Can Nutrition Labeling Affect Obesity?

nutrition labeling, food labels, menu labeling, obesity, Food Consumption/Nutrition/Food Safety, I10, Q18,

The Costs of Obesity and Implications for Policymakers

Obesity, Direct Costs, Indirect Costs, Policy, Food Consumption/Nutrition/Food Safety, I10, I18,

Are architectural traits at florewing stage relevant to account for yield advantage in hybrid rice?

Yield advantage of hybrid rice over conventional inbred rice in tropical favorable environments, up to 15%, is commonly and simply attributed to higher biomass production due to higher seedling vigor and higher harvest index. Addressing hybrid superiority with less integrated plant traits should contribute to the development of plant-type management and the design of improved phenotypes. Precise phenotyping of high-yielding hybrid rice (H1, H3, and H5) and improved lines (I1 and I10) with the same crop duration was performed in a series of field experiments in 2004 and 2005 in the Philippines in order to quantify dynamics in assimilate partitioning and architectural plasticity of the plant in response to canopy competition. As commonly observed, higher yield of hybrid varieties was associated at maturity with higher shoot dry weight (38.5 g m-2 for H5 and 31.9 g m-2 for I10 in the wet season) and with higher harvest index (0.51 for H5 and 0.46 for I10). Indeed, hybrid rice had the ability to support a heavier panicle with an even weaker stem (high specific stem length), but then was more susceptible to lodging. Early seedling vigor for H1 and I1 was similar in the dry and wet seasons: dynamics in shoot dry matter, leaf area index and specific leaf area (SLA) did not significantly differ between genotypes. In contrast, internode dry weight of H1 was significantly higher with time than that of Il, both in the dry and in the wet seasons: at 69 days after sowing (DAS), it was 5.3 g for H1 and only 2.7 g for I1, while, for H1, there was quicker increase in the partitioning coefficient for internode and quicker decrease in the partitioning coefficient for blade. Internode dry weight of H1 was also significantly higher with time compared to that of I10: 3.9 g at 69 DAS, despite slightly higher vigor (lower SLA in H1). Early seedling vigor was also similar between H5 and I10 when observed in the wet season, as were dynamics in internode dry weight. Increase in shoot dry weight during grain filling in H5 was, however, significantly higher than that of I10 and I1. During the reproductive phase, Il exhibited a slower stem elongation and a quicker increase in blade angle with the main tiller compared to that of a check hybrid (H3). Despite the increasing competition for access to light in the canopy, I1 also exhibited an increase in the ellipse area formed by the leaf area of the plant at the highest collar level, whereas this was relatively constant for the hybrid variety. At flowering, better light distribution in the hybrid canopy, due to lower mutual shading between leaves, was suspected to have enhanced dry matter accumulation and a larger number of fully elongated stems was suspected to have increased harvest index by maximizing assimilate partitioning to grain right after flowering. Faster internode elongation and more efficient leaf spatial arrangement after canopy closure are proposed as more relevant traits than higher seedling vigor to account for hybrid rice superiority. (Résumé d'auteur

The VGAM Package for Categorical Data Analysis

Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM) framework. Additionally, there are natural extensions, such as reduced-rank VGLMs for dimension reduction, and allowing covariates that have values specific to each linear/additive predictor, e.g., for consumer choice modeling. This article describes some of the framework behind the VGAM R package, its usage and implementation details.

CircStat: A MATLAB Toolbox for Circular Statistics

Directional data is ubiquitious in science. Due to its circular nature such data cannot be analyzed with commonly used statistical techniques. Despite the rapid development of specialized methods for directional statistics over the last fifty years, there is only little software available that makes such methods easy to use for practioners. Most importantly, one of the most commonly used programming languages in biosciences, MATLAB, is currently not supporting directional statistics. To remedy this situation, we have implemented the CircStat toolbox for MATLAB which provides methods for the descriptive and inferential statistical analysis of directional data. We cover the statistical background of the available methods and describe how to apply them to data. Finally, we analyze a dataset from neurophysiology to demonstrate the capabilities of the CircStat toolbox.

Spatio-Temporal Multiway Data Decomposition Using Principal Tensor Analysis on k-Modes: The R Package PTAk

The purpose of this paper is to describe the R package {PTAk and how the spatio-temporal context can be taken into account in the analyses. Essentially PTAk() is a multiway multidimensional method to decompose a multi-entries data-array, seen mathematically as a tensor of any order. This PTAk-modes method proposes a way of generalizing SVD (singular value decomposition), as well as some other well known methods included in the R package, such as PARAFAC or CANDECOMP and the PCAn-modes or Tucker-n model. The example datasets cover different domains with various spatio-temporal characteristics and issues: (i)~medical imaging in neuropsychology with a functional MRI (magnetic resonance imaging) study, (ii)~pharmaceutical research with a pharmacodynamic study with EEG (electro-encephaloegraphic) data for a central nervous system (CNS) drug, and (iii)~geographical information system (GIS) with a climatic dataset that characterizes arid and semi-arid variations. All the methods implemented in the R package PTAk also support non-identity metrics, as well as penalizations during the optimization process. As a result of these flexibilities, together with pre-processing facilities, PTAk constitutes a framework for devising extensions of multidimensional methods such ascorrespondence analysis, discriminant analysis, and multidimensional scaling, also enabling spatio-temporal constraints.

spectrino Software: Spectra Visualization and Preparation for R

spectrino is a spectra preparation software utility for the R language and environment for statistical computing. It is an operating-system specific tool, for use under Microsoft Windows, with specialized visualization, organization and preprocessing features for spectra. The software accepts spectral data from analytical instruments and then prepares a data structure to be introduced in R. spectrino has a rich set of features to create data structures and visually manipulate/compare spectra. The application is accessible by a library of functions from within R. These commands allow for the creation and manipulation of data structures in spectrino and the selective extraction of spectral data. Before exporting, the spectra are preprocessed according the requirements of consecutive discriminant analysis. This preprocessing is adjustable by a series of options.

Quantifying Obesity in Economic Research: How Misleading is the Body Mass Index?

Replaced with revised version of paper 07/19/10.Obesity, percent body fat (PBF), body mass index (BMI), economic costs, measurement error, Health Economics and Policy, Research and Development/Tech Change/Emerging Technologies, C52, I10,

Automatic Generation of Exams in R

Package exams provides a framework for automatic generation of standardized statistical exams which is especially useful for large-scale exams. To employ the tools, users just need to supply a pool of exercises and a master file controlling the layout of the final PDF document. The exercises are specified in separate Sweave files (containing R code for data generation and LaTeX code for problem and solution description) and the master file is a LaTeX document with some additional control commands. This paper gives an overview of the main design aims and principles as well as strategies for adaptation and extension. Hands-on illustrations---based on example exercises and control files provided in the package---are presented to get new users started easily.