Understanding nutrition students' knowledge, perceived barriers and their views on the future role of nutritionists regarding sustainable diets

Nutrition professionals are important stakeholders in sustainable food systems with skills to promote the connection between health, food production, environment, culture and economics. Higher education institutions are increasingly recognising the importance of teaching about sustainability, yet there exists a gap in the literature detailing the awareness of sustainability issues by nutrition students. This study aimed to ascertain the level of knowledge of sustainable diets (SDs), the perceived barriers to their adoption in their own diets, students' experience of university‐based teaching about SDs and their views on the future role of the nutrition profession in relation to SD amongst nutrition students on Association for Nutrition (AfN)‐accredited degrees. The study assessed environmental and sustainable food literacy (SFL) through an online questionnaire and explored the issues in more detail in virtual or face‐to‐face interviews in 2019. Quantitative data were analysed using descriptive statistics (Kruskal‐Wallis, Jonckheere‐Terpstra, independent t‐test, Spearman, Pearson Correlations). Qualitative data were analysed using the Braun and Clark (2006) six‐step approach to thematic analysis. The questionnaire responses (n= 51) represented 17 AfN‐accredited undergraduate courses (35% of AfN‐accredited universities in 2019). The majority (76%) of students had received an introduction, partaken in a module or received teaching on SDs throughout their whole degree. Students were predominantly environmentally literate, yet had a fragmented understanding of SDs, focusing on the environmental aspects of SDs. There was no correlation between SFL and reported sustainability content of university courses, highlighting a need for more effective teaching on sustainability topics. Additionally, no relationship between self‐reported diet intake and SFL was found. Students identified a lack of knowledge and education as barriers preventing them from adopting sustainable practices in the present and future. To integrate sustainability into their future practice more consistently and effectively, nutrition students require more structured, holistic sustainability education and knowledge

Family composition and age at menarche: findings from the international Health Behaviour in School-Aged Children Study

This research was funded by The University of St Andrews and NHS Health Scotland.Background Early menarche has been associated with father absence, stepfather presence and adverse health consequences in later life. This article assesses the association of different family compositions with the age at menarche. Pathways are explored which may explain any association between family characteristics and pubertal timing. Methods Cross-sectional, international data on the age at menarche, family structure and covariates (age, psychosomatic complaints, media consumption, physical activity) were collected from the 2009–2010 Health Behaviour in School-aged Children (HBSC) survey. The sample focuses on 15-year old girls comprising 36,175 individuals across 40 countries in Europe and North America (N = 21,075 for age at menarche). The study examined the association of different family characteristics with age at menarche. Regression and path analyses were applied incorporating multilevel techniques to adjust for the nested nature of data within countries. Results Living with mother (Cohen’s d = .12), father (d = .08), brothers (d = .04) and sisters (d = .06) are independently associated with later age at menarche. Living in a foster home (d = −.16), with ‘someone else’ (d = −.11), stepmother (d = −.10) or stepfather (d = −.06) was associated with earlier menarche. Path models show that up to 89% of these effects can be explained through lifestyle and psychological variables. Conclusions Earlier menarche is reported amongst those with living conditions other than a family consisting of two biological parents. This can partly be explained by girls’ higher Body Mass Index in these families which is a biological determinant of early menarche. Lower physical activity and elevated psychosomatic complaints were also more often found in girls in these family environments.Publisher PDFPeer reviewe

Vortices in (2+1)d Conformal Fluids

We study isolated, stationary, axially symmetric vortex solutions in (2+1)-dimensional viscous conformal fluids. The equations describing them can be brought to the form of three coupled first order ODEs for the radial and rotational velocities and the temperature. They have a rich space of solutions characterized by the radial energy and angular momentum fluxes. We do a detailed study of the phases in the one-parameter family of solutions with no energy flux. This parameter is the product of the asymptotic vorticity and temperature. When it is large, the radial fluid velocity reaches the speed of light at a finite inner radius. When it is below a critical value, the velocity is everywhere bounded, but at the origin there is a discontinuity. We comment on turbulence, potential gravity duals, non-viscous limits and non-relativistic limits.Comment: 39 pages, 10 eps figures, v2: Minor changes, refs, preprint numbe

Selecting control genes for RT-QPCR using public microarray data

Background: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e. g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. Results: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/similar to vpopovic/research/ Conclusion: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable

Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains

The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concerns itself with questions about the expressiveness of formal languages for capturing knowledge about the world, together with proof systems for reasoning from such knowledge bases. The learning camp attempts to generalize from examples about partial descriptions about the world. In AI, historically, these camps have loosely divided the development of the field, but advances in cross-over areas such as statistical relational learning, neuro-symbolic systems, and high-level control have illustrated that the dichotomy is not very constructive, and perhaps even ill-formed. In this article, we survey work that provides further evidence for the connections between logic and learning. Our narrative is structured in terms of three strands: logic versus learning, machine learning for logic, and logic for machine learning, but naturally, there is considerable overlap. We place an emphasis on the following "sore" point: there is a common misconception that logic is for discrete properties, whereas probability theory and machine learning, more generally, is for continuous properties. We report on results that challenge this view on the limitations of logic, and expose the role that logic can play for learning in infinite domains