Generalized synchronization-based partial topology identification of complex networks

summary:In this paper, partial topology identification of complex networks is investigated based on synchronization method. We construct the response networks consisting of nodes with sim-pler dynamics than that in the drive networks. By constructing Lyapunov function, sufficient conditions are derived to guarantee partial topology identification by designing suitable controllers and parameters update laws. Several numerical examples are provided to illustrate the effectiveness of the theoretical results

BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models

We introduce a new R package, BeSS, for solving the best subset selection problem in linear, logistic and Cox's proportional hazard (CoxPH) models. It utilizes a highly efficient active set algorithm based on primal and dual variables, and supports sequential and golden search strategies for best subset selection. We provide a C++ implementation of the algorithm using an Rcpp interface. We demonstrate through numerical experiments based on enormous simulation and real datasets that the new BeSS package has competitive performance compared to other R packages for best subset selection purposes

A Splicing Approach to Best Subset of Groups Selection

Best subset of groups selection (BSGS) is the process of selecting a small part of non-overlapping groups to achieve the best interpretability on the response variable. It has attracted increasing attention and has far-reaching applications in practice. However, due to the computational intractability of BSGS in high-dimensional settings, developing efficient algorithms for solving BSGS remains a research hotspot. In this paper,we propose a group-splicing algorithm that iteratively detects the relevant groups and excludes the irrelevant ones. Moreover, coupled with a novel group information criterion, we develop an adaptive algorithm to determine the optimal model size. Under mild conditions, it is certifiable that our algorithm can identify the optimal subset of groups in polynomial time with high probability. Finally, we demonstrate the efficiency and accuracy of our methods by comparing them with several state-of-the-art algorithms on both synthetic and real-world datasets.Comment: 49 pages, 7 figure

Characterization and antitumor activity of camptothecin from endophytic fungus Fusarium solani isolated from Camptotheca acuminate

Background: Camptothecin (CPT) is a potent drug against cancers, originally from plants. The endophytic fungi could produce the secondary metabolite same as the host and is used as medicine.Objectives: The aim of this paper was to investigate an endophytic fungal CPT with anti-neoplastic activity.Methods: Endophytic fungi were isolated from Camptotheca acuminata in China. CPT from strain S-019 was characterized by TLC, HPLC and EI-MS analysis. Anti-tumor activity of fungal CPT was detected by MTT and fluorescent dye methods using Vero and PC-3 cells.Results: A total of 94 endophytic fungi strains were isolated from tissues of C. acuminata and 16 fungi strains displayed cytotoxic activity on Vero or PC3 cells. Of which, the fungal strain S-019, classified as Fusarium solani, displayed impressive cytotoxic activity on cancer cells and was found to produce CPT by analysis of TLC, HPLC and EI-MS methods. Bioassay studies confirmed that the fungi CPT had potent cytotoxicity on Vero cells and induced apoptosis of Vero cells.Conclusion: The endophytic fungi from camptotheca trees are a reliable source for natural anticancer compounds. The endophytic fungi could produce CPT same as plant. The fungal CPT exhibited effective activity at inhibiting cell growth and inducing apoptosis on Vero cells.Keywords: Endophytic fungi, camptothecin, anti-tumor, Camptotheca acuminat

Nonparametric statistical inference via metric distribution function in metric spaces

The distribution function is essential in statistical inference and connected with samples to form a directed closed loop by the correspondence theorem in measure theory and the Glivenko-Cantelli and Donsker properties. This connection creates a paradigm for statistical inference. However, existing distribution functions are defined in Euclidean spaces and are no longer convenient to use in rapidly evolving data objects of complex nature. It is imperative to develop the concept of the distribution function in a more general space to meet emerging needs. Note that the linearity allows us to use hypercubes to define the distribution function in a Euclidean space. Still, without the linearity in a metric space, we must work with the metric to investigate the probability measure. We introduce a class of metric distribution functions through the metric only. We overcome this challenging step by proving the correspondence theorem and the Glivenko-Cantelli theorem for metric distribution functions in metric spaces, laying the foundation for conducting rational statistical inference for metric space-valued data. Then, we develop a homogeneity test and a mutual independence test for non-Euclidean random objects and present comprehensive empirical evidence to support the performance of our proposed methods. Supplementary materials for this article are available online