Mot-2 protein is shown to interact with p53 and inhibit its transcriptional activation function. Mot-2 overexpressing stable clones of NIH 3T3 cells were malignantly transformed, however, they had a high level of expr...Mot-2 protein is shown to interact with p53 and inhibit its transcriptional activation function. Mot-2 overexpressing stable clones of NIH 3T3 cells were malignantly transformed, however, they had a high level of expression of a p53 downstream gene, p21WAF1. The present study was undertaken to elucidate possible molecular mechanism(s) of such upregulation. An inCreased level of p21WAF1, expression was detected in sta- ble transfectants although an exogenous reporter gene driven by p21WAF1, promoter exhibited lower activity in these cells suggesting that some post-transcriptional mechanism contributes to upregulation. Western analyses of transient and stable clones revealed that upregulation of p21WAF1, in stable NIH 3T3/mot-2 cells may be mediated by cyclin D1 and cdk-2.展开更多
An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective ...An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.展开更多
文摘Mot-2 protein is shown to interact with p53 and inhibit its transcriptional activation function. Mot-2 overexpressing stable clones of NIH 3T3 cells were malignantly transformed, however, they had a high level of expression of a p53 downstream gene, p21WAF1. The present study was undertaken to elucidate possible molecular mechanism(s) of such upregulation. An inCreased level of p21WAF1, expression was detected in sta- ble transfectants although an exogenous reporter gene driven by p21WAF1, promoter exhibited lower activity in these cells suggesting that some post-transcriptional mechanism contributes to upregulation. Western analyses of transient and stable clones revealed that upregulation of p21WAF1, in stable NIH 3T3/mot-2 cells may be mediated by cyclin D1 and cdk-2.
文摘An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features.
