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Background:Increases in the aging population and in the number of accidents have resulted in more people suffering from physical impairments or disabilities. Rehabilitation therapy thus attracts greater attention as a means of helping patients recover and return to a normal life. With the extremely long and tedious nature of traditional rehabilitation, patients are reluctant to continue the entire process, thus the expected effects of the therapy cannot be obtained. Games are well known to help patients improve their concentration and shift their attention away from the discomfort of their injuries during rehabilitation. Thus, incorporating game technology into a rehabilitation program may be a promising approach.Methods:In this study, a gaming system used for shoulder rehabilitation was developed. The mechanical parts and electric circuits were integrated to mimic the functionalities of a shoulder wheel. Several games were also designed to suit the rehabilitation needs of the patients based on the age and gender differences among the individual users, enabling individuals to undergo the rehabilitation process by playing games. Two surveys were conducted to evaluate the satisfaction of the participants regarding the gaming system.Results:The results of the online survey among a larger population coincide with the responses of the hands-on participants through a paper-and-pencil survey. Statistical results suggest that the participants are willing to accept this novel approach.Conclusion:This gaming system can distract a patient from the sensation of pain or anxiety, and increase their motivation to participate in the therapeutic program. Advantages in terms of low-cost and easy setup increase the attractiveness of this new equipment for various potential users.

BackgroundSeveral dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses.Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC).ResultsWe compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation.ConclusionsThree dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks.

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The discrete-time fractional Gaussian noise (DFGN) has been proven to be a regular process. According to Wold and Kolmogorov theorems, this process can be described as an autoregressive (AR) model of an infinite order. An estimator for the Hurst exponent based on autoregressive power spectrum estimation has been proposed, but without considering order selection. In this paper, six common order selection methods for the AR model were used to select appropriate orders of the AR model in order to raise the accuracy of estimating the Hurst exponent. Experimental results show that these six AR methods with considering order selection are more accurate than the original AR method without considering order selection.