Discovery of Novel [1,2,4]Triazolo[1,5‑<i>a</i>]pyrimidine Derivatives as Novel Potent S‑Phase Kinase-Associated Protein 2 (SKP2) Inhibitors for the Treatment of Cancer by Kaizhao Hu (15670612)

“…Pharmacological inhibition of Skp2 has exhibited promising antitumor activity. Herein, we present the design and optimization of a series of [1,2,4]triazolo[1,5-<i>a</i>]pyrimidine-based small molecules targeting Skp2. …”

Discovery of Novel [1,2,4]Triazolo[1,5‑<i>a</i>]pyrimidine Derivatives as Novel Potent S‑Phase Kinase-Associated Protein 2 (SKP2) Inhibitors for the Treatment of Cancer by Kaizhao Hu (15670612)

“…Pharmacological inhibition of Skp2 has exhibited promising antitumor activity. Herein, we present the design and optimization of a series of [1,2,4]triazolo[1,5-<i>a</i>]pyrimidine-based small molecules targeting Skp2. …”

Discovery of Novel [1,2,4]Triazolo[1,5‑<i>a</i>]pyrimidine Derivatives as Novel Potent S‑Phase Kinase-Associated Protein 2 (SKP2) Inhibitors for the Treatment of Cancer by Kaizhao Hu (15670612)

“…Pharmacological inhibition of Skp2 has exhibited promising antitumor activity. Herein, we present the design and optimization of a series of [1,2,4]triazolo[1,5-<i>a</i>]pyrimidine-based small molecules targeting Skp2. …”

Discovery of Novel [1,2,4]Triazolo[1,5‑<i>a</i>]pyrimidine Derivatives as Novel Potent S‑Phase Kinase-Associated Protein 2 (SKP2) Inhibitors for the Treatment of Cancer by Kaizhao Hu (15670612)

“…Pharmacological inhibition of Skp2 has exhibited promising antitumor activity. Herein, we present the design and optimization of a series of [1,2,4]triazolo[1,5-<i>a</i>]pyrimidine-based small molecules targeting Skp2. …”

Study on the expression of mRNA related to biofilm formation of <i>C. albicans</i> by CSR extract (100 μg/mL). by Xuxi Cheng (22453447)

Prediction of transition readiness. by Sharon Barak (4803966)

“…In most transition domains, help needed did not decrease with age and was not affected by function. …”

Fos does not interact with Jun but homodimerizes post axotomy. by Gibarni Mahata (19486154)

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%. …”

Performance comparison of three loss functions. 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%. …”