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Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a long noncoding RNA (lncRNA) that contributes to the initiation and development of many solid tumors, including osteosarcoma (OS). In this study, we showed that MALAT1 was increased in human OS tissues and cell lines. MALAT1 knockdown suppressed OS cell growth and metastasis, induced OS cell apoptosis and delayed tumor growth in an OS xenograft model. We also detected downregulation of microRNA-509 (miR-509), a suppressor of OS growth, in OS tissues and cell lines. Then, we identified that miR-509 is a direct target of MALAT1 and Ras-related C3 botulinum toxin substrate 1 (Rac1) is a direct target of miR-509. MALAT1 may promote OS cell growth through inhibition of miR-509, leading to the activation of Rac1/JNK pathway. Our results suggest a MALAT1/miR-509/Rac1 axis that mediates OS cell proliferation and tumor progression.

To improve the accuracy of surface electromyography (sEMG)-based gesture recognition, we present a novel hybrid approach that combines real sEMG signals with corresponding virtual hand poses. The virtual hand poses are generated by means of a proposed cross-modal association model constructed based on the adversarial learning to capture the intrinsic relationship between the sEMG signals and the hand poses. We report comprehensive evaluations of the proposed approach for both frame-and window-based sEMG gesture recognitions on seven-sparse-multichannel and four-high-density-benchmark databases. The experimental results show that the proposed approach achieves significant improvements in sEMG-based gesture recognition compared to existing works. For frame-based sEMG gesture recognition, the recognition accuracy of the proposed framework is increased by an average of +5.2% on the sparse multichannel sEMG databases and by an average of +6.7% on the high-density sEMG databases compared to the existing methods. For window-based sEMG gesture recognition, the state-of-the-art recognition accuracies on three of the high-density sEMG databases are already higher than 99%, i.e., almost saturated; nevertheless, we achieve a +0.2% improvement. For the remaining eight sEMG databases, the average improvement with the proposed framework for the window-based approach is +2.5%. INDEX TERMSHand gesture recognition, surface electromyography (sEMG), myoelectric control, generative adversarial learning, virtual hand pose.

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Biometric signal based human-computer interface (HCI) has attracted increasing attention due to its wide application in healthcare, entertainment, neurocomputing, and so on. In recent years, deep learning based approaches have made great progress on biometric signal processing. However, the state-of-the-art (SOTA) approaches still suffer from model degradation across subjects or sessions. In this work, we propose a novel unsupervised domain adaptation approach for biometric signal based HCI via causal representation learning. Specifically, three kinds of interventions on biometric signals (i.e., subjects, sessions, and trials) can be selected to generalize deep models across the selected intervention. In the proposed approach, a generative model is trained for producing intervened features that are subsequently used for learning transferable and causal relations with three modes. Experiments on the EEG-based emotion recognition task and sEMG-based gesture recognition task are conducted to confirm the superiority of our approach. An improvement of +0.21% on the task of inter-subject EEG-based emotion recognition is achieved using our approach. Besides, on the task of inter-session sEMG-based gesture recognition, our approach achieves improvements of +1.47%, +3.36%, +1.71% and +1.01% on sEMG datasets including CSL-HDEMG, CapgMyo DB-b, 3DC and Ninapro DB6, respectively. The proposed approach also works on the task of inter-trial sEMG-based gesture recognition and an average improvement of +0.66% on Ninapro databases is achieved. These experimental results show the superiority of the proposed approach compared with the SOTA unsupervised domain adaptation methods on HCIs based on biometric signal.