FctClus: A Fast Clustering Algorithm for Heterogeneous Information Networks

<div><p>It is important to cluster heterogeneous information networks. A fast clustering algorithm based on an approximate commute time embedding for heterogeneous information networks with a star network schema is proposed in this paper by utilizing the sparsity of heterogeneous information networks. First, a heterogeneous information network is transformed into multiple compatible bipartite graphs from the compatible point of view. Second, the approximate commute time embedding of each bipartite graph is computed using random mapping and a linear time solver. All of the indicator subsets in each embedding simultaneously determine the target dataset. Finally, a general model is formulated by these indicator subsets, and a fast algorithm is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. The proposed fast algorithm, FctClus, is shown to be efficient and generalizable and exhibits high clustering accuracy and fast computation speed based on a theoretic analysis and experimental verification.</p></div

Comparison of computation speed(s).

<p>Comparison of computation speed(s).</p

The influence of k_<i>r</i> for clustering authors on <i>s</i>_{<i>small</i>.}

<p>The influence of k_<i>r</i> for clustering authors on <i>s</i>_{<i>small</i>.}</p

The influence of <i>k</i>_<i>r</i> for clustering papers on <i>s</i>_{<i>small</i>.}

<p>The influence of <i>k</i>_<i>r</i> for clustering papers on <i>s</i>_{<i>small</i>.}</p

The influence of <i>u</i> for clustering papers on <i>s</i>_{<i>small</i>.}

<p>The influence of <i>u</i> for clustering papers on <i>s</i>_{<i>small</i>.}</p

Distribution of running time for FctClus.

<p>Distribution of running time for FctClus.</p

The influence of <i>u</i> for clustering authors on <i>s</i>_{<i>small</i>.}

<p>The influence of <i>u</i> for clustering authors on <i>s</i>_{<i>small</i>.}</p

Type III Interferon Induces Distinct SOCS1 Expression Pattern that Contributes to Delayed but Prolonged Activation of Jak/STAT Signaling Pathway: Implications for Treatment Non-Response in HCV Patients

<div><p>Suppressor of cytokine signaling 1 (SOCS1) has long been thought to block type I interferon signaling. However, IFN-λ, a type III IFN with limited receptor expression in hepatic cells, efficiently inhibits HCV (Hepatitis C virus) replication in vivo with potentially less side effects than IFN-α. Previous studies demonstrated that type I and type III activated Janus kinase/signal transducer and activator of transcription (Jak/STAT) signaling pathway differently, with delayed but prolonged activation by IFN-λ stimulation compared to IFNα/β. However, the molecular mechanisms underlying this observation is not well understood. Here, we found that there are distinct differences in SOCS1 expression patterns in Huh-7.5.1 cells following stimulation with IFN-α and IFN-λ. IFN-λ induced a faster but shorter expression of SOCS1. Furthermore, we confirmed that SOCS1 over-expression abrogates anti-HCV effect of both IFN-α and IFN-λ, leading to increased HCV RNA replication in both HCV replicon cells and JFH1 HCV culture system. In line with this, SOCS1 over-expression inhibited STAT1 phosphorylation, attenuated IFN-stimulated response elements (ISRE) reporter activity, and blocked IFN-stimulated genes (ISGs) expression. Finally, we measured SOCS1 mRNA expression levels in peripheral blood mononuclear cells (PBMCs) with or without IFN-α treatment from 48 chronic hepatitis C patients and we found the baseline SOCS1 expression levels are higher in treatment non-responders than in responders before IFN-α treatment. Taken together, SOCS1 acts as a suppressor for both type I and type III IFNs and is negatively associated with sustained virological response (SVR) to IFN-based therapy in patients with HCV. More importantly, faster but shorter induction of SOCS1 by IFN-λ may contribute to delayed but prolonged activation of IFN signaling and ISG expression kinetics by type III IFN.</p></div

Emulation of a process-based estuarine hydrodynamic model

<p>Emulation modelling can be an effective alternative to traditional mechanistic approaches for complex environmental systems and, if carefully conceived, can offer significantly reduced run times and user expertise requirements. We present a case study of dynamic emulation for the domain of estuarine water quality modelling, by reporting the development and evaluation of a one-dimensional hydrodynamic model emulator. The proposed “neuroemulator” retains the dynamic nature of the process-based model utilizing a set of artificial neural networks. The underlying hydrodynamic model is routinely used for analysis and management of the northern reach of the San Francisco Bay-Delta estuary, a large complex region of strategic importance for water supply and ecosystem services on the Pacific coast of California, USA. The reduced computational expense of the emulator affords opportunities for direct use, as well as embedded use within other modelling frameworks such as those developed for reservoir operations and socio-hydrology.</p

Over-expression of SOCS1 blocked IFN-α and IFN-λ signaling pathway.

<p>Over-expression of SOCS1 repressed IFN-induced ISRE-luciferase activity (A), decreased IFN-α and IFN-λ induced ISG15 and MxA (B) mRNA and protein levels (C) and STAT1 phosphorylation (C) in Con1b replicon cell. (D) Western blot data analyzed by Quantity One are expressed as the means of ratios of targeted genes (pSTAT1, ISG15 and MxA) /β-actin. Con1b cells were cotransfected with pCR3.1 (mock) or pCR3.1/SOCS1, pISRE-luc and pRL-TK for 24 hours and then 100 IU/mL IFN-α and 50 ng/mL IFN-λ was added to the cells for 24 hours respectively. The firefly and Renilla luciferase activity was measured. Total RNA was harvested and reverse transcribed. Cells were transfected with pCR3.1 (mock) or pCR3.1/SOCS1 for 24 hours and then treated with 100 IU/mL IFN-α and 50 ng/mL IFN-λ respectively for 24 hours and the cells were collected. Cell lysates were harvested and the levels of mRNA expression of ISG15 and MxA were determined by quantitative real time PCR normalized to GAPDH. In addition, the cell were analyzed by immunoblotting with the indicated antibodies as described in Materials and Methods. The samples for Tyrosine phosphorylation of STAT1 (pSTAT1) were harvested after incubating with IFNs for 15 min. Shown is one representative Western blot out of three performed experiments. + with;—without. Data are presented as means ± SEM, n = 3. Error bars indicate standard error of mean (SEM). “*” means <i>p</i> values less than 0.05; “**” <i>p</i> values less than 0.01; “***” means <i>p</i> values less than 0.001.</p