Data Sheet 1_Dynamic electrophysiological changes in abnormal brain cavities post-ischemic stroke.docx by Ugur Kilic (9927866)

Image 1_NK cells-derived extracellular vesicles potency in the B cell lymphoma biotherapy.tif by Serena Cecchetti (396031)

“…Ex vivo lymphoma metabolomics revealed a significant increase in fatty acid (FA) pool and decrease in unsaturated and mono-unsaturated FA in treated groups, as compared to control one, thus suggesting an alteration of tumor homeostasis. …”

Video 1_The clinical application value of mixed reality in robotic laparoscopic partial nephrectomy.mp4 by Xin Chang Zou (20129301)

Timeline for study enrollment. by Saeun Park (20410160)

“…Caregivers had more severe symptoms of depression and/or anxiety if they experienced greater changes in living situations or decreases in physical activity (CESD-20: β = 3.35, 95% CI [1.00, 5.70], p = .01), food insecurity (HSCL-25: β = 3.25, 95% CI [0.41, 6.10], p = .03, CESD-25: β = 3.09, 95% CI [0.79, 5.39], p = .01), and domestic violence (HSCL-25: β = 3.82, 95% CI [0.94, 6.70], p = .01) during COVID-19. …”

Dataset used in the manuscript. by Saeun Park (20410160)

“…Caregivers had more severe symptoms of depression and/or anxiety if they experienced greater changes in living situations or decreases in physical activity (CESD-20: β = 3.35, 95% CI [1.00, 5.70], p = .01), food insecurity (HSCL-25: β = 3.25, 95% CI [0.41, 6.10], p = .03, CESD-25: β = 3.09, 95% CI [0.79, 5.39], p = .01), and domestic violence (HSCL-25: β = 3.82, 95% CI [0.94, 6.70], p = .01) during COVID-19. …”

Dexmedetomidine improves septic acute kidney injury by inhibiting inflammation and oxidative stress through the activation of the Pink1/Park2 autophagy pathway by Qiuxia Liao (21507933)

“…Dexmedetomidine reduced inflammatory factors, such as tumor necrosis factor-α, interleukin (IL)-18, IL-6, and IL-1β, and improved kidney function by decreasing serum cystatin C, creatinine, blood urea nitrogen, kidney injury molecule-1, and neutrophil gelatinase-associated lipocalin. …”

The overall framework of CARAFE. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

KPD-YOLOv7-GD network structure diagram. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison of different pruning rates. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison of experimental results at ablation. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Result of comparison of different lightweight. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

DyHead Structure. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

The parameters of the training phase. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Structure of GSConv network. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Comparison experiment of accuracy improvement. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Improved model distillation structure. by Zhongjian Xie (4633099)

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model’s complexity. Finally, the experiment significantly enhanced the efficiency of feature extraction and the detection accuracy of the model algorithm through the integration of the Dynamic Head (DyHead) module, the Content-Aware Re-Assembly of Features (CARAFE) module, and the incorporation of knowledge distillation techniques. …”

Table 1_Exploring the effects of aerobic and resistance exercise on mood-related symptoms and EEG activity.docx by Kihoon Yuk (20950130)

Table 1_Investigating the psychophysiological effects of NaiKan Therapy: salivary oxytocin and cortisol release.docx by Ming Qian (429232)

Data Sheet 1_Temporal and spatial variability of S-wave and coda attenuation in the Central Apennines, Italy.docx by Simona Gabrielli (11473572)