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The flexible microactuator (FMA) is a novel pneumatic rubber actuator developed for use in microrobots. This paper reports on integrated FMA systems to achieve distributed motion as occurs in intestinal villi, and with multi-legged arthropods such as centipedes.For the purpose of miniaturization and integration of FMAs, the authors focussed on two technical issues: A new fabrication process based on stereo lithography and a new FMA design called a restraint beam FMA. Stereo lithography enables fabrication of micro-structures with rubber-like materials suitable for integrated FMAs. The restraint beam FMA makes it possible to fabricate FMAs from a single material, allowing stereo lithography to be used.As examples of integrated FMA systems, four prototypes are shown: a 5 X 5 FMA array, a 3 X 3 FMA array which has pneumatic circuits at its base, a pipe interior mobile robot, and an amusement system consisting of 30 FMAs, which demonstrates ball handling ability.

Background Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD. Methods We reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors. Results The multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%–75.00%, p = 0.001–0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%–60.84%, p = 0.014–0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications. Conclusions Our multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD.

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