Loop in the IB domain drives ParM monomer opening. by Natalie Ng (6560246)

“…Two simulations (ParM-ATP-2,3) consistently exhibited opening angles of ~102° after 50 ns and maintained that value, whereas in the other simulation (ParM-ATP-1), the opening angle increased beyond 105° after 100 ns and then eventually decreased to ~103° in the last 20 ns of the 200-ns simulation (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006683#pcbi.1006683.s004" target="_blank">S4A Fig</a>). …”

Data file. by Maryam Khalaji (21743611)

“…The results showed that mental fatigue significantly decreased QE duration (Mean difference = −138.75 ms, p = .0009) and fixation duration (Mean difference = 67.50 ms, p = .001), indicating a detrimental effect on sustained attention. …”

Structure-Based Design, Synthesis, and Biological Evaluation of New Triazolo[1,5‑<i>a</i>]Pyrimidine Derivatives as Highly Potent and Orally Active ABCB1 Modulators by Shuai Wang (109515)

“…In this work, we reported the structure-based design of triazolo­[1,5-<i>a</i>]­pyrimidines as new ABCB1 modulators, of which <b>WS-691</b> significantly increased sensitization of ABCB1-overexpressed SW620/Ad300 cells to paclitaxel (PTX) (IC₅₀ = 22.02 nM). …”

Structure-Based Design, Synthesis, and Biological Evaluation of New Triazolo[1,5‑<i>a</i>]Pyrimidine Derivatives as Highly Potent and Orally Active ABCB1 Modulators by Shuai Wang (109515)

“…In this work, we reported the structure-based design of triazolo­[1,5-<i>a</i>]­pyrimidines as new ABCB1 modulators, of which <b>WS-691</b> significantly increased sensitization of ABCB1-overexpressed SW620/Ad300 cells to paclitaxel (PTX) (IC₅₀ = 22.02 nM). …”

Structure-Based Design, Synthesis, and Biological Evaluation of New Triazolo[1,5‑<i>a</i>]Pyrimidine Derivatives as Highly Potent and Orally Active ABCB1 Modulators by Shuai Wang (109515)

“…In this work, we reported the structure-based design of triazolo­[1,5-<i>a</i>]­pyrimidines as new ABCB1 modulators, of which <b>WS-691</b> significantly increased sensitization of ABCB1-overexpressed SW620/Ad300 cells to paclitaxel (PTX) (IC₅₀ = 22.02 nM). …”

Dataset visualization diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Dataset sample images. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Performance comparison of different models. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

C2f and BC2f module structure diagrams. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

YOLOv8n detection results diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

YOLOv8n-BWG model structure diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

BiFormer structure diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

YOLOv8n-BWG detection results diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

GSConv module structure diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

mAP0.5 Curves of various models. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Network loss function change diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Comparative diagrams of different indicators. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

YOLOv8n structure diagram. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Geometric model of the binocular system. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”

Enhanced dataset sample images. by Yaojun Zhang (389482)

“…Results on a specialized dataset reveal that YOLOv8n-BWG outperforms YOLOv8n by increasing the mean Average Precision (mAP) by 4.2%, boosting recognition speed by 21.3% per second, and decreasing both the number of floating-point operations (FLOPs) by 28.9% and model size by 26.3%. …”