BackgroundTumor suppressive let-7 miRNAs are universally down-regulated in human hepatocellular carcinoma (HCC) versus normal tissues; however, the roles and related molecular mechanisms of let-7 in HCC stem cells are poorly understood.MethodsWe examined the inhibitory effect of let-7 miRNAs on the proliferation of MHCC97-H and HCCLM3 hepatic cancer cells by using MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay, which was further confirmed by apoptosis and cell cycle studies. The sphere-forming assay was used to study the effects of let-7a on stem like cells. Through western blot, immunofluorescence and the luciferase-reporter assay, we explored the activity of epithelial-mesenchymal transition (EMT) signaling factors in HCC cells. qRT-PCR was applied to detect miRNA expression levels in clinical tissues.ResultsLet-7a effectively repressed cell proliferation and viability, and in stem-like cells, also let-7a decreased the efficiency of sphere formation.in stem-like cells. The suppression of EMT signaling factors in HCC cells contributed to let-7’s induced tumor viability repression and Wnt activation repression. Besides, Wnt1 is critical and essential for let-7a functions, and the rescue with recombinant Wnt1 agent abolished the suppressive roles of let-7a on hepatospheres. In clinical HCC and normal tissues, let-7a expression was inversely correlated with Wnt1 expression.ConclusionsLet-7 miRNAs, especially let-7a, will be a promising therapeutic strategy in the treatment of HCC through eliminating HCC stem cells, which could be achieved by their inhibitory effect on the Wnt signaling pathway.

Convolutional Neural Networks (CNNs) have been widely adopted for many imaging applications. For image aesthetics prediction, state-of-the-art algorithms train CNNs on a recently-published large-scale dataset, AVA. However, the distribution of the aesthetic scores on this dataset is extremely unbalanced, which limits the prediction capability of existing methods. We overcome such limitation by using weighted CNNs. We train a regression model that improves the prediction accuracy of the aesthetic scores over state-of-the-art algorithms. In addition, we propose a novel histogram prediction model that not only predicts the aesthetic score, but also estimates the difficulty of performing aesthetics assessment for an input image. We further show an image enhancement application where we obtain an aesthetically pleasing crop of an input image using our regression model.

We propose a weakly supervised semantic segmentation algorithm that uses image tags for supervision. We apply the tags in queries to collect three sets of web images, which encode the clean foregrounds, the common backgrounds, and realistic scenes of the classes. We introduce a novel three-stage training pipeline to progressively learn semantic segmentation models. We first train and refine a class-specific shallow neural network to obtain segmentation masks for each class. The shallow neural networks of all classes are then assembled into one deep convolutional neural network for end-to-end training and testing. Experiments show that our method notably outperforms previous state-of-the-art weakly supervised semantic segmentation approaches on the PASCAL VOC 2012 segmentation benchmark. We further apply the class-specific shallow neural networks to object segmentation and obtain excellent results.

KLF5 is frequently deleted or downregulated in prostate cancer. However, it is not known whether downregulation of KLF5 is associated with the response of prostate cancer cells to chemotherapy and/or prognosis of patients.Methods: We monitored cell growth by MTT and colony formation assays, and cell autophagy through tandem fluorescence microscopy and transmission electron microscopy. Gene expression was analyzed by RT-qPCR and Western blotting. We determined the binding of KLF5 together with HDAC3 on the beclin-1 (BECN1) promoter by the ChIP assay, oligonucleotides pulldown, and co-immunoprecipitation. The effect of docetaxel on cell growth in vivo was examined in a CWR22RV1 xenograft tumor mouse model.Results: In the present study, we found that KLF5 down-regulation was associated with progression of prostate cancer and poor prognosis of patients. KLF5 knockdown reduced the sensitivity of prostate cancer cells to docetaxel in vitro and in vivo, and docetaxel treatment decreased the expression of KLF5. Moreover, we confirmed that docetaxel treatment inhibited cell death by inducing autophagy in prostate cancer cells. Thus, we hypothesized that KLF5 could be a regulator of cell autophagy. Interestingly, KLF5 could inhibit prostate cancer cell autophagy by suppressing the transcription of BECN1 cooperatively with HDAC3. Another significant finding was that docetaxel treatment repressed KLF5 expression through AMPK/mTOR/p70S6K signaling pathway resulting in increased BECN1, induction of cell autophagy, and promotion of cell survival in castration-resistant prostate cancer cells.Conclusions: Our results indicated that downregulation of KLF5 promoted cell autophagy in prostate cancer. Furthermore, reduced KLF5 also facilitated cell autophagy induced by docetaxel resulting in desensitization to the drug and cell survival. Decreased levels of KLF5 led to increased BECN1 via AMPK/mTOR/p70S6K signaling. Thus, repression of BECN1 and cell autophagy was critical for KLF5 to increase the sensitivity of prostate cancer cells to docetaxel.

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