Physicochemical properties and cytocompatibility of radiation-resistant and anti-washout calcium phosphate cement by introducing artemisia sphaerocephala krasch gum.

The anti-washout ability of calcium phosphate cement (CPC) determines the effectiveness of CPC in clinical application. The γ-ray irradiation method often used in the sterilization process of CPC products is easy to degrade some commonly polymer anti-washout agent, which greatly reduces its anti-washout performance. Artemisia sphaerocephala Krasch gum (ASKG) has the potential of radiation resistance and anti-washout, but no one has considered its performance as anti-washout agent of CPC and mechanism of radiation resistance and anti-washout so far. In this study, we report the effect of γ-ray on ASKG and the effectiveness of ASKG for enhancing of radiation resistance and anti-washout ability of CPC, the physical, chemical properties and in vitro cell behaviors of ASKG-CPCs were also investigated. The results showed that addition of ASKG before and after irradiation could significantly enhanced the anti-washout performance of CPC, which is differ from conventional anti-washout agents. Meanwhile, ASKG-CPCs had an excellent injectable property and biocompatibility, and low content of irradiated ASKG could promote bone differentiation well. We anticipate that the radiation-resistant and anti-washout ASKG-CPCs have potential application prospect in orthopaedic surgery.

WMNN: Wearables-Based Multi-Column Neural Network for Human Activity Recognition.

In recent years, human activity recognition (HAR) technologies in e-health have triggered broad interest. In literature, mainstream works focus on the body's spatial information (i.e. postures) which lacks the interpretation of key bioinformatics associated with movements, limiting the use in applications requiring comprehensively evaluating motion tasks' correctness. To address the issue, in this article, a Wearables-based Multi-column Neural Network (WMNN) for HAR based on multi-sensor fusion and deep learning is presented. Here, the Tai Chi Eight Methods were utilized as an example as in which both postures and muscle activity strengths are significant. The research work was validated by recruiting 14 subjects in total, and we experimentally show 96.9% and 92.5% accuracy for training and testing, for a total of 144 postures and corresponding muscle activities. The method is then provided with a human-machine interface (HMI), which returns users with motion suggestions (i.e. postures and muscle strength). The report demonstrates that the proposed HAR technique can enhance users' self-training efficiency, potentially promoting the development of the HAR area.