Noncoding RNAs in tumorigenesis and tumor therapy

Tumorigenesis is a complicated process in which numerous modulators are involved in different ways. Previous studies have focused primarily on tumor-associated protein-coding genes such as oncogenes and tumor suppressor genes, as well as their associated oncogenic pathways. However, noncoding RNAs (ncRNAs), rising stars in diverse physiological and pathological processes, have recently emerged as additional modulators in tumorigenesis. In this review, we focus on two typical kinds of ncRNAs: long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs). We describe the molecular patterns of ncRNAs and focus on the roles of ncRNAs in cancer stem cells (CSCs), tumor cells, and tumor environmental cells. CSCs are a small subset of tumor cells and are generally considered to be cells that initiate tumorigenesis, and dozens of ncRNAs have been defined as critical modulators in CSC maintenance and oncogenesis. Moreover, ncRNAs are widely involved in oncogenetic processes, including sustaining proliferation, resisting cell death, genome instability, metabolic disorders, immune escape and metastasis. We also discuss the potential applications of ncRNAs in tumor diagnosis and therapy. The progress in ncRNA research greatly improves our understanding of ncRNAs in oncogenesis and provides new potential targets for future tumor therapy

Electrical anisotropic response of water conducted fractured zone in the mining goaf

Based on the Maxwell equation, the occurrences of fractured zones are studied through the galvanic method. The electrical and magnetic fields are first derived in the spatial domain. To simplify the calculations, the computational formulas of the electrical fields in the spatial domain are transformed into the wavenumber domain by Fourier transform. The basic solution of the electromagnetic field can thus be easily solved in the wavenumber domain. According to the boundary conditions, a recursive relationship between the different layers is established. The electromagnetic fields are obtained through the recursive relationships with the bottom-last layer. Finally, the apparent resistivity is calculated using the surface electric field. A typical goaf model is used for the numerical simulation. Based on the modeling results the effectiveness of this method is determined. The modeling results indicate that the galvanic method is very effective for detecting the electrical anisotropic characters.This work was jointly sponsored by Jiangsu Province National Natural Science Foundation (NO. BK20130180), Research Funds for the Central Universities- China University of Mining and Technology (NO. 2014QNA88) and China Postdoctoral Science Foundation (NO. 2015M570491).http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639hb2016Electrical, Electronic and Computer Engineerin

Causal relation of queries from temporal logs

In this paper, we study a new problem of mining causal relation of queries in search engine query logs. Causal relation between two queries means event on one query is the causation of some event on the other. We first detect events in query logs by efficient statistical frequency threshold. Then the causal relation of queries is mined by the geometric features of the events. Finally the Granger Causality Test (GCT) is utilized to further re-rank the causal relation of queries according to their GCT coefficients. In addition, we develop a 2-dimensional visualization tool to display the detected relationship of events in a more intuitive way. The experimental results on the MSN search engine query logs demonstrate that our approach can accurately detect the events in temporal query logs and the causal relation of queries is detected effectively.EI

Image noise reduction based on applying adaptive thresholding onto PDEs methods

In this study the authors present a novel image denoising method based on applying adaptive thresholding on partial differential (PDEs) methods. In the proposed method the authors utilise the adaptive thresholding to blend the total variation filter with anisotropic diffusion filter. The adaptive thresholding has a high capacity to adapt and change according to the amount of noise. More specifically, applying a hard thresholding on the higher noise areas, whereas, applying soft thresholding on the lower noise areas. Therefore, the authors can successfully remove the noise effectively and maintain the edges of the image simultaneously. Based on the adaptation and stability of the adaptive thresholding we can achieve; optimal noise reduction and sharp edges as well. Experimental results demonstrate that the new algorithm consistently outperforms other reference methods in terms of noise removal and edges preservation, in addition to 4.7 dB gain higher than those in the other reference algorithms

Improving text classification using local latent semantic indexing

Latent Semantic Indexing (LSI) has been shown to be extremely useful in information retrieval, but it is not an optimal representation for text classification. It always drops the text classification performance when being applied to the whole training set (global LSI) because this completely unsupervised method ignores class discrimination while only concentrating on representation. Some local LSI methods have been proposed to improve the classification by utilizing class discrimination information. However, their performance improvements over original term vectors are still very limited. In this paper, we propose a new local LSI method called “Local Relevancy Weighted LSI ” to improve text classification by performing a separate Single Value Decomposition (SVD) on the transformed local region of each class. Experimental results show that our method is much better than global LSI and traditional local LSI methods on classification within a much smaller LSI dimension. 1

Spatiotemporal Video Denoising Based on Adaptive Thresholding and Clustering

In this paper we propose a novel video denoising method based on adaptive thresholding and K-means clustering. In the proposed method the adaptive thresholding is applied rather than the conventional hard-thresholding of the VBM3D method. The adaptive thresholding has a high ability to adapt and change according to the amount of noise. More specifically, hard-thresholding is applied on the higher noise areas while soft-thresholding is applied on the lower noise areas. Consequently, we can successfully remove the noise effectively and at the same time preserve the edges of the image, because the clustering approach saves more computation time and is more capable of finding relevant patches than the block-matching approach. So, the K-means clustering method in the final estimate in this paper is adopted instead of the block-matching method in the VBM3D method in order to restrict the search of the candidate patches within the region of the reference patch and therefore improve the grouping. Experimental results emphasize the superiority of the new method over the reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR), and Image Enhancement Factor (IEF). Execution time of the proposed algorithm consumes less time in denoising than that in the VBM3D algorithm

Affinity Rank: A New Scheme for Efficient Web Search

Maximizing only the relevance between queries and documents will not satisfy users if they want the top search results to present a wide coverage of topics by a few representative documents. In this paper, we propose two new metrics to evaluate the performance of information retrieval: diversity, which measures the topic coverage of a group of documents, and information richness, which measures the amount of information contained in a document. Then we present a novel ranking scheme, Affinity Rank, which utilizes these two metrics to improve search results. We demonstrate how Affinity Rank works by a toy data set, and verify our method by experiments on real-world data sets

Valproic acid regulates HR and cell cycle through MUS81-pRPA2 pathway in response to hydroxyurea

Breast cancer is the primary problem threatening women’s health. The combined application of valproic acid (VPA) and hydroxyurea (HU) has a synergistic effect on killing breast cancer cells, but the molecular mechanism remains elusive. Replication protein A2 phosphorylation (pRPA2), is essential for homologous recombination (HR) repair and cell cycle. Here we showed that in response to HU, the VPA significantly decreased the tumor cells survival, and promoted S-phase slippage, which was associated with the decrease of pCHK1 and WEE1/pCDK1-mediated checkpoint kinases phosphorylation pathway and inhibited pRPA2/Rad51-mediated HR repair pathway; the mutation of pRPA2 significantly diminished the above effect, indicating that VPA-caused HU sensitization was pRPA2 dependent. It was further found that VPA and HU combination treatment also resulted in the decrease of endonuclease MUS81. After MUS81 elimination, not only the level of pRPA2 was abolished in response to HU treatment, but also VPA-caused HU sensitization was significantly down-regulated through pRPA2-mediated checkpoint kinases phosphorylation and HR repair pathways. In addition, the VPA altered the tumor microenvironment and reduced tumor burden by recruiting macrophages to tumor sites; the Kaplan-Meier analysis showed that patients with high pRPA2 expression had significantly worse survival. Overall, our findings demonstrated that VPA influences HR repair and cell cycle through down-regulating MUS81-pRPA2 pathway in response to HU treatment

A novel scalable algorithm for supervised subspace learning

Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as Principal Component Analysis (PCA) do not make use of the class information, and Linear Discriminant Analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as Supervised Kampong Measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, Information Gain (IG). 1