Modularity functions maximization with nonnegative relaxation facilitates community detection in networks

We show here that the problem of maximizing a family of quantitative functions, encompassing both the modularity (Q-measure) and modularity density (D-measure), for community detection can be uniformly understood as a combinatoric optimization involving the trace of a matrix called modularity Laplacian. Instead of using traditional spectral relaxation, we apply additional nonnegative constraint into this graph clustering problem and design efficient algorithms to optimize the new objective. With the explicit nonnegative constraint, our solutions are very close to the ideal community indicator matrix and can directly assign nodes into communities. The near-orthogonal columns of the solution can be reformulated as the posterior probability of corresponding node belonging to each community. Therefore, the proposed method can be exploited to identify the fuzzy or overlapping communities and thus facilitates the understanding of the intrinsic structure of networks. Experimental results show that our new algorithm consistently, sometimes significantly, outperforms the traditional spectral relaxation approaches

A Completed Multiple Threshold Encoding Pattern for Texture Classification

The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, existing center based sub-patterns are much weaker than other local neighborhood based sub-patterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiple threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiple threshold encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into a 3-bit MTCP. Furthermore, this paper proposes a completed multiple threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiple threshold encoding pattern achieves superior texture classification performance

LMP1 Signaling Pathway Activates IRF4 in Latent EBV Infection and a Positive Circuit Between PI3K and Src Is Required

Interferon (IFN) regulatory factors (IRFs) have crucial roles in immune regulation and oncogenesis. We have recently shown that IRF4 is activated through c-Src-mediated tyrosine phosphorylation in virus-transformed cells. However, the intracellular signaling pathway triggering Src activation of IRF4 remains unknown. In this study, we provide evidence that Epstein–Barr virus (EBV) latent membrane protein 1 (LMP1) promotes IRF4 phosphorylation and markedly stimulates IRF4 transcriptional activity, and that Src mediates LMP1 activation of IRF4. As to more precise mechanism, we show that LMP1 physically interacts with c-Src, and the phosphatidylinositol 3 kinase (PI3K) subunit P85 mediates their interaction. Depletion of P85 by P85-specific short hairpin RNAs disrupts their interaction and diminishes IRF4 phosphorylation in EBV-transformed cells. Furthermore, we show that Src is upstream of PI3K for activation of both IRF4 and Akt. In turn, inhibition of PI3K kinase activity by the PI3K-speicfic inhibitor LY294002 impairs Src activity. Our results show that LMP1 signaling is responsible for IRF4 activation, and further characterize the IRF4 regulatory network that is a promising therapeutic target for specific hematological malignancies

MDL, Penalized Likelihood, and Statistical Risk

Abstract-We determine, for both countable and uncountable collections of functions, information-theoretic conditions on a penalty pen(f ) such that the optimizerf of the penalized log likelihood criterion log 1/likelihood(f )+pen(f ) has risk not more than the index of resolvability corresponding to the accuracy of the optimizer of the expected value of the criterion. If F is the linear span of a dictionary of functions, traditional descriptionlength penalties are based on the number of non-zero terms (the 0 norm of the coefficients). We specialize our general conclusions to show the 1 norm of the coefficients times a suitable multiplier λ is also an information-theoretically valid penalty

An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis.

Purpose: To detect visual field (VF) progression by analyzing spatial pattern changes. Methods: We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists. Results: In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P \u3c 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60). Conclusions: The archetype method can inform clinicians of VF progression patterns

Enhanced Virus-Specific CD8\u3csup\u3e+\u3c/sup\u3e T Cell Responses by Listeria Monocytogenes-Infected Dendritic Cells in the Context of Tim-3 Blockade

In this study, we engineered Listeria monocytogens (Lm) by deleting the LmΔactA/ΔinlB virulence determinants and inserting HCV-NS5B consensus antigens to develop a therapeutic vaccine against hepatitis C virus (HCV) infection. We tested this recombinant Lm-HCV vaccine in triggering of innate and adaptive immune responses in vitro using immune cells from HCV-infected and uninfected individuals. This live-attenuated Lm-HCV vaccine could naturally infect human dendritic cells (DC), thereby driving DC maturation and antigen presentation, producing Th1 cytokines, and triggering CTL responses in uninfected individuals. However, vaccine responses were diminished when using DC and T cells derived from chronically HCV-infected individuals, who express higher levels of inhibitory molecule Tim-3 on immune cells. Notably, blocking Tim-3 signaling significantly improved the innate and adaptive immune responses in chronically HCV-infected patients, indicating that novel strategies to enhance the potential of antigen presentation and cellular responses are essential for developing an effective therapeutic vaccine against HCV infection

Emulating opportunistic networks with KauNet Triggers

In opportunistic networks the availability of an end-to-end path is no longer required. Instead opportunistic networks may take advantage of temporary connectivity opportunities. Opportunistic networks present a demanding environment for network emulation as the traditional emulation setup, where application/transport endpoints only send and receive packets from the network following a black box approach, is no longer applicable. Opportunistic networking protocols and applications additionally need to react to the dynamics of the underlying network beyond what is conveyed through the exchange of packets. In order to support IP-level emulation evaluations of applications and protocols that react to lower layer events, we have proposed the use of emulation triggers. Emulation triggers can emulate arbitrary cross-layer feedback and can be synchronized with other emulation effects. After introducing the design and implementation of triggers in the KauNet emulator, we describe the integration of triggers with the DTN2 reference implementation and illustrate how the functionality can be used to emulate a classical DTN data-mule scenario