Ferroptosis Induction by a New Pyrrole Derivative in Triple Negative Breast Cancer and Colorectal Cancer by Domiziana Masci (4224451)

“…Furthermore, lactoperoxidase, malondialdehyde, and Fe­(II) levels significantly increased in <b>12</b>-treated tissues, whereas superoxide dismutase concentrations decreased. …”

Ferroptosis Induction by a New Pyrrole Derivative in Triple Negative Breast Cancer and Colorectal Cancer by Domiziana Masci (4224451)

“…Furthermore, lactoperoxidase, malondialdehyde, and Fe­(II) levels significantly increased in <b>12</b>-treated tissues, whereas superoxide dismutase concentrations decreased. …”

Characteristics of water footprint by sector at the regional level in China. by Jianqin Ma (20328414)

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Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…This narrow approach overlooks the multifaceted variables influencing runoff, resulting in incomplete and less reliable predictions. To address these challenges, we selected and integrated Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron Regression (MLPR) to develop two coupled intelligent prediction models—RF-SVR and RF-MLPR—due to their complementary strengths. …”

Sociodemographic and economic factors of study subjects, Ethiopia, EDHS 2005–2016 (N = 29,525). by Girma Beressa (9580436)

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Multivariable multilevel mixed-effects logistic regression analyses of zinc utilisation and predictors among under-five children with diarrhoea, Ethiopia, 2005–2016 (N = 29,525). by Girma Beressa (9580436)

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Common DEGs between Antonio and Colorado after MCP application under control condition. by Wardah K. Mustahsan (17484456)

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Effect of variety on physiological traits under ambient night-time temperature (ANT) and high night-time temperature (HNT) conditions, with or without MCP (1-methylcyclopropene) tr... by Wardah K. Mustahsan (17484456)

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Ethylene-related differentially expressed genes (DEGs) under 1-MCP treatment and HNT interaction effect in Antonio. by Wardah K. Mustahsan (17484456)

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Venn diagrams of the number of DEGs between each comparison. by Wardah K. Mustahsan (17484456)

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Lists of differentially expressed genes under high night-time temperatures with and without 1-MCP application. by Wardah K. Mustahsan (17484456)

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Fig 5 - by Wardah K. Mustahsan (17484456)

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Lists of top 20 hub genes identified from differentially expressed genes (DEGs) between the different comparisons. by Wardah K. Mustahsan (17484456)

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Lists of differentially expressed genes under high night-time temperatures without 1-MCP application. by Wardah K. Mustahsan (17484456)

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Lists of differentially expressed genes under ambient night-time temperatures with and without 1-MCP application. by Wardah K. Mustahsan (17484456)

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Gene ontology (GO) enrichment analysis under high night-time temperatures without 1-MCP application. by Wardah K. Mustahsan (17484456)

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