Functionality-Driven Musculature Retargeting

We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation-ready, so we can physically simulate muscle-actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi-planar X-ray images and medical examinations.Comment: 15 pages, 20 figure

Learning predict-and-simulate policies from unorganized human motion data

The goal of this research is to create physically simulated biped characters equipped with a rich repertoire of motor skills. The user can control the characters interactively by modulating their control objectives. The characters can interact physically with each other and with the environment. We present a novel network-based algorithm that learns control policies from unorganized, minimally-labeled human motion data. The network architecture for interactive character animation incorporates an RNN-based motion generator into a DRL-based controller for physics simulation and control. The motion generator guides forward dynamics simulation by feeding a sequence of future motion frames to track. The rich future prediction facilitates policy learning from large training data sets. We will demonstrate the effectiveness of our approach with biped characters that learn a variety of dynamic motor skills from large, unorganized data and react to unexpected perturbation beyond the scope of the training data.N

In-depth proteome analysis of brain tissue from Ewsr1 knockout mouse by multiplexed isobaric tandem mass tag labeling

Abstract EWS RNA binding protein 1 (EWSR1) is a multifunctional protein whose epigenetic signatures contribute to the pathogenesis of various human diseases, such as neurodegenerative disorders, skin development, and tumorigenic processes. However, the specific cellular functions and physiological characteristics of EWSR1 remain unclear. In this study, we used quantitative mass spectrometry-based proteomics with tandem mass tag labeling to investigate the global proteome changes in brain tissue in Ewsr1 knockout and wild-type mice. From 9115 identified proteins, we selected 118 differentially expressed proteins, which is common to three quantitative data processing strategies including only protein level normalizations and spectrum-protein level normalization. Bioinformatics analysis of these common differentially expressed proteins revealed that proteins up-regulated in Ewsr1 knockout mouse are mostly related to the positive regulation of bone remodeling and inflammatory response. The down-regulated proteins were associated with the regulation of neurotransmitter levels or amino acid metabolic processes. Collectively, these findings provide insight into the physiological function and pathogenesis of EWSR1 on protein level. Better understanding of EWSR1 and its protein interactions will advance the field of clinical research into neuronal disorders. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026994