A ThDP-dependent enzymatic carboligation reaction involved in Neocarazostatin A tricyclic carbazole formation

Acknowledgements This work was supported by grants from the National Natural Science Foundation of China (31570033 to Y. Y.) and the Leverhulme Trust-Royal Society Africa Award (AA090088 to K. K and H. D.). Open access via RSC Gold 4 Gold.Peer reviewedPublisher PD

Critical behavior of a stochastic anisotropic Bak-Sneppen model

In this paper we present our study on the critical behavior of a stochastic anisotropic Bak-Sneppen (saBS) model, in which a parameter $\alpha$ is introduced to describe the interaction strength among nearest species. We estimate the threshold fitness $f_c$ and the critical exponent $\tau_r$ by numerically integrating a master equation for the distribution of avalanche spatial sizes. Other critical exponents are then evaluated from previously known scaling relations. The numerical results are in good agreement with the counterparts yielded by the Monte Carlo simulations. Our results indicate that all saBS models with nonzero interaction strength exhibit self-organized criticality, and fall into the same universality class, by sharing the universal critical exponents.Comment: 9 pages, 7 figures. arXiv admin note: text overlap with arXiv:cond-mat/9803068 by other author

Community detection by label propagation with compression of flow

The label propagation algorithm (LPA) has been proved to be a fast and effective method for detecting communities in large complex networks. However, its performance is subject to the non-stable and trivial solutions of the problem. In this paper, we propose a modified label propagation algorithm LPAf to efficiently detect community structures in networks. Instead of the majority voting rule of the basic LPA, LPAf updates the label of a node by considering the compression of a description of random walks on a network. A multi-step greedy agglomerative strategy is employed to enable LPAf to escape the local optimum. Furthermore, an incomplete update condition is also adopted to speed up the convergence. Experimental results on both synthetic and real-world networks confirm the effectiveness of our algorithm