MIT-BIH expanded dataset proportion chart. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

AUROC Graphs of RF Model and ResNet. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

PCA-CGAN Model Workflow Diagram. by Chao Tang (10925)

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

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

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

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

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

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

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

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

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

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

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

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

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

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

“…Through in-depth analysis of ECG signal principal component distribution characteristics, we discovered that just a few principal components can explain over 90% of signal variance, revealing the inherent inefficiency and limitations of traditional complete waveform generation methods. …”

PCAIs inhibit NCI-H23 cell line viability. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

PCAIs disrupt actin filaments in NCI-H23 cells. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

PCAIs suppress 3D NCI-H23 cell invasion. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

PCAIs suppress NCI-H23 cancer cell migration. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

PCAIs induce apoptosis in NCI-H23 cells. by Matthew D. Gregory (19929096)

“…Polyisoprenylated Cysteinyl amide Inhibitors (PCAIs) constitute a group of potential cancer therapy agents that we designed to specifically disrupt and suppress hyperactive G-protein signaling, such as that caused by mutated RAS proteins. …”

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). …”