Pinning Complex Networks by a Single Controller

In this paper, without assuming symmetry, irreducibility, or linearity of the couplings, we prove that a single controller can pin a coupled complex network to a homogenous solution. Sufficient conditions are presented to guarantee the convergence of the pinning process locally and globally. An effective approach to adapt the coupling strength is proposed. Several numerical simulations are given to verify our theoretical analysis

Cluster synchronization in networks of coupled non-identical dynamical systems

In this paper, we study cluster synchronization in networks of coupled non-identical dynamical systems. The vertices in the same cluster have the same dynamics of uncoupled node system but the uncoupled node systems in different clusters are different. We present conditions guaranteeing cluster synchronization and investigate the relation between cluster synchronization and the unweighted graph topology. We indicate that two condition play key roles for cluster synchronization: the common inter-cluster coupling condition and the intra-cluster communication. From the latter one, we interpret the two well-known cluster synchronization schemes: self-organization and driving, by whether the edges of communication paths lie at inter or intra-cluster. By this way, we classify clusters according to whether the set of edges inter- or intra-cluster edges are removable if wanting to keep the communication between pairs of vertices in the same cluster. Also, we propose adaptive feedback algorithms on the weights of the underlying graph, which can synchronize any bi-directed networks satisfying the two conditions above. We also give several numerical examples to illustrate the theoretical results

Lawsone inhibits cell growth and improves the efficacy of cisplatin in SKOV-3 ovarian cancer cell lines

Background: Lawsone (LWS) is a colored napthoquinone moiety found in plant Lawsonia inermis L. (Lythraceae) it is used as precursor for synthesis some anticancer drugs. In present research we evaluate the effect of LWS alone and in combination with Cisplatin (CP) on SKOV-3 ovarian cancer cells.Materials and Methods: Cell proliferation studies were done by MTT assay while the cell apoptosis studies were carried by performing nuclear Hoechst 33258 staining. Cell cycle analysis was done by Flow cytometric studies, Immunoblotting studies for protein expression was done, proteins controlling cell cycle such as cyclinD1, cyclin E, cyclin A, cyclin B1 and Cip1/p21 and p53 which also are cyclin dependent inhibitors of protein kinase were estimated. Annexin V staining was done to mark extent of apoptosis, expression of apoptotic protein such as Bcl-2, Bax, Bax/Bcl-2 ratio and activity of caspase 3.Results: LWS alone and in combination with CP suppressed the growth of SKOV-3 cells in dose-dependent manner. Treatment inhibited SKOV-3 cells by arresting of G1/G0 phase in the cell cycle, by increasing the expression of p53 and Cip1/p21 followed by decreasing levels of two important proteins cyclin E and cyclin D1. LWS was found to induce apoptosis via decreasing the levels of Bcl-2, improving Bax:Bcl-2 ratio and activating caspase 3.Conclusion: Results of this study clearly indicate LWS alone and in combination with CP have antiproliferative effect, causes apoptosis of SKOV-3 cells via suppressing Bcl-2. LWS could be a useful compound for treatment of ovarian cancer.Keywords: Lawsone, Ovarian cancer, SKOV-3 cells, Cisplati

Dual Skipping Networks

Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-to-fine object categorization. Such a network has two branches to simultaneously deal with both coarse and fine-grained classification tasks. Specifically, we propose a layer-skipping mechanism that learns a gating network to predict which layers to skip in the testing stage. This layer-skipping mechanism endows the network with good flexibility and capability in practice. Evaluations are conducted on several widely used coarse-to-fine object categorization benchmarks, and promising results are achieved by our proposed network model.Comment: CVPR 2018 (poster); fix typ