Elevated expression of Dickkopf-1 increases the sensitivity of human glioma cell line SHG44 to BCNU

<p>Abstract</p> <p>Background</p> <p>Studies have shown that Dickkopf-1 (DKK-1) is involved in tumorigenesis. Recently, we found that 9 out of 12 human glioma cell lines had high level of DKK-1 protein while the other 3 had very low or non-detectable level of DKK-1. The aim of this study is to further examine the function of DKK-1 in glioma cells.</p> <p>Materials and methods</p> <p>The glioma cell line SHG₄₄was obtained from a patient with grade II-III astrocytoma. SHG₄₄cells were transfected with a human DKK-1 gene. Transfection of the empty vector pcDNA3.1 was used as negative control. Sensitivity to BCNU was measured by Annexin-V staining. Expression of bax, bcl-2 and caspase-3 of three groups was determined by immunohistochemistry.</p> <p>Results</p> <p>The tranfection was confirmed by PCR, RT-PCR and Western blot. More apoptotic cell death was observed in the DKK-1 transfected cells, comparing to the non-transfected cells, or cells with empty vector. The expression of bax and caspase-3 of the SHG₄₄-DDK-1 increased, whereas the expression of bcl-2 decreased</p> <p>Conclusion</p> <p>Our results indicated that DKK-1 has a pro-apoptotic function of in glioma.</p

Lightweight Image Super-Resolution with Information Multi-distillation Network

In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive results. Thanks to the powerful representation capabilities of the deep networks, numerous previous ways can learn the complex non-linear mapping between low-resolution (LR) image patches and their high-resolution (HR) versions. However, excessive convolutions will limit the application of super-resolution technology in low computing power devices. Besides, super-resolution of any arbitrary scale factor is a critical issue in practical applications, which has not been well solved in the previous approaches. To address these issues, we propose a lightweight information multi-distillation network (IMDN) by constructing the cascaded information multi-distillation blocks (IMDB), which contains distillation and selective fusion parts. Specifically, the distillation module extracts hierarchical features step-by-step, and fusion module aggregates them according to the importance of candidate features, which is evaluated by the proposed contrast-aware channel attention mechanism. To process real images with any sizes, we develop an adaptive cropping strategy (ACS) to super-resolve block-wise image patches using the same well-trained model. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, memory footprint, and inference time. Code is available at \url{https://github.com/Zheng222/IMDN}.Comment: To be appear in ACM Multimedia 2019, https://github.com/Zheng222/IMD

Silencing BMI1 eliminates tumor formation of pediatric glioma CD133+ cells not by affecting known targets but by down-regulating a novel set of core genes

Abstract Clinical outcome of children with malignant glioma remains dismal. Here, we examined the role of over-expressed BMI1, a regulator of stem cell self-renewal, in sustaining tumor formation in pediatric glioma stem cells. Our investigation revealed BMI1 over-expression in 29 of 54 (53.7%) pediatric gliomas, 8 of 8 (100%) patient derived orthotopic xenograft (PDOX) mouse models, and in both CD133+ and CD133− glioma cells. We demonstrated that lentiviral-shRNA mediated silencing of suppressed cell proliferation in vitro in cells derived from 3 independent PDOX models and eliminated tumor-forming capacity of CD133+ and CD133− cells derived from 2 PDOX models in mouse brains. Gene expression profiling showed that most of the molecular targets of BMI1 ablation in CD133+ cells were different from that in CD133- cells. Importantly, we found that silencing BMI1 in CD133+ cells derived from 3 PDOX models did not affect most of the known genes previously associated with the activated BMI1, but modulated a novel set of core genes, including RPS6KA2, ALDH3A2, FMFB, DTL, API5, EIF4G2, KIF5c, LOC650152, C20ORF121, LOC203547, LOC653308, and LOC642489, to mediate the elimination of tumor formation. In summary, we identified the over-expressed BMI1 as a promising therapeutic target for glioma stem cells, and suggest that the signaling pathways associated with activated BMI1 in promoting tumor growth may be different from those induced by silencing BMI1 in blocking tumor formation. These findings highlighted the importance of careful re-analysis of the affected genes following the inhibition of abnormally activated oncogenic pathways to identify determinants that can potentially predict therapeutic efficacy.http://deepblue.lib.umich.edu/bitstream/2027.42/110124/1/40478_2014_Article_160.pd

Topography of transcriptionally active chromatin in glioblastoma

Molecular profiling of the most aggressive brain tumor glioblastoma (GBM) on the basis of gene expression, DNA methylation, and genomic variations advances both cancer research and clinical diagnosis. The enhancer architectures and regulatory circuitries governing tumor-intrinsic transcriptional diversity and subtype identity are still elusive. Here, by mapping H3K27ac deposition, we analyze the active regulatory landscapes across 95 GBM biopsies, 12 normal brain tissues, and 38 cell line counterparts. Analyses of differentially regulated enhancers and super-enhancers uncovered previously unrecognized layers of intertumor heterogeneity. Integrative analysis of variant enhancer loci and transcriptome identified topographies of transcriptional enhancers and core regulatory circuitries in four molecular subtypes of primary tumors: AC1-mesenchymal, AC1-classical, AC2-proneural, and AC3-proneural. Moreover, this study reveals core oncogenic dependency on super-enhancer–driven transcriptional factors, long noncoding RNAs, and druggable targets in GBM. Through profiling of transcriptional enhancers, we provide clinically relevant insights into molecular classification, pathogenesis, and therapeutic intervention of GBM

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance

Distribution Matching for Rationalization

The task of rationalization aims to extract pieces of input text as rationales to justify neural network predictions on text classification tasks. By definition, rationales represent key text pieces used for prediction and thus should have similar classification feature distribution compared to the original input text. However, previous methods mainly focused on maximizing the mutual information between rationales and labels while neglecting the relationship between rationales and input text. To address this issue, we propose a novel rationalization method that matches the distributions of rationales and input text in both the feature space and output space. Empirically, the proposed distribution matching approach consistently outperforms previous methods by a large margin. Our data and code are available