Long non-coding RNA (lncRNA), highly up-regulated in liver cancer (HULC) plays an important role in tumorigenesis. Depletion of HULC resulted in a significant deregulation of several genes involved in liver cancer. Although up-regulation of HULC expression in hepatocellular carcinoma has been reported, the molecular mechanisms remain unknown. In this study, we used in vivo and in vitro approaches to characterize cancer-dependent alterations in the chromatin organization and find a CREB binding site (encompassing from −67 to −53 nt) in the core promoter. Besides, we also provided evidence that PKA pathway may involved in up-regulation of HULC. Furthermore, we demonstrated HULC may act as an endogenous ‘sponge’, which down-regulates a series of microRNAs (miRNAs) activities, including miR-372. Inhibition of miR-372 leads to reducing translational repression of its target gene, PRKACB, which in turn induces phosphorylation of CREB. Over-expression of miR-372 decreases the association of CREB with the proximal promoter, followed by the dissociation of P300, resulting in a change of the histone ‘code’, such as in deacetylation and methylation. The study elucidates that fine tuning of HULC expression is part of an auto-regulatory loop in which it’s inhibitory to expression and activity of miR-372 allows lncRNA up-regulated expression in liver cancer.

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Existing semantic segmentation models heavily rely on dense pixelwise annotations. To reduce the annotation pressure, we focus on a challenging task named zero-shot semantic segmentation, which aims to segment unseen objects with zero annotations. This task can be accomplished by transferring knowledge across categories via semantic word embeddings. In this paper, we propose a novel context-aware feature generation method for zero-shot segmentation named CaGNet. In particular, with the observation that a pixel-wise feature highly depends on its contextual information, we insert a contextual module in a segmentation network to capture the pixel-wise contextual information, which guides the process of generating more diverse and context-aware features from semantic word embeddings. Our method achieves state-of-the-art results on three benchmark datasets for zero-shot segmentation. CCS CONCEPTS • Computing methodologies → Image segmentation.

Salient regions detection from images is an important and fundamental research problem in neuroscience and psychology and it serves as an indispensible step for numerous machine vision tasks. In this paper, we propose a novel conceptually simple salient region detection method, namely SDSP, by combining three simple priors. At first, the behavior that the human visual system detects salient objects in a visual scene can be well modeled by band-pass filtering. Secondly, people are more likely to pay their attention on the center of an image. Thirdly, warm colors are more attractive to people than cold colors are. Extensive experiments conducted on the benchmark dataset indicate that SDSP could outperform the other state-of-the-art algorithms by yielding higher saliency prediction accuracy. Moreover, SDSP has a quite low computational complexity, rendering it an outstanding candidate for time critical applications. The Matlab source code of SDSP and the evaluation results have been made online available at

BackgroundProteins functioning in the same biological pathway tend to be transcriptionally co-regulated or form protein-protein interactions (PPI). Multiple spatially and temporally regulated events are coordinated during mitosis to achieve faithful chromosome segregation. The molecular players participating in mitosis regulation are still being unravelled experimentally or using in silico methods.ResultsAn extensive literature review has led to a compilation of 196 human centromere/kinetochore proteins, all with experimental evidence supporting the subcellular localization. Sixty-four were designated as “core” centromere/kinetochore components based on peak expression and/or well-characterized functions during mitosis. By interrogating and integrating online resources, we have mined for genes/proteins that display transcriptional co-expression or PPI with the core centromere/kinetochore components. Top-ranked hubs in either co-expression or PPI network are not only enriched with known mitosis regulators, but also contain candidates whose mitotic functions are not yet established. Experimental validation found that KIAA1377 is a novel centrosomal protein that also associates with microtubules and midbody; while TRIP13 is a novel kinetochore protein and directly interacts with mitotic checkpoint silencing protein p31comet.ConclusionsTranscriptional co-expression and PPI network analyses with known human centromere/kinetochore proteins as a query group help identify novel potential mitosis regulators.