qRT-PCR results of differentially expressed miRNAs in rats’ myocardial tissue after training. by Zhaowen Tan (12283502)

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Volcano plot and clustering of differentially expressed miRNAs. by Zhaowen Tan (12283502)

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Summary of miRNAs sequencing data quality. by Zhaowen Tan (12283502)

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Differential expression of miRNAs in SHR-SED group and BFRT group (all miRNAs). by Zhaowen Tan (12283502)

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Histogram of the GO functional enrichment of differential miRNA target genes. by Zhaowen Tan (12283502)

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The miRNA target genes. by Zhaowen Tan (12283502)

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Changes in SHR body weight, hemodynamics and cardiac function before and after training. by Zhaowen Tan (12283502)

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Table of miRNA comparisons with reference genomes. by Zhaowen Tan (12283502)

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Prevaelnce of different pathogens by location. by Amete Mihret Teshale (12072758)

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Statistical association (P < 0.10) between background factors and dog owners’ attitude (AT), subjective norms (SN) and perceived behavioral control (PBC) that were significantly as... by Fentahun Mengie (20862454)

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Descriptive statistics of the sample. by Alex Bawuah (21702688)

“…The lack of birthweight data is a significant challenge in monitoring the global prevalence of extreme birthweight, either low or high, and newborn health. …”

Sample size characteristics. by Alex Bawuah (21702688)

“…The lack of birthweight data is a significant challenge in monitoring the global prevalence of extreme birthweight, either low or high, and newborn health. …”

Data used for the Study. by Alex Bawuah (21702688)

“…The lack of birthweight data is a significant challenge in monitoring the global prevalence of extreme birthweight, either low or high, and newborn health. …”

Split structural models for belief. by Abigail A. Lee (19935335)

“…More conservative political views and higher income correlated with decreased hesitancy. No other latent variables or covariates had a significant effect on vaccine hesitancy. …”

Architecture of Swin-T model. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Model the experimental results curve. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Results of comparison experiments. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Architecture of Swin Transformer Block. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Disease distribution map of the GZDL-BD. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Token merging module. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”