Cross-User Electromyography Pattern Recognition Based on a Novel Spatial-Temporal Graph Convolutional Network.

With the goal of promoting the development of myoelectric control technology, this paper focuses on exploring graph neural network (GNN) based robust electromyography (EMG) pattern recognition solutions. Given that high-density surface EMG (HD-sEMG) signal contains rich temporal and spatial information, the multi-view spatial-temporal graph convolutional network (MSTGCN)is adopted as the basic classifier, and a feature extraction convolutional neural network (CNN) module is designed and integrated into MSTGCN to generate a new model called CNN-MSTGCN. The EMG pattern recognition experiments are conducted on HD-sEMG data of 17 gestures from 11 subjects. The ablation experiments show that each functional module of the proposed CNN-MSTGCN network has played a more or less positive role in improving the performance of EMG pattern recognition. The user-independent recognition experiments and the transfer learning-based cross-user recognition experiments verify the advantages of the proposed CNN-MSTGCN network in improving recognition rate and reducing user training burden. In the user-independent recognition experiments, CNN-MSTGCN achieves the recognition rate of 68%, which is significantly better than those obtained by residual network-50 (ResNet50, 47.5%, p < 0.001) and long-short-term-memory (LSTM, 57.1%, p=0.045). In the transfer learning-based cross-user recognition experiments, TL-CMSTGCN achieves an impressive recognition rate of 92.3%, which is significantly superior to both TL-ResNet50 (84.6%, p = 0.003) and TL-LSTM (85.3%, p = 0.008). The research results of this paper indicate that GNN has certain advantages in overcoming the impact of individual differences, and can be used to provide possible solutions for achieving robust EMG pattern recognition technology.

Erratum to "A Low-Cost High-Performance Data Augmentation for Deep Learning-Based Skin Lesion Classification".

[This corrects the article DOI: 10.34133/2022/9765307.].

Feasibility study on the application of a spiking neural network in myoelectric control systems.

In recent years, the effectiveness of a spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validated, but there is a lack of comprehensive consideration of the problems of heavy training burden, poor robustness, and high energy consumption in the application of actual myoelectric control systems. In order to explore the feasibility of the application of SNN in actual myoelectric control systems, this paper investigated an EMG pattern recognition scheme based on SNN. To alleviate the differences in EMG distribution caused by electrode shifts and individual differences, the adaptive threshold encoding was applied to gesture sample encoding. To improve the feature extraction ability of SNN, the leaky-integrate-and-fire (LIF) neuron that combines voltage-current effect was adopted as a spike neuron model. To balance recognition accuracy and power consumption, experiments were designed to determine encoding parameter and LIF neuron release threshold. By conducting the gesture recognition experiments considering different training test ratios, electrode shifts, and user independences on the nine-gesture high-density and low-density EMG datasets respectively, the advantages of the proposed SNN-based scheme have been verified. Compared with a Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM) and Linear Discriminant Analysis (LDA), SNN can effectively reduce the number of repetitions in the training set, and its power consumption was reduced by 1-2 orders of magnitude. For the high-density and low-density EMG datasets, SNN improved the overall average accuracies by about (0.99 ~ 14.91%) under different training test ratios. For the high-density EMG dataset, the accuracy of SNN was improved by (0.94 ~ 13.76%) under electrode-shift condition and (3.81 ~ 18.95%) in user-independent case. The advantages of SNN in alleviating the user training burden, reducing power consumption, and improving robustness are of great significance for the implementation of user-friendly low-power myoelectric control systems.

A Novel Event-Driven Spiking Convolutional Neural Network for Electromyography Pattern Recognition.

Electromyography (EMG) pattern recognition is an important technology for prosthesis control and human-computer interaction etc. However, the practical application of EMG pattern recognition is hampered by poor accuracy and robustness due to electrode shift caused by repeated wearing of the signal acquisition device. Moreover, the user's acceptability is low due to the heavy training burden, which is caused by the need for a large amount of training data by traditional methods. In order to explore the advantage of spiking neural network (SNN) in solving the poor robustness and heavy training burden problems in EMG pattern recognition, a spiking convolutional neural network (SCNN) composed of cyclic convolutional neural network (CNN) and fully connected modules is proposed and implemented in this study. High density surface electromyography (HD-sEMG) signals collected from 6 gestures of 10 subjects at 6 electrode positions are taken as the research object. Compared to CNN with the same structure, CNN-Long Short Term Memory (CNN-LSTM), linear kernel linear discriminant analysis classifier (LDA) and spiking multilayer perceptron (Spiking MLP), the accuracy of SCNN is 50.69%, 33.92%, 32.94% and 9.41% higher in the small sample training experiment, 6.50%, 4.23%, 28.73%, and 2.57% higher in the electrode shifts experiment respectively. In addition, the power consumption of SCNN is about 1/93 of CNN. The advantages of the proposed framework in alleviating user training burden, mitigating the adverse effect of electrode shifts and reducing power consumption make it very meaningful for promoting the development of user-friendly real-time myoelectric control system.

Structurally Diverse Metabolites from the Ophiorrhiza japonica Bl. and Their Antioxidant Activities In Vitro and PPARα Agonistic Activities In Silico.

Ophiorrhiza japonica Bl. is a traditional Chinese materia medica widely used to treat several diseases. Chemical and pharmacological studies on O. japonica have been carried out; however, neither of them has been fully explored. In this study, an array of compounds was isolated from the title plant, including a new anthraquinone, ophiorrhizaquinone A (1), three alkaloids 2-4 and seven other compounds 5-11 with diverse structural types. Additionally, compounds 2, 5, 7, 8, 10 and 11 were isolated from the genus of Ophiorrhiza for the first time. Antioxidant bioassays in vitro using DPPH and ABTS were performed, and the results showed that compound 3 exhibited modest antioxidant activity with IC50 values of 0.0321 mg/mL and 0.0319 mg/mL, respectively. An in silico study of PPARα agonistic activities of compounds 2 and 3 was conducted by molecular docking experiments, revealing that both of them occupied the active site of PPARα via hydrogen bonds and hydrophobic interactions effectively. This study enriched both the phytochemical and pharmacological profiles of O. japonica.

Secondary Metabolites from Marine Sponges of the Genus Oceanapia: Chemistry and Biological Activities.

In this review, we summarized the distribution of the chemically investigated Oceanapia sponges, including the isolation and biological activities of their secondary metabolites, covering the literature from the first report in 1989 to July 2019. There have been 110 compounds reported during this period, including 59 alkaloids, 33 lipids, 14 sterols and 4 miscellaneous compounds. Besides their unique structures, they exhibited promising bioactivities ranging from insecticidal to antibacterial. Their complex structural characteristics and diverse biological properties have attracted a great deal of attention from chemists and pharmaceuticals seeking to perform their applications in the treatment of disease.

A Low-Cost High-Performance Data Augmentation for Deep Learning-Based Skin Lesion Classification.

Objective and Impact Statement. There is a need to develop high-performance and low-cost data augmentation strategies for intelligent skin cancer screening devices that can be deployed in rural or underdeveloped communities. The proposed strategy can not only improve the classification performance of skin lesions but also highlight the potential regions of interest for clinicians' attention. This strategy can also be implemented in a broad range of clinical disciplines for early screening and automatic diagnosis of many other diseases in low resource settings. Methods. We propose a high-performance data augmentation strategy of search space 101, which can be combined with any model through a plug-and-play mode and search for the best argumentation method for a medical database with low resource cost. Results. With EfficientNets as a baseline, the best BACC of HAM10000 is 0.853, outperforming the other published models of "single-model and no-external-database" for ISIC 2018 Lesion Diagnosis Challenge (Task 3). The best average AUC performance on ISIC 2017 achieves 0.909 (±0.015), exceeding most of the ensembling models and those using external datasets. Performance on Derm7pt archives the best BACC of 0.735 (±0.018) ahead of all other related studies. Moreover, the model-based heatmaps generated by Grad-CAM++ verify the accurate selection of lesion features in model judgment, further proving the scientific rationality of model-based diagnosis. Conclusion. The proposed data augmentation strategy greatly reduces the computational cost for clinically intelligent diagnosis of skin lesions. It may also facilitate further research in low-cost, portable, and AI-based mobile devices for skin cancer screening and therapeutic guidance.

Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification.

Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is proposed to deal with the defects of sample underrepresentation in the small dataset. Finally, a novel Multi-Weighted New Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are introduced to overcome the challenge of uneven sample size and classification difficulty and to reduce the impact of abnormal samples on training. By combining Modified RandAugment, MWNL and CLS, our single DCNN model method achieved the classification accuracy comparable or superior to those of multiple ensembling models on different dermoscopic image datasets. Our study shows that this method is able to achieve a high classification performance at a low cost of computational resources and inference time, potentially suitable to implement in mobile devices for automated screening of skin lesions and many other malignancies in low resource settings.

Efficient degradation of sulfamethoxazole by NiCo2O4 modified expanded graphite activated peroxymonosulfate: Characterization, mechanism and degradation intermediates.

Expanded graphite (EG) immobilized nickel ferrite (NiCo2O4) was successfully constructed by a simple hydrothermal approach and applied for the degradation of sulfamethoxazole (SMX) in model wastewater by peroxymonosulfate (PMS) activation. The features of prepared catalysts were characterized by SEM, TEM, EDS, XRD, BET, TPD and XPS techniques. The influences of several critical parameters including the prepared NiCo2O4-EG dosages, PMS concentrations, temperature, initial solution pH and inorganic ions on SMX removal were studied in details. In particular, the synthesized NiCo2O4-EG exhibits excellent catalytic performances for SMX depredation over a wide pH range (pH 3.0-11.0). Besides, the transformation of various reactive oxygen species (SO4-, HO, O2- and 1O2) with the change of initial pH was investigated by the electron paramagnetic resonance (EPR) and quenching tests. In addition, twelve major degradation intermediates of SMX were detected by UPLC-QTOF-MS/MS. Finally, the PMS activation mechanism in NiCo2O4-EG/PMS system by the synergistic coupling of EG and NiCo2O4 were put forward. In brief, this work provided a promising and potential catalyst for PMS activation to remove SMX from wastewater.