Exercise Participation during Weight Loss on a High Protein – Low Carbohydrate Diet Plan in Females Aged 15-25 Years

Weight gain due to poor diet and lack of exercise is responsible for over 300,000 deaths each year (U.S. Department of Health and Human Services, 2010). Obese adults have an increased risk for serious health conditions including high blood pressure and cholesterol, type 2 diabetes, coronary heart disease, stroke, gallbladder disease, osteoarthritis, sleep apnea, respiratory problems, and certain cancers (National Cancer Institute, 2012). Participation in exercise can help control weight, strengthen muscles and bones, and reduce the incidence of cardiac events, stroke, hypertension, type 2 diabetes, colon and breast cancers, osteoporotic fractures, gallbladder disease, obesity, depression, anxiety, and delay mortality (ACSM, 2009). The purpose of this study was to determine the effectiveness of exercise participation during weight loss on a high protein-low carbohydrate diet plan during a 12-week span in females aged 15 to 25 years. Specifically, this research study was a comparison of markers of health such as weight, fat mass, percent body fat, and fat-free mass in females who consistently exercised during the diet (Exercisers) from those who did not participate in consistent exercise (Non- Exercisers). The population in this study was selected due to the transition from high school to college being a critical period because it is associated with many identity choices and lifestyle changes that can lead to weight gain (Anderson, Shapiro, & Lundgren, 2003). The data indicate participation in regular exercise, while consuming a high protein-low carbohydrate diet plan, increases the loss of body weight, fat mass, and percent body fat when compared to participating in the diet plan alone. There was no significant difference in fat-free mass reduction between the groups. One implication for practice is recommending moderate to vigorous exercise for a minimum of 30 minutes at a time, totaling a minimum of 150 minutes per week, for females trying to achieve weight loss. Based from the results of this research study, in order to achieve a greater amount of body weight, fat mass, and percent body fat reduction one should consider incorporating exercise participation and high protein-low carbohydrate dieting into their weight loss plan

Toward self-learning model-based EAs

Model-based evolutionary algorithms (MBEAs) are praised for their broad applicability to black-box optimization problems. In practical applications however, they are mostl

Reliable Single Chip Genotyping with Semi-Parametric Log-Concave Mixtures

The common approach to SNP genotyping is to use (model-based) clustering per individual SNP, on a set of arrays. Genotyping all SNPs on a single array is much more attractive, in terms of flexibility, stability and applicability, when developing new chips. A new semi-parametric method, named SCALA, is proposed. It is based on a mixture model using semi-parametric log-concave densities. Instead of using the raw data, the mixture is fitted on a two-dimensional histogram, thereby making computation time almost independent of the number of SNPs. Furthermore, the algorithm is effective in low-MAF situations. Comparisons between SCALA and CRLMM on HapMap genotypes show very reliable calling of single arrays. Some heterozygous genotypes from HapMap are called homozygous by SCALA and to lesser extent by CRLMM too. Furthermore, HapMap's NoCalls (NN) could be genotyped by SCALA, mostly with high probability. The software is available as R scripts from the website www.math.leidenuniv.nl/~rrippe

Why does healthcare utilisation differ between socioeconomic groups in OECD countries with universal healthcare coverage?:A protocol for a systematic review

Introduction: Even in advanced economies with universal healthcare coverage (UHC), a social gradient in healthcare utilisation has been reported. Many individual, community and healthcare system factors have been considered that may be associated with the variation in healthcare utilisation between socioeconomic groups. Nevertheless, relatively little is known about the complex interaction and relative contribution of these factors to socioeconomic differences in healthcare utilisation. In order to improve understanding of why utilisation patterns differ by socioeconomic status (SES), the proposed systematic review will explore the main mechanisms that have been examined in quantitative research. Methods and analysis: The systematic review will follow the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines and will be conducted in Embase, PubMed, Scopus, Web of Science, Econlit and PsycInfo. Articles examining factors associated with the differences in primary and specialised healthcare utilisation between socioeconomic groups in Organisation for Economic Co-operation and Development (OECD) countries with UHC will be included. Further restrictions concern specifications of outcome measures, factors of interest, study design, population, language and type of publication. Data will be numerically summarised, narratively synthesised and thematically discussed. The factors will be categorised according to existing frameworks for barriers to healthcare access

GAM-based individual difference measures for L2 ERP studies

ERPs (Event-Related Potentials) have become a widely-used measure to study second language (L2) processing. To study individual differences, traditionally a component outcome measure is calculated by averaging the amplitude of a participant's brain response in a pre-specified time window of the ERP waveform in different conditions (e.g., the ‘Response Magnitude Index’; Tanner, Mclaughlin, Herschensohn & Osterhout, 2013). This approach suffers from the problem that the definition of such time windows is rather arbitrary, and that the result is sensitive to outliers as well as participant variation in latency. The latter is particularly problematic for studies on L2 processing. Furthermore, the size of the ERP response (i.e., amplitude difference) of an L2 speaker may not be the best indicator of near-native proficiency, as native speakers also show a great deal of variability in this respect, with the ‘robustness’ of an L2 speaker's ERP response (i.e., how consistently they show an amplitude difference) potentially being a more useful indicator. In this paper we introduce a novel method for the extraction of a set of individual difference measures from ERP waveforms. Our method is based on participants’ complete waveforms for a given time series, modelled using generalized additive modelling (GAM; Wood, 2017). From our modelled waveform, we extract a set of measures which are based on amplitude, area and peak effects. We illustrate the benefits of our method compared to the traditional Response Magnitude Index with data on the processing of grammatical gender violations in 66 Slavic L2 speakers of German and 29 German native speakers. One of our measures in particular appears to outperform the others in characterizing differences between native speakers and L2 speakers, and captures proficiency differences between L2 speakers: the ‘Normalized Modelled Peak’. This measure reflects the height of the (modelled) peak, normalized against the uncertainty of the modelled signal, here in the P600 search window. This measure may be seen as a measure of peak robustness, that is, how reliable the individual is able to show a P600 effect, largely independently of where in the P600 window this occurs. We discuss implications of our results and offer suggestions for future studies on L2 processing. The code to implement these analyses is available for other researchers

Variation in growth and stem quality among and within provenances of sycamore (Acer pseudoplatanus) in Denmark

Sycamore currently covers around 2% of the Danish forest cover and is an economic attractive species that is also interesting for ecological reasons. This thesis analyses a provenance and progeny trial series in Denmark in order to get an overview of suitable provenances for Denmark, the importance of provenance selection and possible breeding gains. The trial is of planted 2 year old sycamore measured after 9 years and consist out of 16 provenances. Experimental blocks on three sites were measured for the height, dbh straightness and forking behaviour. This data was analysed for significant differences between provenances and for heritability. The results found are that there are significant differences between provenances regarding height growth, diameter growth, stem straightness and forking frequency below 130 centimetres. Three provenances seem promising for the growth conditions in Denmark. Furthermore the analysis shows that there is heritability for height, diameter and stem straightness and that genetic gains for these traits are possible

Optimal Scaling transformations to model non-linear relations in GLMs with ordered and unordered predictors

In Generalized Linear Models (GLMs) it is assumed that there is a linear effect of the predictor variables on the outcome. However, this assumption is often too strict, because in many applications predictors have a nonlinear relation with the outcome. Optimal Scaling (OS) transformations combined with GLMs can deal with this type of relations. Transformations of the predictors have been integrated in GLMs before, e.g. in Generalized Additive Models. However, the OS methodology has several benefits. For example, the levels of categorical predictors are quantified directly, such that they can be included in the model without defining dummy variables. This approach enhances the interpretation and visualization of the effect of different levels on the outcome. Furthermore, monotonicity restrictions can be applied to the OS transformations such that the original ordering of the category values is preserved. This improves the interpretation of the effect and may prevent overfitting. The scaling level can be chosen for each individual predictor such that models can include mixed scaling levels. In this way, a suitable transformation can be found for each predictor in the model. The implementation of OS in logistic regression is demonstrated using three datasets that contain a binary outcome variable and a set of categorical and/or continuous predictor variables.Comment: 35 pages, 4 figure