Self-organising map-based dynamic decision-making algorithm for heterogeneous wireless sensor network by Umesh M. Kulkarni (10098026)

VANGL2 recruits NLRP3 to OPTN for selective autophagic degradation. by Huaji Jiang (16907514)

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. …”

VANGL2 recruits MARCH8 to promote the K27-linked ubiquitination of NLRP3. by Huaji Jiang (16907514)

Effect of SERINC5 antagonism on HIV-1 replication in PBMC cultures. by Dorota Kmiec (5646374)

Presentation_1_Do We Overestimate the Within-Variability? The Impact of Measurement Error on Intraclass Coefficient Estimation.pdf by Rafael Wilms (8858795)

MCM-BP can decrease phosphorylation by DDK but is not a substrate. by Tin Nguyen (231591)

Mean seedling length of mung bean genotypes grown under Cd and pretreatments of GA₃, SA and proline prior to Cd. by Meher Hassan (11502144)

Mean total protein contents of mung bean grown under Cd and pretreatments of GA₃, SA and proline prior to Cd. by Meher Hassan (11502144)

Mean values for GPX activity in mung bean grown under Cd and pretreatments of GA₃, SA and proline prior to Cd. by Meher Hassan (11502144)

Mean relative water content of mung bean grown under Cd and pretreatments of GA₃, SA and proline prior to Cd. by Meher Hassan (11502144)

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. …”