Coronin-1C is an important F-actin binding protein which is critical for cell motility. Furthermore, the expression of this protein was found to be increased in diffuse tumors and was correlated with the degree of tumor malignancy. However, the mechanism(s) through which this protein enhances malignancy in hepatocellular carcinoma (HCC) is poorly understood. In this study, we found that Coronin-1C was overexpressed in human HCC tissues compared with the adjacent non-tumor tissues. Overexpression of Coronin-1C enhanced the cell migration in the human HCC cell line BEL-7402, whereas suppressed cell migration and proliferation were observed in Coronin-1C-knockdown BEL-7402 cells together with impaired cell polarity, disrupted cytoskeleton and decreased Rac-1 activation. Moreover, the Coronin-1C knockdown cells displayed a lower degree of malignancy by inducing smaller tumors in nude mice. Thus, we demonstrated a relationship between Coronin-1C overexpression and human HCC growth through enhancement of tumor cell proliferation and migration, which are correlated with Rac-1 activation.
In this paper we propose a fast frequency domain saliency detection method that is also biologically plausible, referred to as frequency domain divisive normalization (FDN). We show that the initial feature extraction stage, common to all spatial domain approaches, can be simplified to a Fourier transform with a contourletlike grouping of coefficients, and saliency detection can be achieved in frequency domain. Specifically, we show that divisive normalization, a model of cortical surround inhibition, can be conducted in frequency domain. Since Fourier coefficients are global in space, we extend to this model by conducting piecewise FDN (PFDN) using overlapping local patches to provide better biological plausibility. Not only do FDN and PFDN outperform current state-of-the-art methods in eye fixation prediction, they are also faster. Speed and simplicity are advantages of our frequency domain approach, and its biological plausibility is the main contribution of our paper.
Material recognition is the process of recognizing the constituent material of the object, and it is a crucial step in many fields. Therefore, it is valuable to create a system that could achieve material recognition automatically. This paper proposes a novel approach named ensemble learning for material recognition with convolutional neural networks (CNNs). In the proposed method, firstly, a CNN model is trained to extract the image features. Secondly, knowledge-based classifiers are learned to get the probabilities of the test sample that belongs to different material categories. Finally, we propose three different ways to learn the ensemble features, which achieves higher recognition accuracy. The great difference from the prior work is that we combine the knowledge-based classifiers on probability level. Experimental results show that the proposed ensemble feature learning method performs better than the state-of-the-art material recognition methods and can archive a much higher recognition accuracy.
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