DataSheet_1_Physiological effects of γ-aminobutyric acid application on cold tolerance in Medicago ruthenica.pdf

Low temperatures in the seedling stage during early spring limit Medicago ruthenica germination and seedling growth. Elucidating the physiological mechanism of γ-aminobutyric acid (GABA)-regulated cold tolerance in M. ruthenica could provide a reference for alleviating the harmful effects of low temperatures on legumes in alpine meadows. The regulatory effects of GABA on M. ruthenica physiological parameters were explored by simulating the ground temperatures in the alpine meadow area of Tianzhu, China, in early May (2 h at 7°C; 6 h at 15°C; 4 h at 12°C; 2 h at 7°C; 10 h at 3°C). Our results showed that 15 mmol/l GABA was the optimal spray concentration to promote growth in the aboveground and belowground parts and increase the fresh and dry weights of seedlings. At this concentration, GABA enhanced the activities of catalase, peroxidase, superoxide dismutase, and ascorbate peroxidase; increased the osmotic balance; and inhibited the production of harmful substances in the cells under low-temperature conditions. GABA also regulated the tissue structure of leaves, increased the cell tense ratio, maintained photochemical activity, increased the amount of light energy to the photochemical reaction center, and improved the photosynthetic rate. Furthermore, exogenous GABA application increased the endogenous GABA content by promoting GABA synthesis in the early stages of low-temperature stress but mainly participated in low-temperature stress mitigation via GABA degradation in the late stages. Our results show that GABA can improve the cold tolerance of M. ruthenica by promoting endogenous GABA metabolism, protecting the membrane system, and improving the leaf structure.</p

Training and testing accuracy (batch size = 32).

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier’s performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.</div

Classifier performance using confusion matrix (a) Without normalization (b) With normalization.

Classifier performance using confusion matrix (a) Without normalization (b) With normalization.</p

Performance on ECG test dataset using different batch size for a learning rate of 0.0001.

Performance on ECG test dataset using different batch size for a learning rate of 0.0001.</p

The architecture of the proposed ResNet model.

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier’s performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.</div

Accuracy using 10-Fold cross validation.

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier’s performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.</div

Main procedure involved in the classification of ECG.

Main procedure involved in the classification of ECG.</p

Training and testing loss.

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier’s performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.</div

Performance on ECG test dataset using different batch size for a learning rate of 0.001.

Performance on ECG test dataset using different batch size for a learning rate of 0.001.</p

MIT-BIH verses AAMI 5 heartbeat classes grouping.

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier’s performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.</div