Structural Diagrams of RF Model and ResNet Model. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

PCA-CGAN model convergence curve. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

PCA-CGAN Structure Diagram. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

Comparison of Model Five-classification Results. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

PCAECG-GAN K-fold experiment table. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

PCA-CGAN Pseudocode Table. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

PCA-CGAN Ablation Experiment Results. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

CTV p20 interacts with autophagy-related proteins NbATG8a and NbATG8f and induces autophagy. by Yongle Zhang (11355546)

Supplementary Material for: Global, regional, and national burden of brain and central nervous system cancers for males from 1990 to 2021 and its predicted level in the next 25 yea... by Maimaiti A. (19938294)

“…Background: A study of the global burden of cerebral and central nervous system (CNS) cancers from the Global Burden of Diseases (GBD) indicates that these cancers significantly contribute to morbidity and mortality internationally. …”

Data Sheet 1_Active layer and permafrost microbial community coalescence increases soil activity and diversity in mixed communities compared to permafrost alone.docx by Stacey J. Doherty (6629561)

“…Microbial communities undergo significant compositional and functional shifts as permafrost thaws. …”

Data Sheet 1_Impact of endurance training on mitochondrial H2O2 production and NRF2 levels in different rat organs.pdf by Lukasz Galganski (22442002)

“…NRF2 protein levels were significantly elevated in all examined tissues, accompanied by an increase in glutathione reductase levels.…”

Results of normal and wide step width (cm). by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”

Raw data 16–20. by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”

Demographics, SD= Standard Deviation. by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”

Raw data 6–9 and 15. by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”

Raw data 1–5. by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”

Raw data 10–14. by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”

Coordination angle during running. by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”

Gait retraining with biofeedback. by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”

Coordination angle during walking. by Fateme Khorramroo (18086501)

“…Wide running significantly decreased the coordination variability in the ankle-knee sagittal during LR (p<0.001) and knee-hip sagittal during LR (p=0.007) and push-off (p=0.016). …”