lncRNA-miRNA-mRNA interaction network for colorectal cancer; An in silico analysis

BackgroundColorectal cancer (CRC) is one of the most frequent and diagnosed diseases. Accumulating evidences showed that mRNAs and noncoding RNAs play important regulatory roles in tumorigenesis. Identification and determining the relationship between them can help diagnosis and treatment of cancer.MethodsHere we analyzed three microarray datasets; GSE110715, GSE32323 and GSE21510, to identify differentially expressed lncRNAs and mRNAs in CRC. The adjusted p-value ≤0.05 was considered statistically significant. Gene set enrichment analysis was carried out using DAVID tool. The miRCancer database was searched to obtain differentially expressed miRNAs in colorectal cancer, and the miRDB database was used to attain the targets of the obtained miRNAs. To predict the lncRNA-miRNA interactions we used DIANA-LncBase v2 and RegRNA 2.0. Finally the lncRNA-miRNA-mRNA-signaling pathway network was constructed using Cytoscape v3.1.ResultsBy analyzing the three datasets, a total of 21 mRNAs (15 up- and 6 down-regulated) and 24 lncRNAs (18 up- and 6 down-regulated) were identified as common differentially expressed genes between CRC tumor and marginal tissues. Nevertheless, the constructed lncRNA-miRNA-mRNA-signaling pathway network revealed a convergence on 6 lncRNAs (3 up- and 3 downregulated), 7 mRNAs (2 up- and 5 downregulated) and 6 miRNAs (3 up- and 3 downregulated). We found that dysregulation of lncRNAs such as PCBP1-AS1, UCA1 and SNHG16 could sequester several miRNAs such as hsa-miR-582-5p and hsa-miR-198 and promote the proliferation, invasion and drug resistance of colorectal cancer cells.ConclusionsWe introduced a set of lncRNAs, mRNAs and miRNAs differentially expressed in CRC which might be considered for further experimental research as potential biomarkers of CRC development.     

Identifying critical states of hepatocellular carcinoma based on landscape dynamic network biomarkers

Hepatocellular carcinoma (HCC) is the major histological form of primary liver cancer. It has usually reached the disease state once the patient is diagnosed since there are no specific symptoms in the early stages of HCC. This fact increases the difficulty of curing HCC. Recently, quantities of evidence have shown that many mathematical methods (such as dynamic network biomarkers, DNB) can be used to detect critical states or tipping points of complex diseases. However, it is difficult to apply the DNB theory to the clinic since multiple samples are generally unavailable for individual patient. This paper constructs a novel method based on landscape dynamic network biomarkers (L-DNB), which aims to detect early warning signals from cirrhosis state to very advanced HCC state in individual patient. The selected dataset contains multiple samples for each HCC state. A score that indicates the disease characteristics is calculated for each sample by RNA-seq data, and several scores constitute a distribution in the same state. Quantifying the statistical characteristics of these distributions and determining that low-grade dysplastic and high-grade dysplastic are the critical states of HCC. These results can provide scientific advice for early warning indicators and optimal treatment time for HCC.     

Combining convolutional neural networks and in‐line near‐infrared spectroscopy for real‐time monitoring of the chromatographic elution process in commercial production of notoginseng total saponins

The chromatographic elution process is a key step in the production of notoginseng total saponins. Due to quality variability of loading samples and resin capacity decreasing over cycle time, saponins, especially the five main saponins of notoginseng total saponins, need to be monitored in real time during the elution process. In this study, convolutional neural networks, one of the most popular deep learning methods, were used to develop quantitative calibration models based on in‐line near‐infrared spectroscopy for notoginsenoside R₁, ginsenosides Rg₁, Re, Rb₁ and Rd, and their sum concentration, with root mean square error of prediction values of 0.87, 2.76, 0.60, 1.57, 0.28, and 4.99 mg/mL, respectively. Partial least squares calibration models were also developed for model performance comparison. Results show predicted concentration profiles outputted by both the convolutional neural network models and partial least squares models show agreements with the real trends defined by reference measurements, and can be used for elution process monitoring and endpoint determination. To the best of our knowledge, this is the first reported case study of combining convolutional neural networks and in‐line near‐infrared spectroscopy for monitoring of the chromatographic elution process in commercial production of botanical drug products.     

用逻辑方法验证移动Ad Hoc网络协议

针对移动Ad Hoc网络节点移动和无线广播通信特征,引入移动算子和广播算子,扩展形式逻辑LS2,提出了建模和分析移动Ad Hoc网络安全系统的逻辑ELS2.ELS2把网络模型化为不同位置上执行程序的线程复合,把攻击者模型化为与协议参与方并发运行的线程.ELS2中提出网络迹概念,描述网络节点内部计算和外部交互,以及节点移...     

基于PSO-RBF神经网络模型的原水水质评价方法及应用

针对自来水生产过程的原水水质评价问题,提出了一种基于PSO-RBF神经网络模型的原水水质评价方法.首先,根据水厂生产经验和历史数据分析,制定面向自来水生产过程的原水水质评价标准.然后,采用粒子群优化(PSO)算法训练的RBF神经网络模型,对苏州市相城水厂的进厂原水水质实施在线评价.最后,将进厂原水水质在线评价结果作为前...     

全光纤安防系统模式识别混合编程的实现

介绍了宽域全光纤监控预警系统及光纤扰动信号模式识别算法.利用小波包分解对信号进行分解与重构,并提取各频段内信号分量能量谱作为特征向量;应用综合考虑观测数据和确定性先验知识的BVC-RBF神经网络实现信号分类,提高了网络的泛化能力.利用Matlab引擎、动态链接库(DLL)、组件对象模型(COM)3种混合编程方法将Mat...     

两阶段有向网络马尔可夫系统维修模型与分析

根据实际管道有向网络系统,作者构造了两阶段有向网络马尔可夫维修系统.对组成系统的边以及系统的状态进行了分析,运用求解指标的一般方法和Laplace变换,对模型的可靠性指标进行了求解,给出了边与系统瞬时可用度、瞬时故障频度、稳态可用度、稳态故障频度以及平均指标的表达式.将两部件不独立运行系统与两部件独立的系统可靠性指标进行对比,用数值示例加以说明.研究结果为有向网络的马尔可夫维修系统的进一步研究提供了基础.     

基于PID神经网络自动换挡过程油门调速

提出了一种在AMT换挡过程中运用PID神经网络进行油门调速的方法.通过台架实验,建立了东风康明斯EQB235-20柴油发动机的油门实验模型,同时在Matlab平台上对PID神经网络进行训练,使其输出逼近理想油门实验模型.将训练后的PID神经网络移植入ECU,进行发动机调速实验.实验表明,PID神经网络有响应速度快、鲁棒性好、收敛特性好的特点,提高了车辆的自适应能力.     

基于径向基函数网络的宣城市空气质量预测

提出了一种应用人工神经网络进行空气质量预测的方法,即采用径向基函数神经网络进行短期的空气质量预测;并采用了主成分分析方法降低神经网络学习矩阵维数,浓缩预测信息,降维去噪.选取宣城市气象局2003年到2005年地面气象观测资料作为预测因子,宣城市环境保护监测中心提供的PM10、SO2浓度值作为预测对象,进行训练学习和预测验证.研究结果表明:将该方法应用于空气质量预测,效果良好,具有较强的实用性和推广能力.