International collaborative project to compare and track the nutritional composition of fast foods

Background
Chronic diseases are the leading cause of premature death and disability in the world with over-nutrition a primary cause of diet-related ill health. Excess quantities of energy, saturated fat, sugar and salt derived from fast foods contribute importantly to this disease burden. Our objective is to collate and compare nutrient composition data for fast foods as a means of supporting improvements in product formulation.
Methods/design
Surveys of fast foods will be done in each participating country each year. Information on the nutrient composition for each product will be sought either through direct chemical analysis, from fast food companies, in-store materials or from company websites. Foods will be categorized into major groups for the primary analyses which will compare mean levels of saturated fat, sugar, sodium, energy and serving size at baseline and over time. Countries currently involved include Australia, New Zealand, France, UK, USA, India, Spain, China and Canada, with more anticipated to follow.
Discussion
This collaborative approach to the collation and sharing of data will enable low-cost tracking of fast food composition around the world. This project represents a significant step forward in the objective and transparent monitoring of industry and government commitments to improve the quality of fast foods.

Fast growing self organizing map for text clustering

This paper presents an integration of a novel document vector representation technique and a novel Growing Self Organizing Process. In this new approach, documents are represented as a low dimensional vector, which is composed of the indices and weights derived from the keywords of the document.

An index based similarity calculation method is employed on this low dimensional feature space and the growing self organizing process is modified to comply with the new feature representation model.

The initial experiments show that this novel integration outperforms the state-of-the-art Self Organizing Map based techniques of text clustering in terms of its efficiency while preserving the same accuracy level.

Exploration strategies map along fast-slow metabolic and life-history continua in muroid rodents

1Personality is highly relevant to ecology and the evolution of fast–slow metabolic and life-history strategies. One of the most important personality traits is exploratory behaviour, usually measured on an animal introduced to a novel environment (e.g. open-field test).2Here, we use a unique comparative dataset on open-field exploratory behaviour of muroid rodents to test a key assumption of a recent evolutionary model, i.e. that exploration thoroughness is positively correlated to age at first reproduction (AFR). We then examine how AFR and exploratory behaviour are related to basal metabolic rate (BMR).3Inter-specific variation in exploratory behaviour was positively correlated with AFR. Both AFR and exploration behaviour were negatively correlated with BMR. These results remained significant when taking phylogeny into account.4We suggest that species occupying unproductive and unpredictable environments simultaneously benefit from high exploration, low BMR and delayed AFR because exploration increases the likelihood of finding scarce resources, whereas low BMR and delayed reproduction enhance survival during frequent resources shortages.5This study provides the first empirical evidence for a link between personality, life-history, phylogeny and energy metabolism at the inter-specific level. The superficial-thorough exploration continuum can be mapped along the fast–slow metabolic and life-history continua.