No abstract

Recently, an increasing number of studies have focused on the key function of long noncoding RNAs (lncRNAs) in biological activity. Abnormal lncRNA expression was found to relate to the development and pathogenesis of multiple cancers. LncRNA LINC00152 served as an oncogene in multiple cancers; however, its role in ovarian cancer remains unknown. In our research study, LINC00152 was upregulated in ovarian cancer tissues and cell lines. An increasing LINC00152 level was positively correlated with the histological grade, clinical stage, and poor prognosis of ovarian cancer patients. In addition, knockdown of LINC00152 reduced cell growth, induced cell apoptosis, and suppressed tumor growth. Moreover, we revealed that LINC00152 and Myeloid cell leukemia‐1 (MCL‐1) were targeted by miR‐125b and had the same miR‐125b combining site. The miR‐125b level was negatively correlated with the expression of LINC00152, while MCL‐1 was positively related to the LINC00152 level. MiR‐125b could affect LINC00152 levels as evaluated by qRT‐PCR. Finally, we affirmed that LINC00152 mediated cell proliferation by affecting MCL‐1 expression and MCL‐1‐mediated mitochondrial apoptosis pathways and by working as a competitive endogenous RNA (ceRNA) of miR‐125b. In summary, based on ceRNA theory, the combined research on miR‐125b and MCL‐1, and taking LINC00152 as a new study point, we provide new insight into the molecular mechanism of reversing cell proliferation in ovarian cancer.

Since the discovery of the first microRNA (miRNA), the exploration of miRNA biology has come to a new era in recent decades. Monumental studies have proven that miRNAs can be dysregulated in different types of cancers and the roles of miRNAs turn out to function to either tumor promoters or tumor suppressors. The interplay between miRNAs and the development of cancers has grabbed attention of miRNAs as novel tools and targets for therapeutic attempts. Moreover, the development of miRNA delivery system accelerates miRNA preclinical implications. In this review, we depict recent advances of miRNAs in cancer and discuss the potential diagnostic or therapeutic approaches of miRNAs.

Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmentations are provided, and fully convolutional networks (FCN) are trained in an end-to-end scheme. Although these methods have demonstrated impressive results, their performance highly depends on the quantity and quality of training data. In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations. Our key idea is to exploit the anatomical similarity among human to transfer the parsing results of a person to another person with similar pose. Using these estimated results as additional training data, our semi-supervised model outperforms its strong-supervised counterpart by 6 mIOU on the PASCAL-Person-Part dataset [6], and we achieve stateof-the-art human parsing results. Our approach is general and can be readily extended to other object/animal parsing task assuming that their anatomical similarity can be annotated by keypoints. The proposed model and accompanying source code will be made publicly available. * This work was done when Xiaolin Fang was an intern at MVIG lab of Shanghai Jiao Tong University.† The corresponding author is Cewu Lu, email: lucewu@sjtu.edu.cn. Cewu Lu is also a member of SJTU-SenseTime lab and AI research institute of SJTU.

This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model. The divergence triangle is a compact and symmetric (anti-symmetric) objective function that seamlessly integrates variational learning, adversarial learning, wake-sleep algorithm, and contrastive divergence in a unified probabilistic formulation. This unification makes the processes of sampling, inference, energy evaluation readily available without the need for costly Markov chain Monte Carlo methods. Our experiments demonstrate that the divergence triangle is capable of learning (1) an energy-based model with well-formed energy landscape, (2) direct sampling in the form of a generator network, and (3) feed-forward inference that faithfully reconstructs observed as well as synthesized data. The divergence triangle is a robust training method that can learn from incomplete data.