Maternal depressive and anxiety symptoms, self-esteem, body dissatisfaction and preschooler obesity: a cross-sectional study

The primary aim of the present study was to cross-sectionally examine the associations between maternal psychosocial variables, child feeding practices, and preschooler body mass index z-score (BMI-z) in children (aged 2–4 years). A secondary aim was to examine differences in child weight outcomes between mothers scoring above and below specified cut-offs on the psychosocial measures. Two hundred and ninety mother–child dyads were recruited from Melbourne, Australia, and completed questionnaires examining demographic information, mothers’ depressive and anxiety symptoms, self-esteem and body dissatisfaction, restrictive and pressure child feeding practices, and preschoolers’ BMI-z scores. Independent t-tests and hierarchical multiple regression were employed to analyse the data. In the final regression model, none of the maternal psychosocial measures or feeding practices predicted child BMI-z scores; maternal body mass index and employment status were the only predictors of preschooler BMI-z. However, independent t-tests revealed that children of mothers with elevated body dissatisfaction scores had significantly higher BMI-z scores than children of mothers without elevated scores. The results suggest that psychosocial variables are not related, cross-sectionally, to preschooler weight outcomes; however, further research is needed to replicate the group differences noted between mothers with and without body dissatisfaction, and to track these relationships longitudinally.

Body fatness charts based on BMI and waist circumference

Objective To present percent body fat (PBF) charts based on body mass index (BMI) and waist circumference (WC) which can supplement current public health guidelines for obesity. Methods Based on data from the National Health and Nutrition Examination Survey (NHANES) III for 18- to 65-year-olds, a semi-parametric spline approach was utilized, in which no specific functional forms for BMI and WC are assumed, to depict graphically the relationship between BMI, WC, and PBF. Four distinct PBF charts were created, categorized by gender and ethnicity which are based on data from 2,170 white females, 1,902 African American females, 1,905 white males, and 1,635 African American males. Results PBF prediction based on the semi-parametric spline model outperformed competing linear models. For men, BMI is largely inconsequential, and WC plays a primary role in determining PBF levels. For women, the interaction between BMI and WC is more complex. To have low body fat, women would need to watch both their BMI and WC measurements carefully. Conclusions PBF charts, which incorporate information from three dimensions that are as simple to read as a BMI chart to help determine a person's level of fatness, were proposed.

Virtual lumbar spine of multi-body model based on simbody

The spine is an important and complex skeletal structure in the human body. It is a vulnerable part of our skeleton that is open to many medical problems. Hence it is necessary to establish a virtual spine model to assist surgeons to understand biomechanics of the spine. In this study, we aim to propose a virtual spine multi-body model. The computational biomechanical modeling of the spine is based on the theory of multi-body dynamics and implemented with SimBody open-source SDK. Simbody was then used to solve the kinetic equations and simulate the movement of spine. The spine model was validated by comparing its simulation results with experimental results from literature. The spine model will be helpful to understand biomechanics of the spine under loading.

Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions

Protein-protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%-80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.