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This article presents a new framework for synthesizing motion of a virtual character in response to the actions performed by a user-controlled character in real time. In particular, the proposed method can handle scenes in which the characters are closely interacting with each other such as those in partner dancing and fighting. In such interactions, coordinating the virtual characters with the human player automatically is extremely difficult because the system has to predict the intention of the player character. In addition, the style variations from different users affect the accuracy in recognizing the movements of the player character when determining the responses of the virtual character. To solve these problems, our framework makes use of the spatial relationship-based representation of the body parts called interaction mesh, which has been proven effective for motion adaptation. The method is computationally efficient, enabling real-time character control for interactive applications. We demonstrate its effectiveness and versatility in synthesizing a wide variety of motions with close interactions.

Chinese Herbal Medicines (CHM) are the most common interventions of traditional Chinese medicine (TCM), typically administered as either single herbs or formulas. Systematic reviews (SRs) are essential references for evaluating the efficacy and safety of CHM treatments accurately and reliably. Unfortunately, the reporting quality of SRs with CHM is not optimal, especially the reporting of CHM interventions and the rationale of why these interventions were selected. To address this problem, a group of TCM clinical experts, methodologists, epidemiologists, and editors has developed a PRISMA extension for CHM interventions (PRISMA-CHM) through a comprehensive process, including registration, literature review, consensus meeting, three-round Delphi survey, and finalization. The PRISMA checklist was extended by introducing the concept of TCM Pattern and the characteristics of CHM interventions. A total of twenty-four items (including sub-items) are included in the checklist, relating to title (1), structured summary (2), rationale (3), objectives (4), eligibility criteria (6), data items (11), synthesis of results (14, 21), additional analyses (16, 23), study characteristics (18), summary of evidence (24), and conclusions (26). Illustrative examples and explanations are also provided. The group hopes that PRISMA-CHM 2020 will improve the reporting quality of SRs of CHM.

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In this paper, we propose a new method to index and retrieve animation scenes in which multiple characters closely interact with one another. Such a technique can be an important tool for animators when they want to automatically extract the desired scene from a large database of animation sequence. Existing methods for single character movements do not scale well for multiple characters as they do not take into account the interaction of different body parts. In this paper, we propose a new distance function that computes the similarity of twocharacter interations using the spatial relationship of the body parts. For each interaction, we produce a timevarying graph structure based on the proximity of different joints, and compute the similarity of interactions by comparing the topology and Laplacian coordinates of the time-varying graph. Experimental results show that the proposed method outperforms previous methods which are based on the kinematics of individual characters. The top retrieved samples are found similar in high level semantics while containing style variations.

Objective To investigate how the ulcerative colitis (UC) be treated with Chinese herbal medicines (CHM), using Chinese medicine (CM) pattern (zheng) identification, in the current clinical practice. Methods A total of 7 electronic databases were systematically searched for UC clinical studies with CHM interventions (including single herbs and CHM formulas) published in English and Chinese from the date of their inception to November 25, 2020. Descriptive statistics were adopted to demonstrate the characteristics of study design, and to collate the commonly CM patterns of UC and frequently used CHM herbs and formulas. Further, IBM SPSS Modeler 18.0 and Cytoscape 3.7.1 software were used to analyze and visualize the associations between different categories of CHM and their zheng indications. Results A total of 2311 articles were included in this study, of which most (> 90%) were RCTs with CHM formulas. The most common zheng of UC was Large intestine dampness-heat, while the basic type of CM patten was Spleen deficiency. The most frequently used classical formula was Bai-Tou-Weng-Tang, followed by Shen-Ling-Bai-Zhu-San, and the commonly used proprietary CHM was Xi-Lei-San (enema). Sulfasalazine and Mesalazine are commonly used as concomitant western medicines. The most frequently used single medicinals were Huang Lian and Bai Zhu, which also identified as the core herbs for different CM patterns. Conclusion This study examined the application of CHM interventions for UC and summarized their characteristics in clinical practice. These data indicated there were limited information about the safety assessment of CHM formulas and further RCTs including CM pattern(s) with strict design are necessary.

In this paper, an objective assessment for determining whether a person has Parkinson disease is proposed. This is achieved by analyzing the correlation between joint movements, since Parkinsonian patients often have trouble coordinating different joints in a movement. Thus, the auto-correlation coefficient of single joint movements and the cross-correlation between movements in a pair of joints (hand, wrist, elbow and shoulder) were studied. These features were used to train and provide classification of subjects as having or not having Parkinson's disease using the Least Square Support Vector Machine. Experimental results showed that using either auto-correlation or cross-correlation features for classification provided over 91% correct classification. Using both features together provided better performance (96.0%) than using either feature alone. In addition, the performance of LS-SVM is better than that of Self Organizing Map (SOM) and k-nearest neighbor (KNN) in this case.