The growing ubiquity of social networks has spurred research in link prediction, which aims to predict new connections based on existing ones in the network. The 2011 IJCNN Social Network challenge asked participants to separate real edges from fake in a set of 8960 edges sampled from an anonymized, directed graph depicting a subset of relationships on Flickr. Our method incorporates 94 distinct graph features, used as input for classification with Random Forests. We present a three-pronged approach to the link prediction task, along with several novel variations on established similarity metrics. We discuss the challenges of processing a graph with more than a million nodes. We found that the best classification results were achieved through the combination of a large number of features that model different aspects of the graph structure. Our method achieved an area under the receiver-operator characteristic (ROC) curve of 0.9695, the 2nd best overall score in the competition and the best score which did not de-anonymize the dataset.

As one of the most vulnerable sectors exposed to the COVID-19 pandemic, transport sectors have been severely affected. However, the shocks and impact mechanisms of infectious diseases on transport sectors are not fully understood. This paper employs a multi-sectoral computable general equilibrium model of China, CHINAGEM, with highly disaggregated transport sectors to examine the impacts of the COVID-19 pandemic on China’s transport sectors and reveal the impact mechanisms of the pandemic shocks with the decomposition analysis approach. This study suggests that, first, multiple shocks of the COVID-19 pandemic to transport sectors are specified, including the supply-side shocks that raised the protective cost and reduced the production efficiency of transport sectors, and the demand-side shocks that reduced the demand of households and production sectors for transportation. Second, the outputs of all transport sectors in China have been severely affected by the COVID-19 pandemic, and passenger transport sectors have larger output decreases than freight transport sectors. While the outputs of freight transport sectors are expected to decline by 1.03–2.85%, the outputs of passenger transport sectors would decline by 3.08–11.44%. Third, with the decomposition analysis, the impacts of various exogenous shocks are quite different, while the changes in the output of different transport sectors are dominated by different exogenous shocks. Lastly, while the supply-side shocks of the pandemic would drive output decline in railway, waterway, and aviation transport sectors, the demand-side shocks would drive so in the road, pipeline, and other transport sectors. Moreover, the COVID-19 pandemic has negative impacts on the output of most non-transport sectors and the macro-economy in China. Three policy implications are recommended to mitigate the damages caused by the COVID-19 pandemic to the transport sectors.

Aims: This study aims to explore non-invasive imaging of atherosclerotic plaque through magnetic resonance imaging (MRI) and near-infrared fluorescence (NIRF) by using profilin-1 targeted magnetic iron oxide nanoparticles (PF1-Cy5.5-DMSA-Fe3O4-NPs, denoted as PC-NPs) as multimodality molecular imaging probe in murine model of atherosclerosis. Methods and Results: PC-NPs were constructed by conjugating polyclonal profilin-1 antibody and NHS-Cy5.5 fluorescent dye to the surface of DMSA-Fe3O4-nanoparticles via condensation reaction. Murine atherosclerosis model was induced in apoE-/- mice by high fat and cholesterol diet (HFD) for 16 weeks. The plaque areas in aortic artery were detected with Oil Red O staining. Immunofluorescent staining and Western blot analysis were applied respectively to investigate profilin-1 expression. CCK-8 assay and transwell migration experiment were performed to detect vascular smooth muscle cells (VSMCs) proliferation. In vivo MRI and NIRF imaging of atherosclerotic plaque were carried out before and 36 h after intravenous injection of PC-NPs. Oil Red O staining showed that the plaque area was significantly increased in HFD group (p<0.05). Immunofluorescence staining revealed that profilin-1 protein was highly abundant within plaque in HFD group and co-localized with α-smooth muscle actin. Profilin-1 siRNA intervention could inhibit VSMCs proliferation and migration elicited by ox-LDL (p<0.05). In vivo MRI and NIRF imaging revealed that PC-NPs accumulated in atherosclerotic plaque of carotid artery. There was a good correlation between the signals of MRI and ex vivo fluorescence intensities of NIRF imaging in animals with PC-NPs injection. Conclusion: PC-NPs is a promising dual modality imaging probe, which may improve molecular diagnosis of plaque characteristics and evaluation of pharmaceutical interventions for atherosclerosis.

In order to investigate the effects of autophagy induced by rapamycin in the development of atherosclerosis plaque we established murine atherosclerosis model which was induced in ApoE−/− mice by high fat and cholesterol diet (HFD) for 16 weeks. Rapamycin and 3-Methyladenine (MA) were used as autophagy inducer and inhibitor respectively. The plaque areas in aortic artery were detected with HE and Oil Red O staining. Immunohistochemical staining were applied to investigate content of plaque respectively. In contrast to control and 3-MA groups, rapamycin could inhibit atherosclerosis progression. Rapamycin was able to increase collagen content and a-SMA distribution relatively, as well as decrease necrotic core area. Then we used MOVAS and culture with ox-LDL for 72 h to induce smooth muscle-derived foam cell model in vitro. Rapamycin and 3-MA were cultured together respectively. Flow cytometry assay and SA-β-Gal staining experiments were performed to detect survival and senescence of VSMCs. Western blot analysis were utilized to analyze the levels of protein expression. We found that rapamycin could promote ox-LDL-induced VSMCs autophagy survival and alleviate cellular senescence, in comparison to control and 3-MA groups. Western blot analysis showed that rapamycin could upregulate ULK1, ATG13 and downregulate mTORC1 and p53 protein expression.

There is a UCA1/miR-204/Sirt1 axis in LNCaP and 22RV1 cells. The UCA1/miR-204/Sirt1 axis plays an important role in modulating in vitro docetaxel sensitivity of the prostate cancer cells.