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Background:Currently, the available treatments for long bone nonunion (LBN) are removing of focus of infection, bone marrow transplantation as well as Ilizarov methods etc. Due to a high percentage of failures, the treatments are complex and debated. To develop an effective method for the treatment of LBN, we explored the use of human autologous bone mesenchymal stems cells (hBMSCs) along with extracorporeal shock wave therapy (ESWT).Materials and Methods:Sixty three patients of LBN were subjected to ESWT treatment and were divided into hBMSCs transplantation group (Group A, 32 cases) and simple ESWT treatment group (Group B, 31 cases).Results:The patients were evaluated for 12 months after treatment. In Group A, 14 patients were healed and 13 showed an improvement, with fracture healing rate 84.4%. In Group B, eight patients were healed and 13 showed an improvement, with fracture healing rate 67.7%. The healing rates of the two groups exhibited a significant difference (P < 0.05). There was no significant difference for the callus formation after 3 months treatment (P > 0.05). However, the callus formation in Group A was significantly higher than that in the Group B after treatment for 6, 9, and 12 months (P < 0.05).Conclusion:Autologous bone mesenchymal stems cell transplantation with ESWT can effectively promote the healing of long bone nonunions.

For social robots to be successfully integrated and accepted within society, they need to be able to interpret human social cues that are displayed through natural modes of communication. In particular, a key challenge in the design of social robots is developing the robot's ability to recognize a person's affective states (emotions, moods, and attitudes) in order to respond appropriately during social human-robot interactions (HRIs). In this paper, we present and discuss social HRI experiments we have conducted to investigate the development of an accessibility-aware social robot able to autonomously determine a person's degree of accessibility (rapport, openness) toward the robot based on the person's natural static body language. In particular, we present two one-on-one HRI experiments to: 1) determine the performance of our automated system in being able to recognize and classify a person's accessibility levels and 2) investigate how people interact with an accessibility-aware robot which determines its own behaviors based on a person's speech and accessibility levels.

The synthesis of C-ring hydrogenated sinomenine derivatives is accomplished from sinomenine by treatment with Zn-Hg/HCl, halogenation with NIS, and Heck reactions with various acrylates. A total of nine novel sinomenine cinnamate derivatives are obtained in 84%–93% yields. The structures of all the derivatives are characterized by 1H NMR, 13C NMR, and MS spectroscopy.

The area under the ROC curve (AUC) has been used as a criterion to measure the performance of classification algorithms even the training data embraces unbalanced class distribution and cost-sensitiveness. Support Vector Machine (SVM) is accepted to be a good classification algorithm in classification learning. This paper describes an improved SVM learning method, where RBF is used as its kernel function, and the parameters of RBF are optimized by genetic algorithm. Within the parameter optimization and SVM learning, AUC is used as the evaluation criterion. The improved method can be used to deal with multi-class classification domains. Compared to the previous SVM algorithm, the improved SVM appears to have better learning performance.