Random Forest structure with cross-validation. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Land use change from 1990 to 2020. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Geographic data. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Classification accuracy. by Mingyue Sun (4638535)

“…The thresholds of the ERI in the oasis zone-transition zone and the transition zone-desert zone were 0.08–0.085 and 0.111–0.118, respectively. (2) Socioeconomic factors, including infrastructure expansion, population density, and GDP, were dominant influences, contributing 64% to the ERI, whereas the influence of natural factors such as climate declined. (3) The low-ERI areas increased by 3.3% under government control, and the transition zones increased significantly, slowing the growth rate of the oasis zone. …”

Forest plot between UCAR and cognitive impairment. by Yubo Teng (21246745)

Subjects:

Flowchart of participants in this study. by Yubo Teng (21246745)

Subjects:

Nonlinear relationship between UACR and cognitive impairment. by Yubo Teng (21246745)

Subjects:

Threshold effect analysis of UACR and cognitive impairment. by Yubo Teng (21246745)

Subjects:

Analyzing the relationship between UACR (<30) and cognitive scores and impairment. by Yubo Teng (21246745)

Subjects:

Analyzing the relationship between UACR and cognitive scores and impairment. by Yubo Teng (21246745)

Subjects:

Baseline characteristics of participants (N = 2385) by UACR. by Yubo Teng (21246745)

Subjects:

Table 1_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 3_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 4_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 8_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 2_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 6_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 5_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 7_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”

Table 9_Absolute abundance calculation enhances the significance of microbiome data in antibiotic treatment studies.xlsx by Stefanie Wagner (743707)

“…Here, GCN correction additionally uncovered significant decreases of Lactobacillus and Faecalibacterium. …”