Apoptosis inhibitor abolished and restored rTsSPc-disrupted Caco-2 monolayer barrier integrity. by Qi Qi Lu (17721401)

MGPC module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Comparative experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Pruning experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Parameter setting table. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

DTADH module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Ablation experiment. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Multi scale detection. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

MFDPN module. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Detection effect of different sizes. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Radar chart comparing indicators. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

MFD-YOLO structure. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

Detection results of each category. by Bo Tong (2138632)

“…Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”

PGAM5 knockdown prevented rTsSPc from disrupting Caco-2 monolayer integrity. by Qi Qi Lu (17721401)

Inhibition of PGAM5 and apoptosis alleviated the rTsSPc-damaged Caco-2 monolayer barrier integrity. by Qi Qi Lu (17721401)

Raw data of Figs 1–6 in this study. by Qi Qi Lu (17721401)

“…When gut epithelial PGAM5 receptor and apoptosis were inhibited by PGAM5-specific siRNA, inhibitor (LFHP-1c) and apoptosis inhibitor (Z-VAD-FMK), trans-epithelial electrical resistance (TEER) and TJs expression were obviously increased, and intestinal permeability was evidently decreased. …”

Table 1_Prognostic significance of early alpha fetoprotein and des-gamma carboxy prothrombin responses in unresectable hepatocellular carcinoma patients undergoing triple combinati... by Teng Zhang (457128)

“…</p>Conclusion<p>AFP or DCP response at 6-8 weeks post-therapy predicts better oncological outcomes in patients with uHCC treated with triple therapy.…”

Predicted chronic activation of the cardiomyocyte signaling network during VO. by Johane H. Bracamonte (12883844)

Overall model framework. by Ke Yan (331581)

“…The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3+3", then it rises in turn at "5+5" and "7+7", reaches the highest at "7+7", and then begins to decrease at "10+10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. (4) Compared with other existing researches, although it is slightly inferior to Bidirectional Encoder Representations from Transformers (BERT) in word sense disambiguation, this proposed model performs well in syntactic analysis and English text error detection, and its comprehensive performance is excellent. …”

Key parameters of LSTM training model. by Ke Yan (331581)

“…The results show that: (1) From the experimental data of word sense disambiguation, the accuracy of the SMOSS-LSTM model proposed in this paper is the lowest when the context range is "3+3", then it rises in turn at "5+5" and "7+7", reaches the highest at "7+7", and then begins to decrease at "10+10"; (2) Compared with the control group, the accuracy of syntactic analysis in the experimental group reached 89.5%, while that in the control group was only 73.2%. (3) In the aspect of English text error detection, the detection accuracy of the proposed model in the experimental group is as high as 94.8%, which is significantly better than the traditional SMOSS-based text error detection method, and its accuracy is only 68.3%. (4) Compared with other existing researches, although it is slightly inferior to Bidirectional Encoder Representations from Transformers (BERT) in word sense disambiguation, this proposed model performs well in syntactic analysis and English text error detection, and its comprehensive performance is excellent. …”