Lonicera japonica is a typical Chinese herbal medicine. We previously reported a method to isolate polysaccharides from Lonicera japonica (LJP). In this study, we first performed a qualitative analysis of LJP using the Fourier Transform Infrared Spectrometer (FT-IR) and explored the monosaccharide composition of LJP using the pre-column derivatization high performance liquid chromatography (HPLC) method. We then investigated the immunomodulatory function of LJP in cyclophosphamide (CTX)-induced immunosuppressed mouse models. The results showed that LJP had the characteristic absorption of typical polysaccharides consisting of 6 types of monosaccharides. In addition, LJP can increase significantly the organ index, splenic lymphocyte proliferation, macrophage phagocytosis, and natural killer (NK) cell activity in CTX-treated mice. LJP could also restore the levels of serum cytokines interleukin (IL-2), tumor necrosis factor (TNF-α) and Interferon-γ (IFN-γ) in the CTX-treated mice. Finally, the results on measuring the T-lymphocytes subsets of spleen also confirmed LJP-induced immunomodulatory activity in immunosuppressed mice from another perspective. Therefore, LJP could be used as a potential immunomodulatory agent.

The formation of Breslow intermediates in the reaction of 1,2,3-triazolylidenes (mesoionic carbenes) with aldehydes is reversible. The benzoin condensation is inhibited in deuterated methanol, allowing for H/D exchange at formyl groups.

Silicone sealants with low modulus and high elongation were prepared by using ketoxime silane as chain extender agent, and a novel silane coupling agent acting as adhesion promoting agent was synthesized. Mechanical properties of vulcanized polydimethylsiloxane (PDMS) filled with large amounts of carbonate calcium (CaCO 3 ) and dynamic viscoelastic properties of unvulcanized samples were investigated through electronic multifunctional tensile tests, dynamic mechanical analyzers, and dynamic rheological measurements. The results of mechanical tests indicate that diminishing the particle diameter size, narrowing the particle diameter distribution, and increasing the filler amount lead to a relative high tensile strength and modulus at 100% elongation, but a relative low elongation at break. The reasons for these are believed to be the evolution of molecular interactions and the formation of additional physical crosslinking induced by the filler network. Compared to virgin PDMS, there is a significant elevation of glass transition temperature with filler addition. On the other hand, the results of dynamic rheological measurements reveal that as filler amount increases, the span of the linear viscoelastic region in which dynamic storage modulus (G 0 ) is constant in low strain amplitude narrows. However, a characteristic plateau phenomenon appears in low frequency regions together with increasing the width and height of the modulus plateau. This phenomenon is also ascribed to the formation of a filler network due to filler-polymer and filler-filler interaction.

Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answered is: which residue can be hydroxylated, and which one cannot. The answer will not only help understand the mechanism of hydroxylation but can also benefit the development of new drugs. In this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (CNN) and long short-term memory network (LSTM). We employed a pseudo amino acid composition (PseAAC) method to construct valid benchmark datasets based on a sliding window strategy and used the position-specific scoring matrix (PSSM) to represent samples as inputs to the deep learning model. In addition, we compared our method with popular predictors including CNN, iHyd-PseAAC, and iHyd-PseCp. The results for 5-fold cross-validations all demonstrated that our method significantly outperforms the other methods in prediction accuracy.