Data Sheet 1_Integrative analysis of DNA methylation, RNA sequencing, and genomic variants in the cancer genome atlas (TCGA) to predict endometrial cancer recurrence.zip by Jin Hwa Hong (6523928)

“…These were visualized through volcano plots and heat maps, while decision trees and random forests classified and stratified the samples.</p>Results<p>A machine learning analysis combined with box plots showed that in the copy number-high (CN-H) recurrence group, PARD6G-AS1 had decreased methylation, CSMD1 had increased methylation, and TESC expression was higher than the non-recurrence group. …”

Comparison results of ablation experiments. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Spato-temporal changes in land use types. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”

Table of dataset division. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Pattern indices of landscape levels. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”

Table 1_MicroRNA-27b alleviates septic cardiomyopathy by targeting the Mff/MAVS axis.docx by Xincai Wang (7819064)

“…</p>Results<p>Bioinformatics analysis revealed significant downregulation of miR-27b in SCM cardiac tissues (log2FC=-3.9, P<0.001). …”

Striking image. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Flow chart. by Aysenur Karakus (21192337)

“…Both TTH and migraine groups received PRT twice a week for six weeks,</p><p>Results</p><p>Within-group comparisons showed significant decreases in attack frequency, VAS, HIT-6, PCS, and WHODAS-II scores in both groups post-intervention (p<0.001). …”

Precision, recall, F1-Score curve. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Model comparison experimental results. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Slicing aided hyper inference algorithm. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Microbiome-host genetic association. by Tamizhini Loganathan (18538349)

“…Core microbiome and correlation analysis at the phylum and genus levels identified significant microbiota. Specifically, the abundance of genera such as <i>Pseudomonas</i> and <i>Akkermansia</i> decreased, while <i>Ruminococcus</i> and <i>Allistipes</i> increased, as determined by statistical and machine learning approaches. …”

Summary description of the samples. by Tamizhini Loganathan (18538349)

“…Core microbiome and correlation analysis at the phylum and genus levels identified significant microbiota. Specifically, the abundance of genera such as <i>Pseudomonas</i> and <i>Akkermansia</i> decreased, while <i>Ruminococcus</i> and <i>Allistipes</i> increased, as determined by statistical and machine learning approaches. …”

Improved YOLOv10 network structure. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Loss function variation curve. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Type level landscape index changes in 1990-2020. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”

Location map of the study area. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”

Different model detection results comparison. by Chunhua Yang (346871)

“…The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. …”

Data source. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”

Research Technology Flow Chart. by Chao Ma (207385)

“…The habitat quality shows a spatial distribution pattern of “high in the surrounding areas and low in the central areas”, and autocorrelation analysis shows that county-level units have significant spatial agglomeration effects. …”