as the function of for the two observed datasets, showing that the common feature, , is positively correlated with the object mass.

<p> as the function of for the two observed datasets, showing that the common feature, , is positively correlated with the object mass.</p

, , <i>E</i>, <i>r</i>, <i>D</i> and <i>H</i> as the function of ratio of added links.

<p>The result is obtained by averaging over 50 interdependent network realizations. The dash line highlights the corresponding result of <i>ST</i> network. Results from five representative metrics show that the <i>GR</i> model (blue triangle) is the best one to approach the original <i>ST</i> network.</p

<i>InnerS</i> vs. recommendation length of the three algorithms for <i>Del.icio.us</i> and <i>Movielens</i>.

<p>The result is obtained by averaging over 50 independent realizations of random data division, and yellow lines represent the error intervals. The parameter for algorithm (III) is set to 0.001. Results on both datasets show that the gravity-model based algorithm (black) outperforms other two baselines.</p

Evolutionary results of four corresponding networks.

<p> represents the size of the giant component, denotes the clustering coefficient, and are respectively the assortative coefficient and average distance of network, and denotes the network heterogeneity. In the last three rows, it presents both real value of corresponding metric and the error interval (separated by slash), which is calculated as: , where is the metric value of current model and is the corresponding value of <i>ST</i> network. Each value is obtained by averaging over 50 interdependent network realizations.</p

Comparisons of <i>AUC</i> results of respectively considering the effects of mass (), common interest (), and as well as three algorithms (algorithm I, II and III).

<p>The result is obtained by averaging over 50 independent realizations of random data division, and the three digital numbers behind the signs are the corresponding error intervals. The parameter for algorithm (III) is set to 0.001.</p

<i>Precision</i> vs. recommendation length of the three algorithms for <i>Del.icio.us</i> and <i>Movielens</i>.

<p>The result is obtained by averaging over 50 independent realizations of random data division , and yellow lines represent the error intervals. The parameter for algorithm (III) is set to 0.001. Results on both datasets show that the gravity-model based algorithm (black) outperforms other two baselines.</p

Expression of Flot-2 and EGFR proteins in lung SCC and lung ADC compared to the control of non-cancerous lung tissues.

<p>Results showed that there were significant differences between the groups which were statistically evaluated by chi-square test.</p

Kaplan-Meier cures for overall survival of lung SCC and ADC patients with expression of Flot-2 and EGFR.

<p>Kaplan-Meier analysis was used to plot the overall survival curves of 159 cases of lung SCC and 193 cases of lung ADC patients with differential expression of Flot-2, EGFR, and combined expression of either of these two proteins, which statistical significance was assessed by log-rank test. (A) Lung SCC patients with positive expression of Flot-2 protein showed worse overall survival rates compared to patients with negative Flot-2 (P = 0.043, two sided). (B) Positive expression of EGFR had no significantly correlation with overall survival rates of lung SCC patients (P> 0.05, two sided). (C) Kaplan-Meier curves showed lung SCC patients with positive expression with either of Flot-2 and EGFR proteins had worse overall survival rates than these with all negative staining of two proteins above (P = 0.02, two sided). (D) Lung ADC patients with positive expression of Flot-2 had worse overall survival rates than that with negative one (P = 0.007, two sided, respectively). (E) Lung ADC patients with positive expression of EGFR had worse overall survival rates than that with negative one (P = 0.033, two sided, respectively). (F) Lung ADC patients with positive expression with either of Flot-2 and EGFR proteins showed worse overall survival rates compared with all negative staining of two proteins above (P = 0.005, two sided).</p

Expression of EGFR protein in lung SCC cells, lung ADC cells and the control of non-cancerous lung tissue were detected by IHC using specific antibody as described in the section of materials and methods.

<p>Strong positive staining of EGFR protein was found in cell membranes and cytoplasm of lung SCC and lung ADC cells (Fig 2A and 2B, 20×, IHC, DAB staining). Negative staining of EGFR was showed in non-cancerous lung tissue (Fig 2C, 20×, IHC, DAB staining). Negative control showed no EGFR staining in the lung SCC cells (Fig 2D, 20×, IHC, DAB staining).</p

Expression of Flot-2 protein in lung SCC cells, lung ADC cells and control of non-cancerous lung tissues were detected by IHC using specific antibody as described in the section of materials and methods.

<p>Strong positive staining of Flot-2 protein was found on cell membranes of lung SCC and lung ADC cells (Fig 1A and 1B, 20×, IHC, DAB staining). Negative staining of Flot-2 was showed in non-cancerous lung tissue (Fig 1C, 20×, IHC, DAB staining). Negative control showed no Flot-2 staining in lung ADC cells (Fig 1D, 20×, IHC, DAB staining).</p