Semi-supervised learning in unbalanced and heterogeneous networks

Community detection was a hot topic on network analysis, where the main aim is to perform unsupervised learning or clustering in networks. Recently, semi-supervised learning has received increasing attention among researchers. In this paper, we propose a new algorithm, called weighted inverse Laplacian (WIL), for predicting labels in partially labeled networks. The idea comes from the first hitting time in random walk, and it also has nice explanations both in information propagation and the regularization framework. We propose a partially labeled degree-corrected block model (pDCBM) to describe the generation of partially labeled networks. We show that WIL ensures the misclassification rate is of order $O(\frac{1}{d})$ for the pDCBM with average degree $d=\Omega(\log n),$ and that it can handle situations with greater unbalanced than traditional Laplacian methods. WIL outperforms other state-of-the-art methods in most of our simulations and real datasets, especially in unbalanced networks and heterogeneous networks

Topical Application of Fibroblast Growth Factor 10-PLGA Microsphere Accelerates Wound Healing via Inhibition of ER Stress

There is a high incidence of acute and chronic skin defects caused by various reasons in clinically practice. The repair and functional reconstruction of skin defects have become a major clinical problem, which needs to be solved urgently. Previous studies have shown that fibroblast growth factor 10 (FGF10) plays a functional role in promoting the proliferation, migration, and differentiation of epithelial cells. However, little is known about the effect of FGF10 on the recovery process after skin damage. In this study, we found that the expression of endogenous FGF10 was increased during wound healing. We prepared FGF10-loaded poly(lactic-co-glycolic acid) (FGF10-PLGA) microspheres, and it could sustain release of FGF10 both in vitro and in vivo, accelerating wound healing. Further analysis revealed that compared with FGF10 alone, FGF10-PLGA microspheres significantly improved granulation formation, collagen synthesis, cell proliferation, and blood vessel density. In the meantime, we found that FGF10-PLGA microspheres inhibited the expression of endoplasmic reticulum (ER) stress markers. Notably, activating ER stress with tunicamycin (TM) reduced therapeutic effects of FGF10-PLGA microspheres in wound healing, whereas inhibition of ER stress with 4-phenyl butyric acid (4-PBA) improved the function of FGF10-PLGA microspheres. Taken together, this study indicates that FGF10-PLGA microspheres accelerate wound healing presumably through modulating ER stress