Classification Algorithm for fNIRS-based Brain Signals Using Convolutional Neural Network with Spatiotemporal Feature Extraction Mechanism.

Brain Computer Interface (BCI) is a highly promising human-computer interaction method that can utilize brain signals to control external devices. BCI based on functional near-infrared spectroscopy (fNIRS) is considered a relatively new and promising paradigm. fNIRS is a technique of measuring functional changes in cerebral hemodynamics. It detects changes in the hemodynamic activity of the cerebral cortex by measuring oxyhemoglobin and deoxyhemoglobin (HbR) concentrations and inversely predicts the neural activity of the brain. At the present time, Deep learning (DL) methods have not been widely used in fNIRS decoding, and there are fewer studies considering both spatial and temporal dimensions for fNIRS classification. To solve these problems, we proposed an end-to-end hybrid neural network for feature extraction of fNIRS. The method utilizes a spatial-temporal convolutional layer for automatic extraction of temporally valid information and uses a spatial attention mechanism to extract spatially localized information. A temporal convolutional network (TCN) is used to further utilize the temporal information of fNIRS before the fully connected layer. We validated our approach on a publicly available dataset including 29 subjects, including left-hand and right-hand motor imagery (MI), mental arithmetic (MA), and a baseline task. The results show that the method has few training parameters and high accuracy, providing a meaningful reference for BCI development.

ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network.

Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNN) have demonstrated superior performance compared to conventional machine learning (ML) approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.

EEG-FMCNN: A fusion multi-branch 1D convolutional neural network for EEG-based motor imagery classification.

Motor imagery (MI) electroencephalogram (EEG) signal is recognized as a promising paradigm for brain-computer interface (BCI) systems and has been extensively employed in various BCI applications, including assisting disabled individuals, controlling devices and environments, and enhancing human capabilities. The high-performance decoding capability of MI-EEG signals is a key issue that impacts the development of the industry. However, decoding MI-EEG signals is challenging due to the low signal-to-noise ratio and inter-subject variability. In response to the aforementioned core problems, this paper proposes a novel end-to-end network, a fusion multi-branch 1D convolutional neural network (EEG-FMCNN), to decode MI-EEG signals without pre-processing. The utilization of multi-branch 1D convolution not only exhibits a certain level of noise tolerance but also addresses the issue of inter-subject variability to some extent. This is attributed to the ability of multi-branch architectures to capture information from different frequency bands, enabling the establishment of optimal convolutional scales and depths. Furthermore, we incorporate 1D squeeze-and-excitation (SE) blocks and shortcut connections at appropriate locations to further enhance the generalization and robustness of the network. In the BCI Competition IV-2a dataset, our proposed model has obtained good experimental results, achieving accuracies of 78.82% and 68.41% for subject-dependent and subject-independent modes, respectively. In addition, extensive ablative experiments and fine-tuning experiments were conducted, resulting in a notable 7% improvement in the average performance of the network, which holds significant implications for the generalization and application of the network.

Accelerating the peroxidase-like activity of MoSe2 nanosheets at physiological pH by dextran modification.

Surface modification of MoSe2via dextran during ultrasound exfoliation is demonstrated to be an efficient and easy strategy to accelerate the peroxidase-like catalytic activity of MoSe2 nanosheets at neutral pH. The enhancement of catalytic activity is owing to the rich negative charges of dextran on the dextran-modified MoSe2 nanosheets.

GA3, a new gambogic acid derivative, exhibits potent antitumor activities in vitro via apoptosis-involved mechanisms.

AIM: Gambogic acid (GA) is the major active ingredient of gamboge, which is secreted from a Chinese traditional medicine, Garcinia hanburyi, which possesses potent antitumor activity. GA3, a new GA derivative, has been shown to possess better water solubility than GA. The aim of the present study was to examine the antitumor activity of GA3 and the mechanism underlying it. METHODS: The growth inhibition of cancer cell lines induced by GA3 was assessed using the SRB assay. DAPI staining, flow cytometry, a DNA fragment assay, and Western blot analysis were used to study the apoptotic mechanisms of GA3. RESULTS: GA3 displayed wide cytotoxicity in diversified human cancer cell lines with a mean IC(50) value of 2.15 micromol/L. GA3 was also effective against multidrug resistant cells, with an average resistance factor (RF) that was much lower than that of the reference drug, doxorubicin. Mechanistic studies revealed that GA3-induced apoptosis in HL-60 cells proceeded via both extrinsic and intrinsic pathways, with caspase-8 functioning upstream of caspase-9. In addition, GA3-driven apoptotic events were associated with up-regulation of Bax, down-regulation of Bcl-2 and cleavage of Bid. Moreover, GA3 triggered cytochrome c release from the mitochondria, in particular bypassing the involvement of the mitochondrial membrane potential. CONCLUSION: Better solubility and a potential anti-MDR activity, combined with a comparable antitumor efficacy, make GA3 a potential drug candidate in cancer therapy that deserves further investigation.

Caspase-8 preferentially senses the apoptosis-inducing action of NG-18, a Gambogic acid derivative, in human leukemia HL-60 cells.

Gambogic acid (GA) is the major active ingredient of gamboge secreted from a Chinese traditional medicine Garcinia hanburryi possessing potent anti-tumor activity. N-(2-ethoxyethyl)gambogamide (NG-18), a derivative of GA, also efficiently inhibits proliferation of cultured human tumor cells. The inhibition effect of NG-18 is associated with its ability to induce apoptosis. In the present study, NG-18 markedly induced leukemia HL-60 cells apoptosis, and the extrinsic and intrinsic apoptosis pathways were activated almost at the same time. NG-18-induced tumor cell apoptosis was associated with up-regulation of pro-apoptotic Bcl-2 family member Bax, and downregulation of anti-apoptotic protein Bcl-2. The NG-18-induced apoptosis was blocked completely by a pan-caspase inhibitor Z-VAD-FMK, indicating that caspases were functionally and actively involved in this process. The specific inhibition of caspase-8 activity using Z-IETD-FMK significantly blocked NG-18-induced apoptosis. In contrast, inhibition of other initiator caspases, caspase-2 or -9, using Z-VDVAD-FMK or Z-LEHD-FMK respectively had no effect on NG-18-induced apoptosis. Altogether, our data demonstrated that NG-18-induced apoptosis was dependent on caspases and caspase-8 acted as a key executor in the event.