Background: Lung cancer is the leading cause of cancer-related deaths in both men and women in the United States, and it has a much lower five-year survival rate than many other cancers. Accurate survival analysis is urgently needed for better disease diagnosis and treatment management. Results: In this work, we propose a survival analysis system that takes advantage of recently emerging deep learning techniques. The proposed system consists of three major components. 1) The first component is an end-to-end cellular feature learning module using a deep neural network with global average pooling. The learned cellular representations encode high-level biologically relevant information without requiring individual cell segmentation, which is aggregated into patient-level feature vectors by using a locality-constrained linear coding (LLC)-based bag of words (BoW) encoding algorithm. 2) The second component is a Cox proportional hazards model with an elastic net penalty for robust feature selection and survival analysis.3) The third commponent is a biomarker interpretation module that can help localize the image regions that contribute to the survival model's decision. Extensive experiments show that the proposed survival model has excellent predictive power for a public (i.e., The Cancer Genome Atlas) lung cancer dataset in terms of two commonly used metrics: log-rank test (p-value) of the Kaplan-Meier estimate and concordance index (c-index). Conclusions: In this work, we have proposed a segmentation-free survival analysis system that takes advantage of the recently emerging deep learning framework and well-studied survival analysis methods such as the Cox proportional hazards model. In addition, we provide an approach to visualize the discovered biomarkers, which can serve as concrete evidence supporting the survival model's decision.
-For homogeneous transcoding, we usually transfer a compressed video into a lower bit-rate and/or video with a smaller size. In this paper we introduce the general architecture of a downing sizing transcoder and propose some novel techniques for its practical realization, including motion vector re-estimation, sub-pixel motion re-estimation, mode redecision, etc. We then generalize the idea of transcoding to video enlargement and propose a simple framework for its reencoding. The paper ends with some useful remarks on the formation of super-resolution videos via the transcoder frame work.
Excessive reactive oxygen species (ROS), a highly reactive substance that contains oxygen, induced by ultraviolet A (UVA) cause oxidative damage to skin. We confirmed that hemin can catalyze the reaction of tyrosine (Tyr) and hydrogen peroxide (H2O2). Catalysis was found to effectively reduce or eliminate oxidative damage to cells induced by H2O2 or UVA. The scavenging effects of hemin for other free-radical ROS were also evaluated through pyrogallol autoxidation, 1,1-diphenyl-2-picrylhydrazyl radical (DPPH·)-scavenging assays, and phenanthroline–Fe2+ assays. The results show that a mixture of hemin and tyrosine exhibits strong scavenging activities for H2O2, superoxide anion (O2−·), DPPH·, and the hydroxyl radical (·OH). Furthermore, the inhibition of oxidative damage to human skin keratinocyte (HaCaT) cells induced by H2O2 or UVA was evaluated. The results show that catalysis can significantly reduce the ratio of cell apoptosis and death and inhibit the release of lactate dehydrogenase (LDH), as well as accumulation of malondialdehyde (MDA). Furthermore, the resistance to apoptosis was found to be enhanced. These results show that the mixture of hemin and tyrosine has a significantly protective effect against oxidative damage to HaCaT cells caused by UVA, suggesting it as a protective agent for combating UVA damage.
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