Minimal data set. by Xiaoxia Liu (294088)

Overview of the WeARTolerance program. by Ana Beato (20489933)

“…Arts-based interventions have shown promise in addressing stigma, yet comprehensive longitudinal studies in community settings are limited. This research evaluates the "WeARTolerance’’ arts-based program in reducing mental health stigma among diverse youths. …”

Minimal data set. by Wei Huang (36889)

UHPLC-MS CKO vs K5Cre results. by Kevin J. Mills (9270966)

“…Lipidomic analysis of epidermis using advanced chemical separations and tandem mass spectrometry identified 470 individual lipids in epidermis with 165 significantly decreased and 82 significantly increased in CKO epidermis. …”

Uncropped raw western blot images. by Kevin J. Mills (9270966)

“…Lipidomic analysis of epidermis using advanced chemical separations and tandem mass spectrometry identified 470 individual lipids in epidermis with 165 significantly decreased and 82 significantly increased in CKO epidermis. …”

LC-IMS-CID-MS CKO vs K5Cre results. by Kevin J. Mills (9270966)

“…Lipidomic analysis of epidermis using advanced chemical separations and tandem mass spectrometry identified 470 individual lipids in epidermis with 165 significantly decreased and 82 significantly increased in CKO epidermis. …”

Phospholipid metabolism CKO vs K5Cre Reactome. by Kevin J. Mills (9270966)

“…Lipidomic analysis of epidermis using advanced chemical separations and tandem mass spectrometry identified 470 individual lipids in epidermis with 165 significantly decreased and 82 significantly increased in CKO epidermis. …”

Predictive Significance of Glycosyltransferase-Related lncRNAs in Endometrial Cancer: A Comprehensive Analysis and Experimental Validation by Xiaoyu Shen (4715430)

“…Furthermore, independent prognostic assessments, receiver operating characteristic (ROC) curves and nomograms, demonstrated that risk scores derived from these GTRLs outperformed other clinical variables within the TCGA-UCEC clinical data set in predicting patient prognosis. And the low-risk cohort exhibited increased immune infiltration and decreased tumor purity. …”

S1 Data - by Miki Doi (15347878)

“…<div><p>In this study, we examined the effect of a bundled approach to blood collection for blood culture on decreasing contamination. …”

269 miRNAs of the 8 DSERGs. by Wei He (131453)

“…Compared with normal tissues, AAD tissues exhibited a significant decrease in CD8 T cells and an increase in NK cells and macrophages.…”

Comprehensive list of all SRGs. by Wei He (131453)

“…Compared with normal tissues, AAD tissues exhibited a significant decrease in CD8 T cells and an increase in NK cells and macrophages.…”

A total of 700 DEGs. by Wei He (131453)

“…Compared with normal tissues, AAD tissues exhibited a significant decrease in CD8 T cells and an increase in NK cells and macrophages.…”

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test plan. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting curve parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test instrument. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”