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In this paper, we propose a visual saliency detection algorithm from the perspective of reconstruction errors. The image boundaries are first extracted via superpixels as likely cues for background templates, from which dense and sparse appearance models are constructed. For each image region, we first compute dense and sparse reconstruction errors. Second, the reconstruction errors are propagated based on the contexts obtained from K-means clustering. Third, pixel-level saliency is computed by an integration of multi-scale reconstruction errors and refined by an object-biased Gaussian model. We apply the Bayes formula to integrate saliency measures based on dense and sparse reconstruction errors. Experimental results show that the proposed algorithm performs favorably against seventeen state-of-the-art methods in terms of precision and recall. In addition, the proposed algorithm is demonstrated to be more effective in highlighting salient objects uniformly and robust to background noise.

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Aptamers are oligonucleotide sequences with a length of about 25−80 bases which have abilities to bind to specific target molecules that rival those of monoclonal antibodies. They are attracting great attention in diverse clinical translations on account of their various advantages, including prolonged storage life, little batch-to-batch differences, very low immunogenicity, and feasibility of chemical modifications for enhancing stability, prolonging the half-life in serum, and targeted delivery. In this Review, we demonstrate the emerging aptamer discovery technologies in developing advanced techniques for producing aptamers with high performance consistently and efficiently as well as requiring less cost and resources but offering a great chance of success. Further, the diverse modifications of aptamers for therapeutic applications including therapeutic agents, aptamer−drug conjugates, and targeted delivery materials are comprehensively summarized.

Most salient object detection approaches use U-Net or feature pyramid networks (FPN) as their basic structures. These methods ignore two key problems when the encoder exchanges information with the decoder: one is the lack of interference control between them, the other is without considering the disparity of the contributions of different encoder blocks. In this work, we propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. We design a novel gated dual branch structure to build the cooperation among different levels of features and improve the discriminability of the whole network. Through the dual branch design, more details of the saliency map can be further restored. In addition, we adopt the atrous spatial pyramid pooling based on the proposed "Fold" operation (Fold-ASPP) to accurately localize salient objects of various scales. Extensive experiments on five challenging datasets demonstrate that the proposed model performs favorably against most state-of-the-art methods under different evaluation metrics.