The present study covers a new experimental system, designed to improve the balance and postural control of adults with cerebral palsy. This system is based on a serious game for balance rehabilitation therapy, designed using the prototype development paradigm and features for rehabilitation with serious games: feedback, adaptability, motivational elements, and monitoring. In addition, the employed interaction technology is based on computer vision because motor rehabilitation consists of body movements that can be recorded, and because vision capture technology is noninvasive and can be used for clients who have difficulties in holding physical devices. Previous research has indicated that serious games help to motivate clients in therapy sessions; however, there remains a paucity of clinical evidence involving functionality. We rigorously evaluated the effects of physiotherapy treatment on balance and gait function of adult subjects with cerebral palsy undergoing our experimental system. A 24-week physiotherapy intervention program was conducted with nine adults from a cerebral palsy center who exercised weekly in 20-min sessions. Findings demonstrated a significant increase in balance and gait function scores resulting in indicators of greater independence for our participating adults. Scores improved from 16 to 21 points in a scale of 28, according to the Tinetti Scale for risk of falls, moving from high fall risk to moderate fall risk. Our promising results indicate that our experimental system is feasible for balance rehabilitation therapy.

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Methods for prediction of proteins, DNA, or RNA function and mapping it onto sequence often rely on bioinformatics alignment approach instead of chemical structure. Consequently, it is interesting to develop computational chemistry approaches based on molecular descriptors. In this sense, many researchers used sequence-coupling numbers and our group extended them to 2D proteins representations. However, no coupling numbers have been reported for 2D-RNA topology graphs, which are highly branched and contain useful information. Here, we use a computational chemistry scheme: (a) transforming sequences into RNA secondary structures, (b) defining and calculating new 2D-RNA-coupling numbers, (c) seek a structure-function model, and (d) map biological function onto the folded RNA. We studied as example 1-aminocyclopropane-1-carboxylic acid (ACC) oxidases known as ACO, which control fruit ripening having importance for biotechnology industry. First, we calculated tau(k)(2D-RNA) values to a set of 90-folded RNAs, including 28 transcripts of ACO and control sequences. Afterwards, we compared the classification performance of 10 different classifiers implemented in the software WEKA. In particular, the logistic equation ACO = 23.8 . tau(1)(2D-RNA) + 41.4 predicts ACOs with 98.9%, 98.0%, and 97.8% of accuracy in training, leave-one-out and 10-fold cross-validation, respectively. Afterwards, with this equation we predict ACO function to a sequence isolated in this work from Coffea arabica (GenBank accession DQ218452). The tau(1)(2D-RNA) also favorably compare with other descriptors. This equation allows us to map the codification of ACO activity on different mRNA topology features. The present computational-chemistry approach is general and could be extended to connect RNA secondary structure topology to other functions.

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