Fluorescent dextrans are commonly used as macropinocytic probes to study the properties of endocytic cargoes; however, the effect of the size of dextrans on endocytic mechanisms has not been carefully analyzed. By using chemical and siRNA inhibition of individual endocytic pathways, we evaluated the internalization of two commonly used dextrans, Dex10 (dextran 10 kDa) and Dex70 (dextran 70 kDa), in mammalian HeLa cells and Caenorhabditis elegans coelomocytes. We revealed that Dex70 enters these two cell types predominantly via clathrin- and dynamin-independent and amiloride-sensitive macropinocytosis process; Dex10, on the other hand, enters the two cell types through clathrin-/dynamin-dependent micropinocytosis in addition to macropinocytosis. In addition, although different-sized dextrans follow different endocytic processes, they share common post-endocytic events. Herein, though straightforward, our studies support that the size of nanomaterials could play a paramount role in their inclusion into endocytic vesicles and suggest that care should be taken while selecting endocytic pathway markers. Based on our results, we propose that Dex70 is a better probe for macropinocytosis, whereas Dex10 and smaller molecules are better for probing general fluid-phase endocytosis, which includes macropinocytic and micropinocytic processes.

It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like disgusted are relatively rare comparing to other labels like happy or sad. In this paper, we propose a data augmentation method using generative adversarial networks (GAN). It can complement and complete the data manifold and find better margins between neighboring classes. Specifically, we design a framework using a CNN model as the classifier and a cycle-consistent adversarial networks (CycleGAN) as the generator. In order to avoid gradient vanishing problem, we employ the least-squared loss as adversarial loss. We also propose several evaluation methods on three benchmark datasets to validate GAN's performance. Empirical results show that we can obtain 5%∼10% increase in the classification accuracy after employing the GAN-based data augmentation techniques.

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