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Background Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions. Methods In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network. Results Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly. Conclusion In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.

Background Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed. Methods In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture’s traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results. Results On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance. Conclusion The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG.

Epilepsy is one of the world’s most common neurological diseases. Reliable early prediction and warning of seizures can provide timely treatment for patients with epilepsy, and improve their quality of life. Compared with most hand-designed prediction methods, an automatic prediction model that can process the original electroencephalogram (EEG) signals directly and take into account the leads optimization problem is needed. In this paper, we proposed an end-to-end automatic seizure prediction model based on the Batch Normalization Long Short Term Memory networks (BNLSTM) and Channel and Spatial attention (CASA). Firstly, raw EEG signals without any preprocessing are used as the input to the system, which can reduce the computation amount. Secondly, BNLSTM and CASA retained the time and spatial information of the raw EEG data respectively. Channel attention (CA) achieved the automatic optimization of EEG full-lead data and improved the prediction accuracy. Spatial attention (SA) achieved the adaptive learning of feature parameters. Finally, a fully connected layer is applied to predict the seizures. The performance of the seizure prediction model we proposed is evaluated on the data of 14 patients with Area Under the Curve (AUC) of 0.986, accuracy (Acc) of 0.956, specificity (Spe) of 0.968, and sensitivity (Sen) of 0.942. In addition, the proposed method provided an accurate prediction for all 50 seizures of the other 5 patients in the generalization dataset. Experimental results show that the proposed model has a certain generalization performance, which can provide a reliable basis for early warning of epileptic seizures.

The design of satisfied catalytic activity and stable catalysts for the oxygen evolution reaction (OER) is crucial in the production of hydrogen in electrolyzed water. Herein, S-doping NiCo-metal organic framework (NiCoS-MOF) were prepared successfully in situ on a nickel foamed (NF) substrate by used terephthalic acid (TPA) as an organic ligand via the original one-step solvothermal process, NiCoS-MOF reveals the electrocatalytic performance by the low overpotential of 71 mV at 10 mA cm−2 and the tafel slope of 74.4 mV dec−1 under alkaline condition (1 M KOH) served as OER electrodes directly. The i-t curve for 48 h at 1.50 and 2.14 V (vs.REH) showed excellent stability of NiCoS-MOF and the structure had no significant collapse after 1000 cycles. Superior OER activity and excellent stability could ascribed to the fluffy and orderly petal-like structure, which exhibited dispersal of the active sites and facilitated the evolution of oxygen during the catalysis process. The work enriched exploration of heteroatom-doped bimetallic MOFs in energy storage and conversion.

NiCr amorphous alloy coatings were deposited by magnetron sputtering at different sputtering current for corrosion resistance. X-ray diffraction performed that the coating was constituted of nearly...

Arbuscular mycorrhizal fungi (AMF) can form a symbiotic relationship with most terrestrial plant roots, promote plant growth, and heavy metal (HM) tolerance and thus plays a crucial role in phytoremediation. However, research on the relationship between colonization level and HM tolerance is limited. In this study, apple (Malus domestica) Gretchen Hagen3 genes MdGH3-2/12 silencing plants were treated with four AMF and Cd combination treatments to determine AMF colonization levels, biomass, Cd accumulation, photosynthesis, fluorescence, reactive oxygen species (ROS) and antioxidant substance accumulation, and Cd uptake, transport and detoxification gene expression levels. Results indicate the greater sensitivity of transgenic plants under AMF inoculation and Cd treatment compared with wild type (WT) via lower AMF colonization levels, biomass accumulation, photosynthetic parameters, and the accumulation and clearance homeostasis of ROS, as well as lower detoxification expression levels and higher Cd uptake and transport expression levels. Our study essentially demonstrates that MdGH3-2/12 plays an important role in Cd stress tolerance by regulating AM colonization in apple.