FBMSNet: A Filter-Bank Multi-Scale Convolutional Neural Network for EEG-Based Motor Imagery Decoding.

OBJECT: Motor imagery (MI) is a mental process widely utilized as the experimental paradigm for brain-computer interfaces (BCIs) across a broad range of basic science and clinical studies. However, decoding intentions from MI remains challenging due to the inherent complexity of brain patterns relative to the small sample size available for machine learning. APPROACH: This paper proposes an end-to-end Filter-Bank Multiscale Convolutional Neural Network (FBMSNet) for MI classification. A filter bank is first employed to derive a multiview spectral representation of the EEG data. Mixed depthwise convolution is then applied to extract temporal features at multiple scales, followed by spatial filtering to mitigate volume conduction. Finally, with the joint supervision of cross-entropy and center loss, FBMSNet obtains features that maximize interclass dispersion and intraclass compactness. MAIN RESULTS: We compare FBMSNet with several state-of-the-art EEG decoding methods on two MI datasets: the BCI Competition IV 2a dataset and the OpenBMI dataset. FBMSNet significantly outperforms the benchmark methods by achieving 79.17% and 70.05% for four-class and two-class hold-out classification accuracy, respectively. SIGNIFICANCE: These results demonstrate the efficacy of FBMSNet in improving EEG decoding performance toward more robust BCI applications. The FBMSNet source code is available at https://github.com/Want2Vanish/FBMSNet.

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. MOTIVATION: Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. METHOD: We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. RESULTS: There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

[Studies on the chemical constituents of Ligularia macrophylla].

OBJECTIVE: To study the chemical constituents of Ligularia macrophylla. METHODS: Isolation and purification were carried out on repeated silica gel column chromatography. The structures of the compounds were identified by physico-chemical properties and spectral analyses. RESULTS: Eight compounds were isolated and identified as kaempferol (1), 2,4'-dihydroxy-5-methoxychalcone (2), 5-hydroxy-3,4', 7-trimethoxyflavone (3), isobutyl ester terephthalic acid (4), 4-hydroxybenzaldehyde (5), mono (2-ethylhexyl) terephthalate (6), lupeol (7), beta-sitosterol (8). CONCLUSION: Compounds 1 - 7 are isolated from this plant for the first time.

[Effects of Naoxinduotai capsule on markers of prothrombotic state in patients with essential hypertension].

OBJECTIVE: To investigate the effects of Naoxinduotai capsule on the markers of prothrombotic state as plasminogen activator inhibitor-1 (PAI-1), von Willebrand factor (vWF) and alpha-granular membrane protein (CD62p) in essential hypertension patients of yang hyperactivity and blood stagnation. METHOD: The 62 essential hypertension patients of yang hyperactivity and blood stagnation were divided into Naoxinduotai capsule + perindopril group (treatment group, 30 subjects) and Perindopril group (control group, 32 subjects). Clinical symptoms, blood pressure and the blood plasma PAI-1, vWF, CD62p of the patients were observed. The blood plasma PAI-1, vWF and CD62p were measured by enzyme-linked immunosorbant assay (ELISA). RESULT: After 8 weeks treatment, in treatment group the clinical symptoms became better, and there was a significant difference with control group (P < 0.05). Blood pressure was significantly degraded, but there was not a significant difference with control group. The blood plasma PAI-1, vWF, CD62p level was degraded ,and there were significant differences with control group in all the three makers (P < 0.05). CONCLUSION: Naoxinduotai capsule can treat the essential hypertension patients of Yang hyperactivity and blood stagnation, and it has conspicuous advantages in improving the clinical symptoms and the makers of prothrombotic state.