Rating scale for functional severity [28]. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

Regression model coefficients. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

ICOPE screening positive participant’s responses. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

WHO BMI classification for adults. by Shane Naidoo (20148021)

“…In an ordinal regression model BMI was a significant predictor (<i>B</i> = .10, <i>p</i> = .007) for increases in depression. …”

Histogram of the extracted parameters. by Qiuyu Du (20760848)

“…Subsequently, lines were fitted on intensities within a region of interest perpendicularly to the aorta centerline, from which three parameters were extracted: mean intensity, proximal intensity and contrast gradient. …”

Effects of Ni²⁺ on mitochondrial (A), glycolytic (B), and total (C) ATP production rates in BMDM. by Jesse Corbin (21736578)

“…A double dagger (‡) indicates a significant decrease of mitochondrial ATP production rates (slope = −0.79 ± 0.21, <i>R</i>^<i>2</i> = 0.40, <i>F</i>(1, 22)=15.0, <i>p </i>< 0.001) from 0 to 72 ppm Ni²⁺. …”

Image 1_The effect of Traumeel LT ad us. vet. on the perioperative inflammatory response after castration of stallions: a prospective, randomized, double-blinded study.tif by Julia Laves (19779357)

“…</p>Results<p>The main variable SAA was increased after surgery reaching a mean value of 122 µg/ml in the FL group and a mean SAA of 226 µg/ml in the TR group 48 h after surgery, reaching a significant difference only at the 24 h timepoint (p = 0.03). …”

Table 2_The effect of Traumeel LT ad us. vet. on the perioperative inflammatory response after castration of stallions: a prospective, randomized, double-blinded study.docx by Julia Laves (19779357)

“…</p>Results<p>The main variable SAA was increased after surgery reaching a mean value of 122 µg/ml in the FL group and a mean SAA of 226 µg/ml in the TR group 48 h after surgery, reaching a significant difference only at the 24 h timepoint (p = 0.03). …”

Table 1_The effect of Traumeel LT ad us. vet. on the perioperative inflammatory response after castration of stallions: a prospective, randomized, double-blinded study.docx by Julia Laves (19779357)

“…</p>Results<p>The main variable SAA was increased after surgery reaching a mean value of 122 µg/ml in the FL group and a mean SAA of 226 µg/ml in the TR group 48 h after surgery, reaching a significant difference only at the 24 h timepoint (p = 0.03). …”

Channel pruning process. by Zhongjian Xie (4633099)

Supplementary Material for: Association of platelet count and mean platelet volume with fish intake frequency: Implication for the cardioprotective effect of fish intake by figshare admin karger (2628495)

Panels show results of gut microbiota and nutritional intake analyses by provinces. by Jean d’Amour Mutoni (21474045)

“…<p><b>(A)</b> Beta diversity analyses, visualized by a PCoA, reveal statistically significant differences between the West and both the East and the South; <b>(B)</b> ANCOM-BC analysis results show differentially abundant taxa between the West and the East at phylum, family and genus levels while only one family was differentially abundant between the West and the South. …”

Identification of Key Genes. by Chen Wang (88408)

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

“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”

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

“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”

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

“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”

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

“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”

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

“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”

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

“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”

DyHead Structure. by Zhongjian Xie (4633099)

“…Firstly, improve the multi-scale feature layer and reduce the complexity of the model. 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. …”