BackgroundLeft ventricular mass normalization for body size is recommended, but a question remains: what is the best body size variable for this normalization—body surface area, height or lean body mass computed based on a predictive equation? Since body surface area and computed lean body mass are derivatives of body mass, normalizing for them may result in underestimation of left ventricular mass in overweight children. The aim of this study is to indicate which of the body size variables normalize left ventricular mass without underestimating it in overweight children.MethodsLeft ventricular mass assessed by echocardiography, height and body mass were collected for 464 healthy boys, 5–18 years old. Lean body mass and body surface area were calculated. Left ventricular mass z-scores computed based on reference data, developed for height, body surface area and lean body mass, were compared between overweight and non-overweight children. The next step was a comparison of paired samples of expected left ventricular mass, estimated for each normalizing variable based on two allometric equations—the first developed for overweight children, the second for children of normal body mass.ResultsThe mean of left ventricular mass z-scores is higher in overweight children compared to non-overweight children for normative data based on height (0.36 vs. 0.00) and lower for normative data based on body surface area (-0.64 vs. 0.00). Left ventricular mass estimated normalizing for height, based on the equation for overweight children, is higher in overweight children (128.12 vs. 118.40); however, masses estimated normalizing for body surface area and lean body mass, based on equations for overweight children, are lower in overweight children (109.71 vs. 122.08 and 118.46 vs. 120.56, respectively).ConclusionNormalization for body surface area and for computed lean body mass, but not for height, underestimates left ventricular mass in overweight children.

Additional file 2. This research also provides the code that computes all research results. The geebm() is an R function that implements the GEEB machine. This function has seven arguments: formula, id, iteration, feature_rate, lrate, standardize, and data. Note that formula must be specified in the format "response ~ predictors" to list the predictors (input features) and response variable (output feature) in the dataset. id is a vector that identifies the clusters and can support multiple levels arranged in the order of multilayer structure. iteration is an integer representing the number of iterations, set to default at 100 iterations. feature_rate represents the proportion of random feature selection. When set to 1, it uses all features; by default, it is set to 0.5, using half of the features. lrate is a hyperparameter for the learning rate, with a default value of 0.1. standardize determines whether features are standardized, and the default does not perform standardization. data is used to input the training dataset. For example, when training the model with the Forest Fire Data in this study, the function would be: geebm(area~X+Y+FFMC+DMC+DC+ISI+temp+RH+wind+rain+day, id=c("season","month"), iteration=100, feature_rate=0.5, lrate=0.1, standardize=T, data=Dataset).

Abstract The Poincaré code is a Maple project package that aims to gather significant computer algebra normal form (and subsequent reduction) methods for handling nonlinear ordinary differential equations. As a first version, a set of fourteen easy-to-use Maple commands is introduced for symbolic creation of (improved variants of Poincaré’s) normal forms as well as their associated normalizing transformations. The software is the implementation by the authors of carefully studied and followed up sele...

Title of program: POINCARÉ Catalogue Id: AEPJ_v1_0

Nature of problem Computing structure-preserving normal forms near the origin for nonlinear vector fields.

Versions of this program held in the CPC repository in Mendeley Data AEPJ_v1_0; POINCARÉ; 10.1016/j.cpc.2013.04.003

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)