The Relationship between Microstructure and Mechanical Properties of PBST Two-Component Crystalline Random Copolymers with Different BT Contents.

The mechanical properties of two-component crystalline random copolymers are primarily based on their microstructure. At the same time, the influence of the composition on the crystallization behavior and crystal structure of these materials is also well known. Thus, in this study, a poly (butylene succinate-co-butylene terephthalate) random copolymer (PBST) with different molar ratios of butylene terephthalate (BT) was prepared. A systematic analysis of the crystallization behavior, crystal structure, and mechanical properties of PBST with different BT contents was carried out using WAXD, SAXS, and DSC analyses. The investigations showed that PBST-37.5 containing 37.5 mol% of BT content had the lowest strength and highest elasticity among the different compositions. This was because the two-component crystallization of poly (butylene terephthalate) (PBT) and poly (butylene succinate) (PBS) was greatly inhibited at the corresponding BT composition and the crystal growth was the least perfect, imparting poor strength to the PBT-37.5. Alternately, when the content of BT was 32.5 mol% in the PBST, the PBS segment could crystallize, and both PBT and PBS crystals were formed in the PBST-32.5. Thus, PBST-32.5 showed a higher material hardness than PBST-37.5. In contrast, when the BT content was greater than 37.5 mol% in the PBST, only PBT crystals existed in the PBST copolymer. Further, as the BT content increased, the crystal size of PBT gradually increased, which led to a closer packing of the crystal arrangement, increasing the crystallinity. This led to a gradual increase in the strength of the PBST material and a gradual decrease in its elasticity.

Predicting human disease-associated circRNAs based on locality-constrained linear coding.

Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs.