Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease–gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.     

Discovering DNA methylation patterns for long non-coding RNAs associated with cancer subtypes

Despite growing evidence demonstrates that the long non-coding ribonucleic acids (lncRNAs) are critical modulators for cancers, the knowledge about the DNA methylation patterns of lncRNAs is quite limited. We develop a systematic analysis pipeline to discover DNA methylation patterns for lncRNAs across multiple cancer subtypes from probe, gene and network levels. By using The Cancer Genome Atlas (TCGA) breast cancer methylation data, the pipeline discovers various DNA methylation patterns for lncRNAs across four major subtypes such as luminal A, luminal B, her2-enriched as well as basal-like. On the probe and gene level, we find that both differentially methylated probes and lncRNAs are subtype specific, while the lncRNAs are not as specific as probes. On the network level, the pipeline constructs differential co-methylation lncRNA network for each subtype. Then, it identifies both subtype specific and common lncRNA modules by simultaneously analyzing multiple networks. We show that the lncRNAs in subtype specific and common modules differ greatly in terms of topological structure, sequence conservation as well as expression. Furthermore, the subtype specific lncRNA modules serve as biomarkers to improve significantly the accuracy of breast cancer subtypes prediction. Finally, the common lncRNA modules associate with survival time of patients, which is critical for cancer therapy.     

Protection of flows under targeted attacks

We present a new robust optimization model for the problem of maximizing the amount of flow surviving the attack of an interdictor. Given some path flow, our model allows the interdictor to specify the amount of flow removed from each path individually. In contrast to previous models, for which no efficient algorithms are known, the most important basic variants of our model can be solved in poly-time. We also consider extensions where there is a budget to set the interdiction costs.     

Practical extensions to a minimum cost flow model for level of repair analysis

The level of repair analysis (LORA) gives answers to three questions that are posed when deciding on how to maintain capital goods: (1) which components to repair upon failure and which to discard, (2) at which locations in the repair network to perform each type of repairs, and (3) at which locations in the network to deploy resources, such as test equipment. The goal is to achieve the lowest possible life cycle costs. Various models exist for the LORA problem. However, they tend to be restrictive in that specific business situations cannot be incorporated, such as having repair equipment with finite capacity or the occurrence of unsuccessful repairs or no-fault-founds. We discuss and model such practically relevant extensions to an existing minimum cost flow formulation for the LORA problem. In an extensive numerical experiment, we show that incorporating the model refinements leads to a substantial change in the costs in general. The repair strategy changes substantially only when incorporating finite resource capacities or a probability of unsuccessful repair that is decreasing with an increasing echelon level.     

The Transit Route Arc-Node Service Maximization problem

This article presents a new method for determining optimal transit routes. The Transit Route Arc-Node Service Maximization model is a mathematical model that maximizes the service value of a route, rather than minimizing cost. Cost (distance) is considered as a budget constraint on the extent of the route. The mathematical formulation modifies and exploits the structure of linear programming problems designed for the traveling salesman problem. An innovative divide-and-conquer solution procedure is presented that not only makes the transit routing problem tractable, but also provides a range of high-quality alternate routes for consideration, some of which have substantially varying geometries. Variant formulations are provided for several common transit route types. The model is tested through its application to an existing street network in Richardson, TX. Optimal numeric results are obtained for several problem instances, and these results demonstrate that increased route cost is not correlated with increased service provision.     

一种基于网格模型的无限传感器网络K区域覆盖算法

目前无限传感器网络K覆盖问题的解决机制大都有颇为苛刻的假设条件,如要求节点具有很强的能量、节点的感知区域能被精确定义等.提出了一个基于布尔感知理论的分布式网格模型及相应的K区域覆盖算法,能够很好地处理感知区域形状不规则及大小发生变化的K覆盖问题,算法时间复杂度小,适用范围广泛.仿真实验证明了算法的有效性.     

A dynamic analysis of corporate investments and emission tax policy in an oligopoly market with network externality

We investigate the product innovation, green R&D investments and the emission tax policy in an oligopoly market with network externality. It is shown that an appropriate tax policy should be deployed to effectively control pollution and motivate innovation. At the early stage of the market, the emission tax should gradually reduce to motivate firms to achieve optimal investments. Later at the mature stage, the emission tax policy should carefully consider both the market competition and green technology levels.     

基于变换域PCNN的近红外与彩色可见光融合

针对近红外与彩色可见光图像融合后对比度低、细节丢失和颜色失真等问题,提出一种基于多尺度变换和自适应脉冲耦合神经网络(PCNN-pulse coupled neural network,PCNN)的红外与彩色可见光图像融合的新算法.首先将彩色可见光图像转换到HSI(hue saturation intensity)空间,...     

相控阵天线内部通信的CAN控制器IP核设计

为实现相控阵天线波控系统小型化、芯片化的目的,设计了一种基于Avalon总线的CAN控制器IP核。参照CAN2.0B协议规范,给出了CAN控制器IP核的功能结构,利用Verilog硬件描述语言完成了控制器接口逻辑、位流处理器、位时序逻辑等模块设计,并进行了设备驱动及应用软件开发。将设计的CAN控制器IP核、Nios II微处理器、多个单元相位控制器IP核等集成到FPGA中构成子阵级波束控制的SoPC系统。最后对CAN总线的通信性能进行了实验测试。结果表明：设计的CAN控制器IP核能够在实际CAN网络中稳定可靠地收发数据。基于这种方式构建的系统,扩展方便、可移植性高、具有较强的适应性,也可用于高密度、紧凑型工业控制领域。