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Additional file 1: Supplemental Table 1. The clinical pathological features of EOC patients for microarray (n=6). Supplemental Table 2. Primers used in this study. Supplemental Table 3. Antibodies used in this study. Supplemental Table 4. Reagents used in this study. Supplemental Table 5. Laboratory apparatus used in this study.

As the capacity for machines to extend human capabilities continues to grow, the communication channels used also need to expand. Allowing machines to translate gestural command into robot movements can make the communication between humans and machines more similar to interpersonal communication. Yet for operating requirements, instantaneity and precision of the interaction must reach the application level in realistic scenarios, and the accuracy of results cannot be guaranteed by thejudgement of a single motion sensor. Therefore, exploring a gesture detection technology that integrates multisensor information is very necessary. The presented work takes a step towards real-time gesture detection by fusing multiple physiological signals with wearable motion sensors. An algorithm is presented for processing and extracting motion signal acquired via inertial measurement unit (IMU) and electromyography (EMG) with a high error-tolerant way of wearing, and it is applied to the gesture recognition model established in reasonable threshold and logical judgment. This enables real-time gesture detection of 24 various assembled movements such as rotating palms while bending arms, clenching fist when unwinding upper limb. The result of involving intentional behavior by analyzing electroencephalogram (EEG) is also added to the gesture recognition process for eliminating unconscious actions. Together, these pipelines offer efficient gesture vocabulary suitable for remotely controlling robots. Experiments evaluate classifier performance and interface efficacy. The system successfully detected 95.6% of 360 commands, and the average processing time was 25.4ms among all the 15 trials of the experiment.

Recently, Expectation-maximization (EM) algorithm has been introduced as an effective means to solve multi-view registration problem. Most of the previous methods assume that each data point is drawn from the Gaussian Mixture Model (GMM), which is difficult to deal with the noise with heavy-tail or outliers. Accordingly, this paper proposed an effective registration method based on Student's t Mixture Model (StMM). More specially, we assume that each data point is drawn from one unique StMM, where its nearest neighbors (NNs) in other point sets are regarded as the t-distribution centroids with equal covariances, membership probabilities, and fixed degrees of freedom. Based on this assumption, the multi-view registration problem is formulated into the maximization of the likelihood function including all rigid transformations. Subsequently, the EM algorithm is utilized to optimize rigid transformations as well as the only t-distribution covariance for multi-view registration. Since only a few model parameters require to be optimized, the proposed method is more likely to obtain the desired registration results. Besides, all t-distribution centroids can be obtained by the NN search method, it is very efficient to achieve multi-view registration. What's more, the t-distribution takes the noise with heavy-tail into consideration, which makes the proposed method be inherently robust to noises and outliers. Experimental results tested on benchmark data sets illustrate its superior performance on robustness and accuracy over state-of-the-art methods., Comment: 11pages, 6 figures

A significant problem with diode neutral point clamped (NPC) three-level inverter is the fluctuation in the neutral-point voltage. In this paper, the causes of the neutral point potential unbalance based on space vector pulse width modulation (SVPWM) are analyzed. And then a strategy based on parameters self-tuning fuzzy logic control (FLC) is presented which could solve the problems of some commonly used control strategies. For time-variable nonlinear systems, this algorithm has better robustness and adaptability. Set the voltage difference between up capacitor and down capacitor of DC bus as the input of the proposed controller, then fuzzy calibration unit corrects the quantification factor of fuzzy controller in real time depending on the degree unbalance. At the same time, by changing fuzzy rule according to the degree unbalance and the neutral point's current direction, the duration of small vectors action can be obtained. The effectiveness of the proposed control approach is verified by simulation and experiment results.