Influence of SNPs in nutrient-sensitive candidate genes and gene–diet interactions on blood lipids: the DiOGenes study

Blood lipid response to a given dietary intervention could be determined by the effect of diet, gene variants or gene–diet interactions. The objective of the present study was to investigate whether variants in presumed nutrient-sensitive genes involved in lipid metabolism modified lipid profile after weight loss and in response to a given diet, among overweight European adults participating in the Diet Obesity and Genes study. By multiple linear regressions, 240 SNPs in twenty-four candidate genes were investigated for SNP main and SNP–diet interaction effects on total cholesterol, LDL-cholesterol, HDL-cholesterol and TAG after an 8-week low-energy diet (only main effect), and a 6-month ad libitum weight maintenance diet, with different contents of dietary protein or glycaemic index. After adjusting for multiple testing, a SNP–dietary protein interaction effect on TAG was identified for lipin 1 (LPIN1) rs4315495, with a decrease in TAG of − 0·26 mmol/l per A-allele/protein unit (95 % CI − 0·38, − 0·14, P= 0·000043). In conclusion, we investigated SNP–diet interactions for blood lipid profiles for 240 SNPs in twenty-four candidate genes, selected for their involvement in lipid metabolism pathways, and identified one significant interaction between LPIN1 rs4315495 and dietary protein for TAG concentration.

Implicit and explicit processes in a second-language learning task

Two experiments examined the learning of a set of Greek pronunciation rules through explicit and implicit modes of rule presentation. Experiment 1 compared the effectiveness of implicit and explicit modes of presentation in two modalities, visual and auditory. Subjects in the explicit or rule group were presented with the rule set, and those in the implicit or natural group were shown a set of Greek words, composed of letters from the rule set, linked to their pronunciations. Subjects learned the Greek words to criterion and were then given a series of tests which aimed to tap different types of knowledge. The results showed an advantage of explicit study of the rules. In addition, an interaction was found between mode of presentation and modality. Explicit instruction was more effective in the visual than in the auditory modality, whereas there was no modality effect for implicit instruction. Experiment 2 examined a possible reason for the advantage of the rule groups by comparing different combinations of explicit and implicit presentation in the study and learning phases. The results suggested that explicit presentation of the rules is only beneficial when it is followed by practice at applying them.

Sparse multioutput radial basis function network construction using combined locally regularised orthogonal least square and D-optimality experimental design

A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced by combining a locally regularised orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximised model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious RBF network model with excellent generalisation performance. The D-optimality design criterion enhances the model efficiency and robustness. A further advantage of the combined approach is that the user only needs to specify a weighting for the D-optimality cost in the combined RBF model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Sparse kernel regression modeling using combined locally regularized orthogonal least squares and D-optimality experimental design

The note proposes an efficient nonlinear identification algorithm by combining a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design. The proposed algorithm aims to achieve maximized model robustness and sparsity via two effective and complementary approaches. The LROLS method alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Angular momentum conservation and gravity wave drag parameterization: implications for climate models

The robustness of the parameterized gravity wave response to an imposed radiative perturbation in the middle atmosphere is examined. When momentum is conserved and for reasonable gravity wave drag parameters, the response to a polar cooling induces polar downwelling above the region of the imposed cooling, with consequent adiabatic warming. This response is robust to changes in the gravity wave source spectrum, background flow, gravity wave breaking criterion, and model lid height. When momentum is not conserved, either in the formulation or in the implementation of the gravity wave drag parameterization, the response becomes sensitive to the above-mentioned factors—in particular to the model lid height. The spurious response resulting from nonconservation is found to be nonnegligible in terms of the total gravity wave drag–induced downwelling.

Candidate genes for obesity-susceptibility show enriched association within a large genome-wide association study for BMI

Before the advent of genome-wide association studies (GWASs), hundreds of candidate genes for obesity-susceptibility had been identified through a variety of approaches. We examined whether those obesity candidate genes are enriched for associations with body mass index (BMI) compared with non-candidate genes by using data from a large-scale GWAS. A thorough literature search identified 547 candidate genes for obesity-susceptibility based on evidence from animal studies, Mendelian syndromes, linkage studies, genetic association studies and expression studies. Genomic regions were defined to include the genes ±10 kb of flanking sequence around candidate and non-candidate genes. We used summary statistics publicly available from the discovery stage of the genome-wide meta-analysis for BMI performed by the genetic investigation of anthropometric traits consortium in 123 564 individuals. Hypergeometric, rank tail-strength and gene-set enrichment analysis tests were used to test for the enrichment of association in candidate compared with non-candidate genes. The hypergeometric test of enrichment was not significant at the 5% P-value quantile (P = 0.35), but was nominally significant at the 25% quantile (P = 0.015). The rank tail-strength and gene-set enrichment tests were nominally significant for the full set of genes and borderline significant for the subset without SNPs at P < 10(-7). Taken together, the observed evidence for enrichment suggests that the candidate gene approach retains some value. However, the degree of enrichment is small despite the extensive number of candidate genes and the large sample size. Studies that focus on candidate genes have only slightly increased chances of detecting associations, and are likely to miss many true effects in non-candidate genes, at least for obesity-related traits.

Progress in the genetics of common obesity and type 2 diabetes

The prevalence of obesity and diabetes, which are heritable traits that arise from the interactions of multiple genes and lifestyle factors, continues to rise worldwide, causing serious health problems and imposing a substantial economic burden on societies. For the past 15 years, candidate gene and genome-wide linkage studies have been the main genetic epidemiological approaches to identify genetic loci for obesity and diabetes, yet progress has been slow and success limited. The genome-wide association approach, which has become available in recent years, has dramatically changed the pace of gene discoveries. Genome-wide association is a hypothesis-generating approach that aims to identify new loci associated with the disease or trait of interest. So far, three waves of large-scale genome-wide association studies have identified 19 loci for common obesity and 18 for common type 2 diabetes. Although the combined contribution of these loci to the variation in obesity and diabetes risk is small and their predictive value is typically low, these recently identified loci are set to substantially improve our insights into the pathophysiology of obesity and diabetes. This will require integration of genetic epidemiological methods with functional genomics and proteomics. However, the use of these novel insights for genetic screening and personalised treatment lies some way off in the future.

Joint data assimilation of satellite reflectance and net ecosystem exchange data constrains ecosystem carbon fluxes at a high-elevation subalpine forest

We utilized an ecosystem process model (SIPNET, simplified photosynthesis and evapotranspiration model) to estimate carbon fluxes of gross primary productivity and total ecosystem respiration of a high-elevation coniferous forest. The data assimilation routine incorporated aggregated twice-daily measurements of the net ecosystem exchange of CO2 (NEE) and satellite-based reflectance measurements of the fraction of absorbed photosynthetically active radiation (fAPAR) on an eight-day timescale. From these data we conducted a data assimilation experiment with fifteen different combinations of available data using twice-daily NEE, aggregated annual NEE, eight-day f AP AR, and average annual fAPAR. Model parameters were conditioned on three years of NEE and fAPAR data and results were evaluated to determine the information content from the different combinations of data streams. Across the data assimilation experiments conducted, model selection metrics such as the Bayesian Information Criterion and Deviance Information Criterion obtained minimum values when assimilating average annual fAPAR and twice-daily NEE data. Application of wavelet coherence analyses showed higher correlations between measured and modeled fAPAR on longer timescales ranging from 9 to 12 months. There were strong correlations between measured and modeled NEE (R2, coefficient of determination, 0.86), but correlations between measured and modeled eight-day fAPAR were quite poor (R2 = −0.94). We conclude that this inability to determine fAPAR on eight-day timescale would improve with the considerations of the radiative transfer through the plant canopy. Modeled fluxes when assimilating average annual fAPAR and annual NEE were comparable to corresponding results when assimilating twice-daily NEE, albeit at a greater uncertainty. Our results support the conclusion that for this coniferous forest twice-daily NEE data are a critical measurement stream for the data assimilation. The results from this modeling exercise indicate that for this coniferous forest, average annuals for satellite-based fAPAR measurements paired with annual NEE estimates may provide spatial detail to components of ecosystem carbon fluxes in proximity of eddy covariance towers. Inclusion of other independent data streams in the assimilation will also reduce uncertainty on modeled values.

An open book: what and how young children learn from picture and story books

Looking at and listening to picture and story books is a ubiquitous activity, frequently enjoyed by many young children and their parents. Well before children can read for themselves they are able to learn from books. Looking at and listening to books increases children’s general knowledge, understanding about the world and promotes language acquisition. This collection of papers demonstrates the breadth of information pre-reading children learn from books and increases our understanding of the social and cognitive mechanisms that support this learning. Our hope is that this Research Topic/eBook will be useful for researchers as well as educational practitioners and parents who are interested in optimizing children’s learning. We conceptually divide this research topic into four broad sections, which focus on the nature and attributes of picture and story books, what children learn from picture and story books, the interactions children experience during shared reading, and potential applications of research into shared reading, respectively.

Dynamics of convectively driven banded jets in the laboratory

The banded organization of clouds and zonal winds in the atmospheres of the outer planets has long fascinated observers. Several recent studies in the theory and idealized modeling of geostrophic turbulence have suggested possible explanations for the emergence of such organized patterns, typically involving highly anisotropic exchanges of kinetic energy and vorticity within the dissipationless inertial ranges of turbulent flows dominated (at least at large scales) by ensembles of propagating Rossby waves. The results from an attempt to reproduce such conditions in the laboratory are presented here. Achievement of a distinct inertial range turns out to require an experiment on the largest feasible scale. Deep, rotating convection on small horizontal scales was induced by gently and continuously spraying dense, salty water onto the free surface of the 13-m-diameter cylindrical tank on the Coriolis platform in Grenoble, France. A “planetary vorticity gradient” or “β effect” was obtained by use of a conically sloping bottom and the whole tank rotated at angular speeds up to 0.15 rad s−1. Over a period of several hours, a highly barotropic, zonally banded large-scale flow pattern was seen to emerge with up to 5–6 narrow, alternating, zonally aligned jets across the tank, indicating the development of an anisotropic field of geostrophic turbulence. Using particle image velocimetry (PIV) techniques, zonal jets are shown to have arisen from nonlinear interactions between barotropic eddies on a scale comparable to either a Rhines or “frictional” wavelength, which scales roughly as (β/Urms)−1/2. This resulted in an anisotropic kinetic energy spectrum with a significantly steeper slope with wavenumber k for the zonal flow than for the nonzonal eddies, which largely follows the classical Kolmogorov k−5/3 inertial range. Potential vorticity fields show evidence of Rossby wave breaking and the presence of a “hyperstaircase” with radius, indicating instantaneous flows that are supercritical with respect to the Rayleigh–Kuo instability criterion and in a state of “barotropic adjustment.” The implications of these results are discussed in light of zonal jets observed in planetary atmospheres and, most recently, in the terrestrial oceans.

Innovative implementation of iLearn e-Portfolio for Personal Development Planning

iLearn is a Web 2.0 tool developed in Blackboard to help students with Personal Development Planning (PDP). This paper describes a case study on how the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP benefits the students. The e-Portfolio tool benefits students as it enables them to create and share portfolios, record achievements and reflections that support future job applications and promotion. Students find it beneficial because they can make use of iLearn e-Portfolio to keep academic records and achievements, activities and interests, work experience, reflective practice, employer information and some other useful resources, and also to tailor their CV and covering letters including evidence to support their CV, transferable skills and selling points. Useful information for preparing for an interview, reflecting after an event and any thoughts and evaluation can be kept in iLearn e-Portfolio. Keeping assessment and feedback records in iLearn e-Portfolio enables students to know their progress, to identify any gaps they need to fill to develop their study practices and areas for development. The key points from the feedback on the assignments and assessments are beneficial for future improvement. The reflections on the assignments and how students make use of the advice are particularly useful to improve their overall performance. In terms of pedagogical benefits, the “Individual Learner Profile” records and reviews evidence in verbal communication, basic and higher academic skills, time management, numeracy skill and IT skills, students become increasingly aware of their own strengths and any weaker areas that may require development. The e-Portfolio also provides opportunity for students to reflect on the experience and skills they have gained whilst participating in activities outside their studies. As the iLearn e-Portfolio is a reflective practice tool, it is consistent with the principle of Schon's reflective practitioner to reframe problems and to explore the consequences of actions. From the students’ feedback, for those who engage regularly in iLearn, they are better able to set agendas for their Personal Tutorial meetings and provide their Personal Tutor with a unique record of their achievements, skills and attributes which help them writing effective references for them. They make the most of their student experience in general. They also enhance their transferable skills and employability overall. The iLearn e-Portfolio prepares for the workplace and life beyond University including continuing professional development. Students are aware of their transferable skills, evidence of the skills and skill level, including award or accreditation, and their personal reflection on their transferable skills. It is beneficial for students to be aware of their transferable skills, to produce evidence of the skills and skills level such as award and accreditation, and to record their personal reflection on their transferable skills. Finally, the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP will improve their employability.

Innovative use of e-portfolio for personal development

iLearn is a quasi-Web 2.0 tool developed in Blackboard to help users with Personal Development Planning (PDP). This paper describes a case study on how the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP benefits the users, who are training to be professionals in construction management and surveying, The e-Portfolio tool benefits users as it enables them to create and share portfolios, record achievements and reflections that support future job applications and promotion. Users find it beneficial because they can make use of iLearn e-Portfolio to keep academic records and achievements, activities and interests, work experience, reflective practice, employer information and some other useful resources, and also to tailor their CV and covering letters including evidence to support their CV, transferable skills and selling points. Useful information for preparing for an interview, reflecting after an event and any thoughts and evaluation can be kept in iLearn e-Portfolio. Keeping assessment and feedback records in iLearn e-Portfolio enables learners to know their progress, to identify any gaps they need to fill to develop their study practices and areas for development. The key points from the feedback on the assignments and assessments are beneficial for future improvement. The reflections on the tasks and how they make use of the advice are particularly useful to improve their overall performance. In terms of pedagogical benefits, the “Individual Learner Profile” records and reviews evidence in verbal communication, basic and higher academic skills, time management, numeracy skill and IT skills, learners become increasingly aware of their own strengths and any weaker areas that may require development. The e-Portfolio also provides opportunity for them to reflect on the experience and skills they have gained whilst participating in activities outside their studies. As the iLearn e-Portfolio is a reflective practice tool, it is consistent with the principle of Schon's reflective practitioner to reframe problems and to explore the consequences of actions. From the users’ feedback, for those who engage regularly in iLearn, they are better able to set agendas for their supervision meetings and provide their supervisor with a unique record of their achievements, skills and attributes which help them writing effective references for them. They make the most of their learning experience in general. They also enhance their transferable skills and employability overall. The iLearn e-Portfolio prepares them for the workplace including continuing professional development. Users are aware of their transferable skills, evidence of the skills and skill level, including award or accreditation, and their personal reflection on their transferable skills. It is beneficial for them to be aware of their transferable skills, to produce evidence of the skills and skills level such as award and accreditation, and to record their personal reflection on their transferable skills. Finally, the innovative use of mobile digital technology in iLearn e-Portfolio for developing reflective portfolios for PDP will improve their employability.

A Bayesian approach to disease gene location using allelic association

A Bayesian approach to analysing data from family-based association studies is developed. This permits direct assessment of the range of possible values of model parameters, such as the recombination frequency and allelic associations, in the light of the data. In addition, sophisticated comparisons of different models may be handled easily, even when such models are not nested. The methodology is developed in such a way as to allow separate inferences to be made about linkage and association by including theta, the recombination fraction between the marker and disease susceptibility locus under study, explicitly in the model. The method is illustrated by application to a previously published data set. The data analysis raises some interesting issues, notably with regard to the weight of evidence necessary to convince us of linkage between a candidate locus and disease.

Implementation of microprocessor based self-tuning control on a heating system

This paper describes the implementation, using a microprocessor, of a self-tuning control algorithm on a heating system. The algorithm is based on recursive least squares parameter estimation with a state-space, pole placement design criterion and shows how the controller behaves when applied to an actual system.

GSHMC: an efficient method for molecular simulation

The hybrid Monte Carlo (HMC) method is a popular and rigorous method for sampling from a canonical ensemble. The HMC method is based on classical molecular dynamics simulations combined with a Metropolis acceptance criterion and a momentum resampling step. While the HMC method completely resamples the momentum after each Monte Carlo step, the generalized hybrid Monte Carlo (GHMC) method can be implemented with a partial momentum refreshment step. This property seems desirable for keeping some of the dynamic information throughout the sampling process similar to stochastic Langevin and Brownian dynamics simulations. It is, however, ultimate to the success of the GHMC method that the rejection rate in the molecular dynamics part is kept at a minimum. Otherwise an undesirable Zitterbewegung in the Monte Carlo samples is observed. In this paper, we describe a method to achieve very low rejection rates by using a modified energy, which is preserved to high-order along molecular dynamics trajectories. The modified energy is based on backward error results for symplectic time-stepping methods. The proposed generalized shadow hybrid Monte Carlo (GSHMC) method is applicable to NVT as well as NPT ensemble simulations.