MicroRNAs in the Functional Defects of Skin Aging

Humankind has always been intrigued by death, as illustrated by the eternal quest for the fountain of youth. Aging is a relentless biological process slowly progressing as life cycle proceeds. Indeed, aging traduces an accumulation of physiological changes over time that render organisms more likely to die. Thus, despite our mastery of advanced technologies and robust medical knowledge, defining the molecular basis of aging to control lifespan is still currently one of the greatest challenges in biology. In mammals, the skin is the ultimate multitasker vital organ, protecting organisms from the world they live in. As a preferential interface with the environment, the skin is reflecting the internal physiological balances. The maintenance of these balances, called homeostasis, depends on the concurrent assimilation of diversified signals at the cellular level. MicroRNAs (miRNAs) are noncoding RNAs that regulate gene expression by mRNAs degradation or translational repression. Their relatively recent discovery in 2000 provided new insights into the understanding of the gene regulatory networks. In this chapter, we focused on the role of three miRNA families, namely miR-30, miR-200, and miR-181, playing a key role in the progression of the skin aging process, with particular input in mechanistic considerations related to autophagy, oxidative stress, and mitochondrial homeostasis

LOW RESOLUTION CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC TARGET RECOGNITION

International audienceIn this work, we present an extended study of image representations for automatic target recognition (ATR). More specifically , we tackle the issue of the image resolution influence on the classification performances, an understudied yet major parameter in image classification. Besides, we propose a parallel between low-resolution image recognition and image classification in a fine-grained context. Indeed, in these two particular cases, the main difficulty is to discriminate small details on very similar objects. In this paper, we evaluate Fisher Vectors and deep representations on two significant publicly available fine-grained oriented datasets with respect to the input image resolution. We also introduce LR-CNN, a deep structure designed for classification of low-resolution images with strong semantic content. This net provides rich compact features and outperforms both pre-trained deep features and Fisher Vectors. We also present visual results of our LR-CNN on Thales near-infrared images