Development of a CRISPR/Cas-Based Detection Platform for Tracking Decreased Susceptibility to Cephalosporins in Neisseria gonorrheae by Ziyuan Zhao (2889005)

“…Developing efficient and accurate detection of resistant strains is urgently needed for their management and treatment. We have developed the Multiplex Integrated RPA-CRISPR/Cas12a detection Assay (MIRCA) for simultaneous detection of Neisseria gonorrheae (<i>Ng</i>) and mutations with decreased susceptibility to cephalosporins. …”

Causes and consequences of long-term defaunation in multi-species aggregations: Species traits predict freshwater mussel declines and decreases in biogeochemical storage and recycl... by Jonathan Lopez (16993383)

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. …”

Table 1_Multi-generational adaptation to Solanum nigrum increases reproduction and decreases microbial diversity of Aphis gossypii.docx by Peng Wang (34436)

“…The relative abundance of Buchnera in T5 and T10 significantly increased compared to M and T, while the relative abundance of Arsenophonus decreased markedly. …”

Predicted <i>cis</i>-acting elements in <i>DpAP2</i> promoter. by Lingru Ruan (18995544)

<b>Nest mass in forest tits </b><b><i>Paridae</i></b><b> </b><b>increases with elevation and decreasing body mass, promoting reproductive success</b> by Clara Wild (19246606)

“…We predicted that nest mass should increase with elevation and canopy openness, due to thermoregulation being more demanding in colder or warmer climatic conditions, and decrease with body mass, as larger species have greater thermoregulatory capabilities. …”

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. …”

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. …”

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. …”

Model prediction error trend chart. 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 prediction error analysis index. 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. …”

Passive sensing data. by Thierry Jean (20691795)

“…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …”

Surveys. by Thierry Jean (20691795)

“…Results also showed that metrics that do not account for imbalance (mean absolute error, accuracy) systematically overestimated performance, XGBoost models performed on par with or better than LSTM models, and a significant yet very small decrease in performance was observed as the forecast horizon expanded. …”

Variables of trees located at coordinates . by Alexandra M. Campbell (3284094)

Global parameters and variables for the model. by Alexandra M. Campbell (3284094)

Variables of patch located at coordinates . by Alexandra M. Campbell (3284094)

MRSP Data collected in March 2018. by Alexandra M. Campbell (3284094)