Image 5_Characterization of serum metabolome and respiratory microbiota in children with influenza A virus infection.jpeg by Xinyi Shi (3133611)

“…The respiratory microbiome structure in children with IAV, including H1N1 and H3N2, was significantly different from that of the control, with significantly increased Chao index. …”

Image 6_Characterization of serum metabolome and respiratory microbiota in children with influenza A virus infection.jpeg by Xinyi Shi (3133611)

“…The respiratory microbiome structure in children with IAV, including H1N1 and H3N2, was significantly different from that of the control, with significantly increased Chao index. …”

Image 4_Characterization of serum metabolome and respiratory microbiota in children with influenza A virus infection.jpeg by Xinyi Shi (3133611)

“…The respiratory microbiome structure in children with IAV, including H1N1 and H3N2, was significantly different from that of the control, with significantly increased Chao index. …”

Image 3_Characterization of serum metabolome and respiratory microbiota in children with influenza A virus infection.jpeg by Xinyi Shi (3133611)

“…The respiratory microbiome structure in children with IAV, including H1N1 and H3N2, was significantly different from that of the control, with significantly increased Chao index. …”

CFU counts and Gram stain of lung tissues 2 days following the injury. by Ghaith A. Bahader (21387572)

“…There was a significant decrease in the TBI/H + GM-CSF group in comparison to TBI/H+saline group (†p < 0.05). …”

RFAConv working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

PConv working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Improvement of SPPF to SPPF-R process. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

PR comparison on RSOD dataset. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Ablation study on the RSOD dataset. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Structure and working principle of LI-YOLOv8. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

C2f-E improvement process. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Structure of Detect and GP-Detect. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

YOLOv8 structure and working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Improvement of CBS to CBR process. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

EMA attention mechanism working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

Ablation study on the NWPU VHR-10 dataset. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

GSConv working principle. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

PR comparison on NWPU VHR-10 dataset. by Pingping Yan (462509)

“…Compared to YOLOv8, our approach achieved improvements of 7.6% and 2.8% in mAP@0.5, and increases of 2.1% and 1.1% in mAP@0.5:0.95. Furthermore, Parameters and GFLOPs were reduced by 10.0% and 23.2%, respectively, indicating a significant enhancement in detection accuracy along with a substantial decrease in both parameters and computational costs. …”

An improved <i>in vitro</i> 3T3-L1 adipocyte model of inflammation and insulin resistance by Ifeoluwa A. Odeniyi (19862796)

“…Importantly, insulin resistance and inflammation were still induced in the 12 h treated adipocytes: the expression of the insulin sensitive and inflammatory genes was decreased and increased, respectively. In 72 h treated adipocytes, no significant differences were found in the viability, glucose uptake or insulin-sensitive and inflammatory gene expression. …”