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Immune potentiators, termed adjuvant, trigger early innate immune responses to ensure the generation of robust and long‐lasting adaptive immune responses of vaccines. Here we present study that takes advantage of a self‐assembling small molecule library for the development of a novel vaccine adjuvant. Cell‐based screening of the library and subsequent structural optimization led to the discovery of a simple, chemically tractable deoxycholate derivative (molecule 6 , also named cholicamide) whose well‐defined nano‐assembly potently elicits innate immune responses in macrophages and dendritic cells. Functional and mechanistic analyses indicate that the virus‐like assembly is engulfed inside cells and stimulates the innate immune response through toll‐like receptor 7 (TLR7), an endosomal TLR that detects single‐stranded viral RNA. As an influenza vaccine adjuvant in mice, molecule 6 was as potent as Alum, a clinically used adjuvant. The studies described here paves the way for a new approach to discovering and designing self‐assembling small‐molecule adjuvants against pathogens, including emerging viruses.

Steganography, as one of the three basic information security systems, has long played an important role in safeguarding the privacy and confidentiality of data in cyberspace. The text is the most widely used information carrier in people's daily life, using text as a carrier for information hiding has broad research prospects. However, due to the high coding degree and less information redundancy in the text, it has been an extremely challenging problem to hide information in it for a long time. In this paper, we propose a steganography method which can automatically generate steganographic text based on the Markov chain model and Huffman coding. It can automatically generate fluent text carrier in terms of secret information which need to be embedded. The proposed model can learn from a large number of samples written by people and obtain a good estimate of the statistical language model. We evaluated the proposed model from several perspectives. Experimental results show that the performance of the proposed model is superior to all the previous related methods in terms of information imperceptibility and information hidden capacity., Comment: Submitted to IETE Technical Review