SUMMARY: Colorectal cancer is a heterogeneous disease with diverse prognoses between left-sided and right-sided patients; therefore, it is necessary to precisely evaluate the survival probability of side-specific colorectal cancer patients. Here, we collected multi-omics data from The Cancer Genome Atlas program, including gene expression, DNA methylation and microRNA expression. Specificity measure and robust likelihood-based survival analysis were used to identify 6 left-sided and 28 right-sided prognostic biomarkers. Compared to the performance of clinical prognostic models, the addition of these biomarkers could significantly improve the discriminatory ability and calibration in predicting side-specific 5-year survival for colorectal cancer. Additional dataset derived from Gene Expression Omnibus was used to validate the prognostic value of side-specific genes. Finally, we constructed colorectal cancer side-specific molecular database (CoSMeD), a user-friendly interface for estimating side-specific colorectal cancer 5-year survival probability, which can lay the basis for personalized management of left-sided and right-sided colorectal cancer patients. AVAILABILITY AND IMPLEMENTATION: CoSMeD is freely available at https://mulongdu.shinyapps.io/cosmed. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This work defines an analytical model that computes an upper bound of the transmitted power of a base station in the downlink. The distribution of terminals is modeled as a Poisson point Process. Based on point process theory and concentration inequalities, an upper bound of the distribution of power consumption of base station is derived. Closed forms are also obtained for the mth moments of the transmitted power. They show that overall power varies linearly with users' spatial intensity (or load). Comparisons with simulations prove the accuracy and the robustness of the proposed model.

Object category recognition is a challenging task due to the low level and non-discrimination in visual representation. Most previous methods concentrate to find better high level visual features. Recently, optimally integrating various features to solve the problem attracted more interests. In this paper, we provide a novel method for object category recognition by improving the popular bag-of-words (BoW) methods from the following two aspects. First, we propose to extract a series of high level visual features which exploit both the local spatial co occurrence between low level visual words and the global spatial layout of the object parts. To obtain the global spatial features, a fast method is proposed to generate the semantic meaningful object parts by exploiting the geometric position distribution of the local salient regions. The image part patches are further quantized as semantic coherent high level visual words by using correlational spectral clustering. Based on it, simplified 2D string representation is introduced to model the global spatial patterns of the objects. Second, a multi-kernel learning framework is proposed to adaptively integrate extracted features in an optimal way. For each object class, an optimal feature weight coefficient is learned automatically and separately to combine both the low level and high level visual features by considering their contribution for the different object class. The tests on Caltech-101 and Pascal- VOC 06 dataset demonstrated that our method outperforms the baseline method BoW and state-of-the-art Multi-CM model .