Consumption patterns of sweet drinks in a population of Australian children and adolescents (2003–2008)

<p>Abstract</p> <p>Background</p> <p>Intake of sweet drinks has previously been associated with the development of overweight and obesity among children and adolescents. The present study aimed to assess the consumption pattern of sweet drinks in a population of children and adolescents in Victoria, Australia.</p> <p>Methods</p> <p>Data on 1,604 children and adolescents (4–18 years) from the comparison groups of two quasi-experimental intervention studies from Victoria, Australia were analysed<it>.</it> Sweet drink consumption (soft drink and fruit juice/cordial) was assessed as one day’s intake and typical intake over the last week or month at two time points between 2003 and 2008 (mean time between measurement: 2.2 years).</p> <p>Results</p> <p>Assessed using dietary recalls, more than 70% of the children and adolescents consumed sweet drinks, with no difference between age groups (p = 0.28). The median intake among consumers was 500 ml and almost a third consumed more than 750 ml per day. More children and adolescents consumed fruit juice/cordial (69%) than soft drink (33%) (p < 0.0001) and in larger volumes (median intake fruit juice/cordial: 500 ml and soft drink: 375 ml). Secular changes in sweet drink consumption were observed with a lower proportion of children and adolescents consuming sweet drinks at time 2 compared to time 1 (significant for age group 8 to <10 years, p = 0.001).</p> <p>Conclusion</p> <p>The proportion of Australian children and adolescents from the state of Victoria consuming sweet drinks has been stable or decreasing, although a high proportion of this sample consumed sweet drinks, especially fruit juice/cordial at both time points.</p

Optimality Driven Nearest Centroid Classification from Genomic Data

Nearest-centroid classifiers have recently been successfully employed in high-dimensional applications, such as in genomics. A necessary step when building a classifier for high-dimensional data is feature selection. Feature selection is frequently carried out by computing univariate scores for each feature individually, without consideration for how a subset of features performs as a whole. We introduce a new feature selection approach for high-dimensional nearest centroid classifiers that instead is based on the theoretically optimal choice of a given number of features, which we determine directly here. This allows us to develop a new greedy algorithm to estimate this optimal nearest-centroid classifier with a given number of features. In addition, whereas the centroids are usually formed from maximum likelihood estimates, we investigate the applicability of high-dimensional shrinkage estimates of centroids. We apply the proposed method to clinical classification based on gene-expression microarrays, demonstrating that the proposed method can outperform existing nearest centroid classifiers

Non-Parametric Change-Point Method for Differential Gene Expression Detection

We proposed a non-parametric method, named Non-Parametric Change Point Statistic (NPCPS for short), by using a single equation for detecting differential gene expression (DGE) in microarray data. NPCPS is based on the change point theory to provide effective DGE detecting ability.NPCPS used the data distribution of the normal samples as input, and detects DGE in the cancer samples by locating the change point of gene expression profile. An estimate of the change point position generated by NPCPS enables the identification of the samples containing DGE. Monte Carlo simulation and ROC study were applied to examine the detecting accuracy of NPCPS, and the experiment on real microarray data of breast cancer was carried out to compare NPCPS with other methods.Simulation study indicated that NPCPS was more effective for detecting DGE in cancer subset compared with five parametric methods and one non-parametric method. When there were more than 8 cancer samples containing DGE, the type I error of NPCPS was below 0.01. Experiment results showed both good accuracy and reliability of NPCPS. Out of the 30 top genes ranked by using NPCPS, 16 genes were reported as relevant to cancer. Correlations between the detecting result of NPCPS and the compared methods were less than 0.05, while between the other methods the values were from 0.20 to 0.84. This indicates that NPCPS is working on different features and thus provides DGE identification from a distinct perspective comparing with the other mean or median based methods

Synchronized turbo apoptosis induced by cold-shock

In our research on the role of apoptosis in the pathogenesis of the autoimmune disease systemic lupus erythematosus (SLE), we aim to evaluate the effects of early and late apoptotic cells and blebs on antigen presenting cells. This requires the in vitro generation of sufficiently large and homogeneous populations of early and late apoptotic cells. Here, we present a quick method encountered by serendipity that results in highly reproducible synchronized homogeneous apoptotic cell populations. In brief, granulocytic 32Dcl3 cells are incubated on ice for 2 h and subsequently rewarmed at 37°C. After 30–90 min at 37°C more than 80–90% of the cells become early apoptotic (Annexin V positive/propidium iodide negative). After 24 h of rewarming at 37°C 98% of the cells were late apoptotic (secondary necrotic; Annexin V positive/propidium iodide positive). Cells already formed apoptotic blebs at their cell surface after approximately 20 min at 37°C. Inter-nucleosomal chromatin cleavage and caspase activation were other characteristics of this cold-shock-induced process of apoptosis. Consequently, apoptosis could be inhibited by a caspase inhibitor. Finally, SLE-derived anti-chromatin autoantibodies showed a high affinity for apoptotic blebs generated by cold-shock. Overall, cold-shock induced apoptosis is achieved without the addition of toxic compounds or antibodies, and quickly leads to synchronized homogeneous apoptotic cell populations, which can be applied for various research questions addressing apoptosis

Applying Budd's model to partnership

Over the last decade, the notions of workplace partnership and labour-management cooperation have resulted in distinctive and vociferous debates regarding forms of employee voice in the UK. It is proposed that there is a need to reconsider how we actually evaluate both the process and outcomes of partnership. This article reports on detailed case studies conducted in three diverse banking organizations in order to understand more about the process and outcomes of partnership. The study then applies the 'efficiency, equity, voice' framework developed by Budd, which has not been widely employed in industrial relations research. Accordingly, the article examines if and how partnership contributes to the balancing of efficiency, equity and voice. Judged in this light, the case studies demonstrate various degrees of success in terms of the extent to which partnership has facilitated voice and promoted more considered decision-making, for both management and employees. The article also demonstrates the usefulness of the Budd framework as a device in analysing employment relations processes and outcomes

The impact of human capital on the early success of necessity versus opportunity-based entrepreneurs

This paper examines whether founders' backgrounds influence new firm survival in the early years after startup, focusing, in particular, on the impact of unemployment-driven entrepreneurship. For entrepreneurs who left their previous employment to found a new firm, both general and specific human capital play a key role in enhancing early survival chances. However, various forms of human capital have little effect on early survival of unemployment-driven entrepreneurs, who rely mostly on previous entrepreneurial experience to persevere. Results suggest that pre-entry capabilities play an important role in the early success of opportunity-based entrepreneurs, but have little influence on the early success of necessity-based ones

The impact of human capital on the early success of necessity versus opportunity-based entrepreneurs

This paper examines whether founders' backgrounds influence new firm survival in the early years after startup, focusing, in particular, on the impact of unemployment-driven entrepreneurship. For entrepreneurs who left their previous employment to found a new firm, both general and specific human capital play a key role in enhancing early survival chances. However, various forms of human capital have little effect on early survival of unemployment-driven entrepreneurs, who rely mostly on previous entrepreneurial experience to persevere. Results suggest that pre-entry capabilities play an important role in the early success of opportunity-based entrepreneurs, but have little influence on the early success of necessity-based ones

Radio emission from Supernova Remnants

The explosion of a supernova releases almost instantaneously about 10^51 ergs of mechanic energy, changing irreversibly the physical and chemical properties of large regions in the galaxies. The stellar ejecta, the nebula resulting from the powerful shock waves, and sometimes a compact stellar remnant, constitute a supernova remnant (SNR). They can radiate their energy across the whole electromagnetic spectrum, but the great majority are radio sources. Almost 70 years after the first detection of radio emission coming from a SNR, great progress has been achieved in the comprehension of their physical characteristics and evolution. We review the present knowledge of different aspects of radio remnants, focusing on sources of the Milky Way and the Magellanic Clouds, where the SNRs can be spatially resolved. We present a brief overview of theoretical background, analyze morphology and polarization properties, and review and critical discuss different methods applied to determine the radio spectrum and distances. The consequences of the interaction between the SNR shocks and the surrounding medium are examined, including the question of whether SNRs can trigger the formation of new stars. Cases of multispectral comparison are presented. A section is devoted to reviewing recent results of radio SNRs in the Magellanic Clouds, with particular emphasis on the radio properties of SN 1987A, an ideal laboratory to investigate dynamical evolution of an SNR in near real time. The review concludes with a summary of issues on radio SNRs that deserve further study, and analyzing the prospects for future research with the latest generation radio telescopes.Comment: Revised version. 48 pages, 15 figure

Analyzing 2D gel images using a two-component empirical bayes model

<p>Abstract</p> <p>Background</p> <p>Two-dimensional polyacrylomide gel electrophoresis (2D gel, 2D PAGE, 2-DE) is a powerful tool for analyzing the proteome of a organism. Differential analysis of 2D gel images aims at finding proteins that change under different conditions, which leads to large-scale hypothesis testing as in microarray data analysis. Two-component empirical Bayes (EB) models have been widely discussed for large-scale hypothesis testing and applied in the context of genomic data. They have not been implemented for the differential analysis of 2D gel data. In the literature, the mixture and null densities of the test statistics are estimated separately. The estimation of the mixture density does not take into account assumptions about the null density. Thus, there is no guarantee that the estimated null component will be no greater than the mixture density as it should be.</p> <p>Results</p> <p>We present an implementation of a two-component EB model for the analysis of 2D gel images. In contrast to the published estimation method, we propose to estimate the mixture and null densities simultaneously using a constrained estimation approach, which relies on an iteratively re-weighted least-squares algorithm. The assumption about the null density is naturally taken into account in the estimation of the mixture density. This strategy is illustrated using a set of 2D gel images from a factorial experiment. The proposed approach is validated using a set of simulated gels.</p> <p>Conclusions</p> <p>The two-component EB model is a very useful for large-scale hypothesis testing. In proteomic analysis, the theoretical null density is often not appropriate. We demonstrate how to implement a two-component EB model for analyzing a set of 2D gel images. We show that it is necessary to estimate the mixture density and empirical null component simultaneously. The proposed constrained estimation method always yields valid estimates and more stable results. The proposed estimation approach proposed can be applied to other contexts where large-scale hypothesis testing occurs.</p