Validation of our algorithm.

<p>We compare our local Nusselt number of a non-slip Newtonian fluid in a tube with that in the previous work for testing the algorithm.</p

The thermally fully developed heat transfer rates for different boundary conditions.

<p>The thermally fully developed Nusselt numbers for 0, 1%, 3% and 5% as a function of the slip length with both constant wall temperature and constant heat flux boundary conditions are compared.</p

The volumetric flow rate of the nanofluid in a thin tube.

<p>We calculate the volumetric flow rates as a function of the pressure gradient for 0, 1%, 3% and 5% with radius and slip length .</p

Values of the fluid consistency coefficient <i>m</i> and flow behavior index <i>n</i> for different [13], [14].

<p>Values of the fluid consistency coefficient <i>m</i> and flow behavior index <i>n</i> for different <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037274#pone.0037274-Santra1" target="_blank">[13]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037274#pone.0037274-Putra1" target="_blank">[14]</a>.</p

The temperature profiles of the nanofluid corresponding to different nanoparticle volume fractions.

<p>(<b>A</b>)–(<b>D</b>) Comparison of the axial temperature profiles for 0, 1%, 3% and 5% with a fixed pressure gradient equal to Pa/m and dimensionless slip length , showing the effect of nanoparticle volume fraction on the temperature distribution.</p

The volumetric flow rate of the nanofluid in a thicker tube.

<p>The volumetric flow rates as a function of the pressure gradient are calculated for 0, 1%, 3% and 5% with radius and slip length .</p

The heat transfer rates corresponding to different boundary slip lengths.

<p>Comparison of the heat transfer curves for 0, 0.01, 0.05 and 0.1 with a fixed nanoparticle volume fraction 5% and pressure gradient Pa/m, showing the effect of slip length on the heat transfer rate.</p

The heat transfer rates corresponding to different nanoparticle volume fractions.

<p>The heat transfer curves are calculated for 0, 3% and 5% with a fixed dimensionless slip length 0.01 and pressure gradient Pa/m, showing the effect of nanoparticle volume fraction on the heat transfer rate; the non-slip curve for 0 is also plotted for revealing the slip effect.</p

The velocity profiles for different nanoparticle volume fraction and pressure gradient.

<p>(<b>A</b>) The velocity profiles for 0, 1%, 3% and 5% with the pressure gradient equaling Pa/m. (<b>B</b>) the velocity profiles for 0, 1%, 3% and 5% with the pressure gradient equaling Pa/m.</p

Hepatitis C Virus Protein Interaction Network Analysis Based on Hepatocellular Carcinoma

<div><p>Epidemiological studies have validated the association between hepatitis C virus (HCV) infection and hepatocellular carcinoma (HCC). An increasing number of studies show that protein-protein interactions (PPIs) between HCV proteins and host proteins play a vital role in infection and mediate HCC progression. In this work, we collected all published interaction between HCV and human proteins, which include 455 unique human proteins participating in 524 HCV-human interactions. Then, we construct the HCV-human and HCV-HCC protein interaction networks, which display the biological knowledge regarding the mechanism of HCV pathogenesis, particularly with respect to pathogenesis of HCC. Through in-depth analysis of the HCV-HCC interaction network, we found that interactors are enriched in the JAK/STAT, p53, MAPK, TNF, Wnt, and cell cycle pathways. Using a random walk with restart algorithm, we predicted the importance of each protein in the HCV-HCC network and found that AKT1 may play a key role in the HCC progression. Moreover, we found that NS5A promotes HCC cells proliferation and metastasis by activating AKT/GSK3β/β-catenin pathway. This work provides a basis for a detailed map tracking new cellular interactions of HCV and identifying potential targets for HCV-related hepatocellular carcinoma treatment.</p></div