

This review paper discusses the use of Tellus and Tellus Border soil and stream geochemistry data to investigate the relationship between medical data and naturally occurring background levels of potentially toxic elements (PTEs) such as heavy metals in soils and water. The research hypothesis is that long-term low level oral exposure of PTEs via soil and water may result in cumulative exposures that may act as risk factors for progressive diseases including cancer and chronic kidney disease. A number of public policy implications for regional human health risk assessments, public health policy and education are also explored alongside the argument for better integration of multiple data sets to enhance ongoing medical and social research. This work presents a partnership between the School of Geography, Archaeology and Palaeoecology, Northern Ireland Cancer Registry, Queen’s University Belfast, and the nephrology (kidney medicine) research group.

Interpretable and Reconfigurable Clustering of Document Datasets by Deriving Word-based Rules

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein.

Generating a Word-Emotion Lexicon from #Emotional Tweets

Research in emotion analysis of text suggest that emotion lexicon based features are superior to corpus based n-gram features. However the static nature of the general purpose emotion lexicons make them less suited to social media analysis, where the need to adopt to changes in vocabulary usage and context is crucial. In this paper we propose a set of methods to extract a word-emotion lexicon automatically from an emotion labelled corpus of tweets. Our results confirm that the features derived from these lexicons outperform the standard Bag-of-words features when applied to an emotion classification task. Furthermore, a comparative analysis with both manually crafted lexicons and a state-of-the-art lexicon generated using Point-Wise Mutual Information, show that the lexicons generated from the proposed methods lead to significantly better classi- fication performance.