PEACH™ Queensland program improved child eating behaviours and reduced BMI z-score for overweight children (pilot study)

Background/Aims: Parenting, Eating and Activity for Child Health (PEACH™) Queensland is a 6-month family-focussed child weight management program currently targeting 1400 Queensland children. This study aims to investigate changes in child BMI and eating behaviours during the pilot phase. Methods: From 2013 – 2014, 251 overweight children (US-CDC BMI percentile ≥ 85th) were enrolled in the pilot phase of PEACH™ Queensland, using pre-defined inclusion criteria which included having a child above a healthy weight for their age (5 – 11 years). Baseline and follow-up data were collected from parents by questionnaire and included child eating behaviours, measured by core food intake and Children’s Dietary Questionnaire (CDQ). Children attending sessions were weighed and measured by a trained facilitator. Results: Children attending sessions were 80% obese (IOTF cut-points), 60% female, with average age of 9.0 ± 1.9 years. For the subset of children with complete anthropometry (n = 69), we observed significant decreases in US-CDC z-scores for weight and BMI, 2.3 ± 0.7 to 2.2 ± 0.7 (p < 0.001) and 2.2 ± 0.5 to 2.1 ± 0.7 (p < 0.0001), respectively. This reduction in BMI z-score was accompanied by increases in the proportion meeting recommendations for serves of fruit and vegetables, and significantly lower scores for sweetened beverages and discretionary food intake as measured by CDQ. Conclusions: The PEACH™ Queensland pilot reduced child weight and BMI z-scores, and improved eating behaviours (increased fruit and vegetable intake, decreased intake of discretionary foods and sweetened beverages) although there is need for further improvement

Time of year, age class and body condition predict Hendra virus infection in Australian black flying foxes (Pteropus alecto)

Hendra virus (HeV) continues to cause fatal infection in horses and threaten infection in close-contact humans in eastern Australia. Species of Pteropus bats (flying-foxes) are the natural reservoir of the virus. We caught and sampled flying-foxes from a multispecies roost in southeast Queensland, Australia on eight occasions between June 2013 and June 2014. The effects of sample date, species, sex, age class, body condition score (BCS), pregnancy and lactation on HeV antibody prevalence, log-transformed median fluorescent intensity (lnMFI) values and HeV RNA status were assessed using unbalanced generalised linear models. A total of 1968 flying-foxes were sampled, comprising 1012 Pteropus alecto, 742 P. poliocephalus and 214 P. scapulatus. Sample date, species and age class were each statistically associated with HeV RNA status, antibody status and lnMFI values; BCS was statistically associated with HeV RNA status and antibody status. The findings support immunologically naïve sub-adult P. alecto playing an important role in maintaining HeV infection at a population level. The biological significance of the association between BCS and HeV RNA status, and BCS and HeV antibody status, is less clear and warrants further investigation. Contrary to previous studies, we found no direct association between HeV infection and pregnancy or lactation. The findings in P. poliocephalus suggest that HeV exposure in this species may not result in systemic infection and virus excretion, or alternatively, may reflect assay cross-reactivity with another (unidentified) henipavirus. © The Author(s) 2019

Social disparities in food preparation behaviours: a DEDIPAC study

BACKGROUND: The specific role of major socio-economic indicators in influencing food preparation behaviours could reveal distinct socio-economic patterns, thus enabling mechanisms to be understood that contribute to social inequalities in health. This study investigated whether there was an independent association of each socio-economic indicator (education, occupation, income) with food preparation behaviours. METHODS: A total of 62,373 adults participating in the web-based NutriNet-Santé cohort study were included in our cross-sectional analyses. Cooking skills, preparation from scratch and kitchen equipment were assessed using a 0-10-point score; frequency of meal preparation, enjoyment of cooking and willingness to cook better/more frequently were categorical variables. Independent associations between socio-economic factors (education, income and occupation) and food preparation behaviours were assessed using analysis of covariance and logistic regression models stratified by sex. The models simultaneously included the three socio-economic indicators, adjusting for age, household composition and whether or not they were the main cook in the household. RESULTS: Participants with the lowest education, the lowest income group and female manual and office workers spent more time preparing food daily than participants with the highest education, those with the highest income and managerial staff (P < 0.0001). The lowest educated individuals were more likely to be non-cooks than those with the highest education level (Women: OR = 3.36 (1.69;6.69); Men: OR = 1.83 (1.07;3.16)) while female manual and office workers and the never-employed were less likely to be non-cooks (OR = 0.52 (0.28;0.97); OR = 0.30 (0.11;0.77)). Female manual and office workers had lower scores of preparation from scratch and were less likely to want to cook more frequently than managerial staff (P < 0.001 and P < 0.001). Women belonging to the lowest income group had a lower score of kitchen equipment (P < 0.0001) and were less likely to enjoy cooking meal daily (OR = 0.68 (0.45;0.86)) than those with the highest income. CONCLUSION: Lowest socio-economic groups, particularly women, spend more time preparing food than high socioeconomic groups. However, female manual and office workers used less raw or fresh ingredients to prepare meals than managerial staff. In the unfavourable context in France with reduced time spent preparing meals over last decades, our findings showed socioeconomic disparities in food preparation behaviours in women, whereas few differences were observed in men

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Echos italiens à Genève au début du XVIIIe siècle: le cas de la correspondance de Jean-Alphonse Turrettini

The chapter examines the alignment of science inquiry skills to the information literacy framework (Bent & Stubbings, 2011) and considers how these skills were supported by a range of web 2.0 tools using the TPACK framework to create an on-line scaffolded inquiry in two school contexts. Two case studies of inquiry around the pedagogy of ICT skills are examined here; one study in six grade seven high school classes and the other in a single extension group of twenty-five primary students from grade five to grade seven. In addition the affordances and limitations of the technology tools are assessed to determine how the scaffolded on-line inquiry process could be implemented in schools. In conclusion the chapter describes how in these cases, scaffolded on-line inquiry provides an opportunity for students to create an authentic, rich and detailed inquiry around their focus utilising a range of ICT tools and strategies