Gamal, S. | bioRxiv Science

bioRxiv2026

A Growth-Based Framework for Scaling Infant Anthropometry in Motion Capture Studies

Biomechanical analysis of infant motor behavior is complicated by rapid growth and large inter-subject variability in body size and segment proportions. We present a growth-adjusted anthropometric scaling framework that integrates Centers for Disease Control and Prevention (CDC) growth charts with three-dimensional motion capture to estimate subject-specific infant biomechanics during prone play. Nineteen infants aged 2-6 months were recorded during spontaneous prone movement. Weight-for-age percentiles were computed using CDC LMS parameters and used to infer body length from length-for-age references, ensuring internal consistency between weight and length estimates. Body segments were scaled using published infant proportions and validated against head and shoulder widths measured from motion capture. Infant head and trunk proportions were substantially larger than adult values, whereas limb proportions were similar. Estimated and measured dimensions of head and trunk width showed good agreement, with mean root mean squared errors of approximately 3 cm. Adult-based scaling produced head and trunk length errors of up to 6 cm. This growth-adjusted framework improves anatomical fidelity and enables normalization of biomechanical measures across infants with differing growth trajectories.

bioengineering↗

bioRxiv2019

mitoXplorer, a visual data mining platform to systematically analyze and visualize mitochondrial expression dynamics and mutations

BackgroundMitochondria produce cellular energy in the form of ATP and are involved in various metabolic and signaling processes. However, the cellular requirements for mitochondria are different depending on cell type, cell state or organism. Information on the expression dynamics of genes with mitochondrial functions (mito-genes) is embedded in publicly available transcriptomic or proteomic studies and the variety of available datasets enables us to study the expression dynamics of mito-genes in many different cell types, conditions and organisms. Yet, we lack an easy way of extracting these data for gene groups such as mito-genes.\n\nResultsHere, we introduce the web-based visual data mining platform mitoXplorer, which systematically integrates expression and mutation data of mito-genes. The central part of mitoXplorer is a manually curated mitochondrial interactome containing [~]1200 genes, which we have annotated in 35 different mitochondrial processes. This mitochondrial interactome can be integrated with publicly available transcriptomic, proteomic or mutation data in a user-centric manner. A set of analysis and visualization tools allows the mining and exploration of mitochondrial expression dynamics and mutations across various datasets from different organisms and to quantify the adaptation of mitochondrial dynamics to different conditions. We apply mitoXplorer to quantify expression changes of mito-genes of a set of aneuploid cell lines that carry an extra copy of chromosome 21. mitoXplorer uncovers remarkable differences in the regulation of the mitochondrial transcriptome and proteome due to the dysregulation of the mitochondrial ribosome in retinal pigment epithelial trisomy 21 cells which results in severe defects in oxidative phosphorylation.\n\nConclusionsWe demonstrate the power of the visual data mining platform mitoXplorer to explore expression data in a focused and detailed way to uncover underlying potential mechanisms for further experimental studies. We validate the hypothesis-creating power of mitoXplorer by testing predicted phenotypes in trisomy 21 model systems. MitoXplorer is freely available at http://mitoxplorer.ibdm.univ-mrs.fr. MitoXplorer does not require installation nor programming knowledge and is web-based. Therefore, mitoXplorer is accessible to a wide audience of experimental experts studying mitochondrial dynamics.

bioinformatics↗