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Nanocellulose is a renewable material with excellent properties. At present, nanocellulose powder is mainly prepared by high-cost spray drying, which severely limits its output and quality. In this paper, we propose an innovative method to prepare dispersible nanocellulose powder by simple precipitation and conventional evaporation. Potassium acetate was added to CNC (a kind of nanocellulose) suspension to precipitate and purify CNC. The added potassium acetate was dissolved in ethanol and removed. Then dispersible nanocellulose powder can be obtained by simple evaporation. In CNC purification, we discussed the combination of different electrolytes and CNCs. It was found that although the monovalent electrolytes lead to weak aggregation with CNC, the resulting dry CNC powder had the best dispersion. In CNC drying, we discussed the effects of different dispersants and different drying methods on the forming of dry CNC powder. It is revealed that ethanol is the best dispersant tested in this paper because it can only form a single hydrogen bond with CNC and is easy to evaporate. In the evaporation of CNC and ethanol mixture, it will not lead to the aggregation of CNC fibers. It is hopeful that the new method may greatly reduce the cost of the preparation of nanocellulose powder.

MiRNA is reported as an important biomarker candidate and therapeutic target in a variety of diseases and cancers. However, ultrasensitive, straightforward, isothermal, low-cost and credible methods for the detection of miRNA from clinical specimens still remain challenges. Here, we establish an isothermal, highly sensitive and specific method for miRNA analysis based on a novel ligation-promoted hyperbranched rolling circle amplification (L-HRCA). This strategy exhibits 103-fold sensitivity enhancement compared to T4 ligase-assisted target-primer BRCA. The excellent specificity of L-HRCA allows for discriminating target miRNA from negative miRNAs. This method is applied to analyse the expression level of miR-21 in hepatocellular carcinoma. These results indicated that the L-HRCA strategy will become a promising tool to quantify miRNA in early clinical diagnostics.

Nanocellulose is a kind of cellulose based nano material with fantastic properties and numerous potential applications. However, due to the fact that nanocellulose exhibits colloidal properties when its concentration is high, the drying of nanocellulose has always been an urgent problem to be solved. To address this problem, the precipitation and drying mechanism of cellulose nanocrystal (CNC), one of the most common types of nanocellulose, was studied in this paper. The CNC was precipitated from the aqueous suspension by salting out to avoid the CNC colloidal state when concentrated in water. The obtained CNC precipitation with a small amount of water and the actively added electrolyte was dehydrated and purified by solvent displacement with volatile organic. Then CNC powder can be taken shape by drying the mixture of CNC and organic solvent with different drying methods. The mechanism of CNC precipitation from aqueous suspension and CNC powder formation from volatile organic solvent was studied. After comprehensive consideration, a method for preparing re-dispersible CNC powder was established, and the properties of this powder were studied, that provided a solution for the industrial preparation and application of nanocellulose.

Incorporating social relations into the recommendation system, i.e. social recommendation, has been widely studied in academic and industrial communities. While many promising results have been achieved, existing methods mostly assume that the social relations can be homogeneously applied to all the items, which is not practical for users' actually diverse preferences. In this paper, we argue that the effect of the social relations should be inhomogeneous, that is, two socially-related users may only share the same preference on some specific items, while for the other products, their preferences can be inconsistent or even contradictory. Inspired by this idea, we build a novel social recommendation model, where the traditional pair-wise "user-user'' relation is extended to the triple relation of "user-item-user''. To well handle such high-order relations, we base our framework on the hypergraph. More specifically, each hyperedge connects a user-user-item triplet, representing that the two users share similar preferences on the item. We develop a Social HyperGraph Convolutional Network (short for SHGCN) to learn from the complex triplet social relations. With the hypergraph convolutional networks, the social relations can be modeled in a more fine-grained manner, which more accurately depicts real users' preferences, and benefits the recommendation performance. Extensive experiments on two real-world datasets demonstrate our model's effectiveness. Studies on data sparsity and hyper-parameter studies further validate our model's rationality. Our codes and dataset are available at https://github.com/ziruizhu/SHGCN.

Federated Learning (FL) is a privacy-protected machine learning paradigm that allows model to be trained directly at the edge without uploading data. One of the biggest challenges faced by FL in practical applications is the heterogeneity of edge node data, which will slow down the convergence speed and degrade the performance of the model. For the above problems, a representative solution is to add additional constraints in the local training, such as FedProx, FedCurv and FedCL. However, the above algorithms still have room for improvement. We propose to use the aggregation of all models obtained in the past as new constraint target to further improve the performance of such algorithms. Experiments in various settings demonstrate that our method significantly improves the convergence speed and performance of the model.