Paeameter ranges and optimal values. by Zhen Zhao (159931)

“…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”

Improved random forest algorithm. by Zhen Zhao (159931)

“…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”

Datasets used in the study area. by Zhen Zhao (159931)

“…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”

Evaluation of the improved random forest model. by Zhen Zhao (159931)

“…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”

Comparison of model metrics. by Zhen Zhao (159931)

“…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”

Flowchart of population spatialization. by Zhen Zhao (159931)

“…Firstly, recursive feature elimination using cross validation (RFECV), maximum information coefficient (MIC), and mean decrease accuracy (MDA) methods were utilized to select population distribution feature factors. …”

Typical AFM detachment curves of MB231 cells interacting with HBMEC-60 (A, C, E and G) and HPMEC (B, D, F and H) cells at NCF (A and B), SCF (C and D), CF (E and F) and Mgrt (G and... by Leike Xie (1317108)

The lumen of the efferent ductules of 4-month-old <i>Slc9a3</i>^<i>-/-</i> males is dilated and calcified. by Ya-Yun Wang (175049)

Patterns of lifetime use of substances by gender. by Daniel Waiganjo Kinyanjui (15877725)

“…The lifetime prevalence of substance use was 41.5%, while that of alcohol use was 36%. For both, a higher mean neuroticism score [substance use- (AOR 1.05, 95%CI; 1, 1.10: p = 0.013); alcohol use- (AOR 1.04, 95%CI; 0.99, 1.09: p = 0.032)] showed increased odds of lifetime use, while a higher mean agreeableness score [substance use- (AOR 0.99, 95%CI; 0.95, 1.02: p = 0.008); alcohol use- (AOR 0.99, 95%CI; 0.95, 1.02: p = 0.032)] showed decreased odds of lifetime use. …”

S1 Data - by Daniel Waiganjo Kinyanjui (15877725)

“…The lifetime prevalence of substance use was 41.5%, while that of alcohol use was 36%. For both, a higher mean neuroticism score [substance use- (AOR 1.05, 95%CI; 1, 1.10: p = 0.013); alcohol use- (AOR 1.04, 95%CI; 0.99, 1.09: p = 0.032)] showed increased odds of lifetime use, while a higher mean agreeableness score [substance use- (AOR 0.99, 95%CI; 0.95, 1.02: p = 0.008); alcohol use- (AOR 0.99, 95%CI; 0.95, 1.02: p = 0.032)] showed decreased odds of lifetime use. …”

Factors associated with substance use. by Daniel Waiganjo Kinyanjui (15877725)

“…The lifetime prevalence of substance use was 41.5%, while that of alcohol use was 36%. For both, a higher mean neuroticism score [substance use- (AOR 1.05, 95%CI; 1, 1.10: p = 0.013); alcohol use- (AOR 1.04, 95%CI; 0.99, 1.09: p = 0.032)] showed increased odds of lifetime use, while a higher mean agreeableness score [substance use- (AOR 0.99, 95%CI; 0.95, 1.02: p = 0.008); alcohol use- (AOR 0.99, 95%CI; 0.95, 1.02: p = 0.032)] showed decreased odds of lifetime use. …”

Factors associated with personality traits. by Daniel Waiganjo Kinyanjui (15877725)

“…The lifetime prevalence of substance use was 41.5%, while that of alcohol use was 36%. For both, a higher mean neuroticism score [substance use- (AOR 1.05, 95%CI; 1, 1.10: p = 0.013); alcohol use- (AOR 1.04, 95%CI; 0.99, 1.09: p = 0.032)] showed increased odds of lifetime use, while a higher mean agreeableness score [substance use- (AOR 0.99, 95%CI; 0.95, 1.02: p = 0.008); alcohol use- (AOR 0.99, 95%CI; 0.95, 1.02: p = 0.032)] showed decreased odds of lifetime use. …”

Fig 2 - by Nida Mansoora (11648641)

Appearance of two brothers with hypophosphatemic rickets of an Indian family (E0023). by Sara L. Ma (759837)

Fig 3 - by Nida Mansoora (11648641)

Intracellular images (×20000) of <i>S</i>. <i>cerevisiae</i> strain BH8 before (a) and after (b, c) culturing in MSM with 1.50 mM Cu²⁺ for 48 h; CW: cell wall; N: cell n... by Xiang-yu Sun (748032)

lncRNA ZEB1-AS1 inhibits high glucose-induced EMT and fibrogenesis by regulating the miR-216a-5p/BMP7 axis in diabetic nephropathy by Qingqing Meng (1613494)

“…Long non-coding RNA zinc finger E-box binding homeobox 1 antisense 1 (ZEB1-AS1) plays a crucial role in the development of various diseases, including DN. …”

Fig 4 - by Nida Mansoora (11648641)

Fig 8 - by Torsten Schober (20485754)

S1 Data - by Gennaro Boccia (3700765)

“…Retrospectively, less than 10–20% of youth players were subsequently selected for the senior team. Data revealed a skewed birth date distribution in all age groups, and the RAEs magnitude decreased when age increased (i.e., ORs for Q1 vs Q4 was ~ 9 in U16 and ~ 1.7 in senior teams). …”