Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems.One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and fine details. Then it adaptively learns to combine these feature maps at each resolution and predict saliency maps with the combined features. Finally, the predicted results are efficiently fused to generate the final saliency map. In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework. By aggregating multi-level convolutional features in this efficient and flexible manner, the proposed saliency model provides accurate salient object labeling. Comprehensive experiments demonstrate that our method performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.

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Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which encourage the robustness and accuracy of saliency detection. We achieve this via introducing a reformulated dropout (R-dropout) after specific convolutional layers to construct an uncertain ensemble of internal feature units. In addition, we propose an effective hybrid upsampling method to reduce the checkerboard artifacts of deconvolution operators in our decoder network. The proposed methods can also be applied to other deep convolutional networks. Compared with existing saliency detection methods, the proposed UCF model is able to incorporate uncertainties for more accurate object boundary inference. Extensive experiments demonstrate that our proposed saliency model performs favorably against state-ofthe-art approaches. The uncertain feature learning mechanism as well as the upsampling method can significantly improve performance on other pixel-wise vision tasks.

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MicroRNAs (miRNAs) are important gene regulators, which are often deregulated in cancers. In this study, the authors analyzed the microRNAs profiles of 78 matched cancer/noncanerous liver tissues from HCC patients and 10 normal liver tissues and found that 69 miRNAs were differentially expressed between hepatocellular carcinoma (HCC) and corresponding noncancerous liver tissues (N). Then the expressions of 8 differentially expressed miRNAs were validated by real time RT PCR. The set of differentially expressed miRNAs could distinctly classify HCC, N and normal liver tissues (NL). Moreover, some of these differentially expressed miRNAs were related to the clinical factors of HCC patients. Most importantly, Kaplan-Meier estimates and the log-rank test showed that high expression of hsa-miR-125b was correlated with good survival of HCC patients (hazard ratio, 1.787, 95% confidence interval, 1.020-3.133, p 5 0.043). The transfection assay showed that overexpression of miR-125b in HCC cell line could obviously suppress the cell growth and phosporylation of Akt. In conclusion, the authors have demonstrated the diagnostic miRNA profile for HCC, and for the first time, identified the miR-125b with predictive significance for HCC prognosis.

Transforming growth factor-beta (TGF-beta) plays a dual and complex role in human cancer. In this report, we observe a specific set of MicroRNAs (miRNAs) changed in response to TGF-beta in human hepatocellular carcinoma (HCC) cells by miRNA microarray screening. A cluster of miRNA, miR-23a 27a 24, is induced in an early stage by TGF-beta in Huh-7 cells. Knockdown of Smad4, Smad2 or Smad3 expression by RNA interference can attenuate the response of miR-23a 27a 24 to TGF-beta addition, indicating that this induction is dependent on Smad pathway. We also explore that miR-23a 27a 24 can function as an antiapoptotic and proliferation-promoting factor in liver cancer cells. In addition, expression of this miRNA cluster is found to be remarkably upregulated in HCC tissues versus normal liver tissues. These findings suggest a novel, alternative mechanism through which TGF-beta could induce specific miRNA expression to escape from tumor-suppressive response in HCC cells.

Background Obesity has been widely reported to be associated with the disease progression of coronavirus disease 2019 (COVID-19); however, some studies have reported different findings. We conducted a systematic review and meta-analysis to investigate the association between obesity and poor outcomes in patients with COVID-19 pneumonia. Methods A systematic review and meta-analysis of studies from the PubMed, Embase, and Web of Science databases from 1 November 2019 to 24 May 2020 was performed. Study quality was assessed, and data extraction was conducted. The meta-analysis was carried out using fixed-effects and random-effects models to calculate odds ratios (ORs) of several poor outcomes in obese and non-obese COVID-19 patients. Results Twenty-two studies (n = 12,591 patients) were included. Pooled analysis demonstrated that body mass index (BMI) was higher in severe/critical COVID-19 patients than in mild COVID-19 patients (MD 2.48 kg/m2, 95% CI [2.00 to 2.96 kg/m2]). Additionally, obesity in COVID-19 patients was associated with poor outcomes (OR = 1.683, 95% CI [1.408–2.011]), which comprised severe COVID-19, ICU care, invasive mechanical ventilation use, and disease progression (OR = 4.17, 95% CI [2.32–7.48]; OR = 1.57, 95% CI [1.18–2.09]; OR = 2.13, 95% CI [1.10–4.14]; OR = 1.41, 95% CI [1.26–1.58], respectively). Obesity as a risk factor was greater in younger patients (OR 3.30 vs. 1.72). However, obesity did not increase the risk of hospital mortality (OR = 0.89, 95% CI [0.32–2.51]). Conclusions As a result of a potentially critical role of obesity in determining the severity of COVID-19, it is important to collect anthropometric information for COVID-19 patients, especially the younger group. However, obesity may not be associated with hospital mortality, and efforts to understand the impact of obesity on the mortality of COVID-19 patients should be a research priority in the future.

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