Effects of Flood on Thermal Structure of a Stratified Reservoir

AbstractAn important factor in aquatic environment is water temperature, which influence many important processes in an aquatic ecosystem. In this paper, a 3-D model of thermal-hydrodynamic was developed to simulate the thermal structure in stratified reservoir, taking YE reservoir, a typical canyon reservoir in south-west China as an example. The simulated results showed that YE reservoir is a stratified reservoir, and water column in reservoir have two overturning periods per year separated by periods of stratification. In this case, 7-days flood process (from Jul.17th to Jul.23th) during the normal flow year was taken as a inflow charge, the results showed if the temperature of flood is the same as that of the outflow from the reservoir, the thermal structure will maintain the former status, and if the temperature of flood is lower than that of the outflow, the flood will cause thermocline erosion to some extent. And in this case, the effect time can last two months. By changing the outlet location, simulations demonstrate that the outlet elevation is the main factor controlling the thermal structure in YE reservoir

Deep Learning with Convolutional Neural Networks for Motor Brain-Computer Interfaces based on Stereo-electroencephalography (SEEG)

Objective: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals. Methods: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN. Results: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 ± 6.1%, 38 ± 4.9%, 60 ± 3.9%, 60 ± 3.3%, 61 ± 3.2%, and 63 ± 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain. Conclusion: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives. Significance: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.</p

Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients

Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10% to 50%) of subjects are BCI-illiterate users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and ten healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately one minute). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r=-0.732, p<0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (r=0.641, p<0.001). Furthermore, the BCI-illiterate users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-illiterate users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-illiteracy phenomenon in stroke patients.National Natural Science Foundation of China (Grant No. 51620105002) National High Technology Research and Development Program (863 Program) of China (Grant No.2015AA020501

Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns

Background: Most prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since HD EMG electrodes have been developed more conveniently to wear with respect to the previous versions recently, HD EMG signals become an alternative for myoelectric prostheses. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA). Methods: HD EMG signals acquired from the forearm of the subjects were used for pattern recognition-based myoelectric control in this study. Multiclass common spatial patterns (CSP) with two types of schemes, namely one versus one (CSP-OvO) and one versus rest (CSP-OvR), were used for feature extraction to improve the robustness against electrode shift for myoelectric control. Shift transversal (ST1 and ST2) and longitudinal (SL1 and SL2) to the direction of the muscle fibers were taken into consideration. We tested nine intact-limb subjects for eleven hand and wrist motions. The CSP features (CSP-OvO and CSP-OvR) were compared with three commonly used features, namely time-domain (TD) features, time-domain autoregressive (TDAR) features and variogram (Variog) features. Results: Compared with the TD features, the CSP features significantly improved the CA over 10 % in all shift configurations (ST1, ST2, SL1 and SL2). Compared with the TDAR features, a. the CSP-OvO feature significantly improved the average CA over 5 % in all shift configurations; b. the CSP-OvR feature significantly improved the average CA in shift configurations ST1, SL1 and SL2. Compared with the Variog features, the CSP features significantly improved the average CA in longitudinal shift configurations (SL1 and SL2). Conclusion: The results demonstrated that the CSP features significantly improved the robustness against electrode shift for myoelectric control with respect to the commonly used features.National Basic Research Program (973 Program) of China [2011CB013305]; National Natural Science Foundation of China [51375296, 51475292

Establishment and application of health risk ranking model for heavy metals in aquatic products

Objective To develop a scientific and rapid risk ranking model that is suitable for the analysis of data from food chemicals surveillance system. Methods Based on the principles of food safety risk assessment, a series of index were developed for the contamination and toxicity characteristics of chemicals in food, food consumption characteristics of population, and standards violation rate, respectively. Then the logical operation relationship and the standards for assigning scores were set. The model was verified using real surveillance data. Results Total risk scores were calculated using the equation: total risk score = toxicity adjusted content score × violation rate score × consumption score. This model was applied to the risk classification of arsenic, cadmium, mercury and lead in aquatic products in China. The ranking result were in line with those estimated by the classical risk assessment model. Conclusion The model could rank the health risk of heavy metals in aquatic products properly, and can provide a scientific foundation for regulatory priority

Transplanted Human Amniotic Membrane-Derived Mesenchymal Stem Cells Ameliorate Carbon Tetrachloride-Induced Liver Cirrhosis in Mouse

BACKGROUND: Human amniotic membrane-derived mesenchymal stem cells (hAMCs) have the potential to reduce heart and lung fibrosis, but whether could reduce liver fibrosis remains largely unknown. METHODOLOGY/PRINCIPAL FINDINGS: Hepatic cirrhosis model was established by infusion of CCl₄ (1 ml/kg body weight twice a week for 8 weeks) in immunocompetent C57Bl/6J mice. hAMCs, isolated from term delivered placenta, were infused into the spleen at 4 weeks after mice were challenged with CCl₄. Control mice received only saline infusion. Animals were sacrificed at 4 weeks post-transplantation. Blood analysis was performed to evaluate alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Histological analysis of the livers for fibrosis, hepatic stellate cells activation, hepatocyte apoptosis, proliferation and senescence were performed. The donor cell engraftment was assessed using immunofluorescence and polymerase chain reaction. The areas of hepatic fibrosis were reduced (6.2%±2.1 vs. control 9.6%±1.7, p<0.05) and liver function parameters (ALT 539.6±545.1 U/dl, AST 589.7±342.8 U/dl,vs. control ALT 139.1±138.3 U/dl, p<0.05 and AST 212.3±110.7 U/dl, p<0.01) were markedly ameliorated in the hAMCs group compared to control group. The transplantation of hAMCs into liver-fibrotic mice suppressed activation of hepatic stellate cells, decreased hepatocyte apoptosis and promoted liver regeneration. More interesting, hepatocyte senescence was depressed significantly in hAMCs group compared to control group. Immunofluorescence and polymerase chain reaction revealed that hAMCs engraftment into host livers and expressed the hepatocyte-specific markers, human albumin and α-fetoproteinran. CONCLUSIONS/SIGNIFICANCE: The transplantation of hAMCs significantly decreased the fibrosis formation and progression of CCl₄-induced cirrhosis, providing a new approach for the treatment of fibrotic liver disease