Inositol requiring enzyme-1 （IRE1） is highly conserved from yeasts to humans. Upon endoplasmic reticulum （ER） stress, IRE1 activates X-box-binding protein 1 （XBP1） by unconventional splicing of XBP1 mRNA, which ...Inositol requiring enzyme-1 （IRE1） is highly conserved from yeasts to humans. Upon endoplasmic reticulum （ER） stress, IRE1 activates X-box-binding protein 1 （XBP1） by unconventional splicing of XBP1 mRNA, which activates unfolded protein response （UPR） to restore ER homeostasis. In mice, IRE1α plays an essential role in extraembryonic tissues. However, its precise action during the early stage of development is unknown. In this study, the gain and loss-of-function analyses were used to investigate the function of Xenopus IRE1α （xIRE1α）. The effects of xIRE1α during embryo development were detected with RT-PCR and whole mount in situ hybridization. ER stress was induced by tunicamycin. The apoptofic cells were measured by TUNNEL assays. Although both gain and loss of xlRE1α function had no significant effect on Xenopus embryogenesis, knockdown of xIRE1α could rescue tunicamycin-induced developmental defects and apoptosis. The finding indicates that xIRE1α is not required for embryogenesis but is required for tunicamycin-induced developmental defects and apoptosis in Xenopus laevis.展开更多
Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detecti...Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.展开更多
文摘Inositol requiring enzyme-1 （IRE1） is highly conserved from yeasts to humans. Upon endoplasmic reticulum （ER） stress, IRE1 activates X-box-binding protein 1 （XBP1） by unconventional splicing of XBP1 mRNA, which activates unfolded protein response （UPR） to restore ER homeostasis. In mice, IRE1α plays an essential role in extraembryonic tissues. However, its precise action during the early stage of development is unknown. In this study, the gain and loss-of-function analyses were used to investigate the function of Xenopus IRE1α （xIRE1α）. The effects of xIRE1α during embryo development were detected with RT-PCR and whole mount in situ hybridization. ER stress was induced by tunicamycin. The apoptofic cells were measured by TUNNEL assays. Although both gain and loss of xlRE1α function had no significant effect on Xenopus embryogenesis, knockdown of xIRE1α could rescue tunicamycin-induced developmental defects and apoptosis. The finding indicates that xIRE1α is not required for embryogenesis but is required for tunicamycin-induced developmental defects and apoptosis in Xenopus laevis.
文摘Structural development defects essentially refer to code structure that violates object-oriented design principles. They make program maintenance challenging and deteriorate software quality over time. Various detection approaches, ranging from traditional heuristic algorithms to machine learning methods, are used to identify these defects. Ensemble learning methods have strengthened the detection of these defects. However, existing approaches do not simultaneously exploit the capabilities of extracting relevant features from pre-trained models and the performance of neural networks for the classification task. Therefore, our goal has been to design a model that combines a pre-trained model to extract relevant features from code excerpts through transfer learning and a bagging method with a base estimator, a dense neural network, for defect classification. To achieve this, we composed multiple samples of the same size with replacements from the imbalanced dataset MLCQ1. For all the samples, we used the CodeT5-small variant to extract features and trained a bagging method with the neural network Roberta Classification Head to classify defects based on these features. We then compared this model to RandomForest, one of the ensemble methods that yields good results. Our experiments showed that the number of base estimators to use for bagging depends on the defect to be detected. Next, we observed that it was not necessary to use a data balancing technique with our model when the imbalance rate was 23%. Finally, for blob detection, RandomForest had a median MCC value of 0.36 compared to 0.12 for our method. However, our method was predominant in Long Method detection with a median MCC value of 0.53 compared to 0.42 for RandomForest. These results suggest that the performance of ensemble methods in detecting structural development defects is dependent on specific defects.
