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.     

Quantum properties of nonclassical states generated by an optomechanical system with catalytic quantum scissors

A scheme is proposed to investigate the non-classical states generated by a quantum scissors device (QSD) operating on the the cavity mode of an optomechanical system. When the catalytic QSD acts on the cavity mode of the optomechanical system, the resulting state contains only the vacuum, single-photon and two-photon states depending upon the coupling parameter of the optomechanical system as well as the transmission coefficients of beam splitters (BSs). Especially, the output state is just a class of multicomponent cat state truncations at time t=2π by choosing the appropriate value of coupling parameter. We discuss the success probability of such a state and the fidelity between the output state and input state via QSD. Then the linear entropy is used to investigate the entanglement between the two subsystems, finding that QSD operation can enhance their entanglement degree. Furthermore, we also derive the analytical expression of the Wigner function (WF) for the cavity mode via QSD and numerically analyze the WF distribution in phase space at time t=2π. These results show that the high non-classicality of output state can always be achieved by modulating the coupling parameter of the optomechanical system as well as the transmittance of BSs.     

高中学业水平考试试题难度模型建构*

利用主成分分析方法厘清试题难度影响因素之间的关系,进而从问题表征、问题解决和结果输出等3个维度上建立了影响高中学业水平考试试题难度的主要因素。通过编写高中学业水平考试测试题进行实证研究,建立影响因素常见呈现类型的难易赋值规则。选用机器学习的线性回归方法建构试题难度分析模型,并使用高考化学江苏卷部分试题的实测难度数据进行校验,模型难度预测值和高考难度实测值具有较好的拟合度,表明模型具有较好的应用价值和推广意义。     

高分子化学线型缩聚反应数均聚合度计算的教学体验和改进

对高分子化学课程本科教学中有关线型缩聚反应数均聚合度的计算,以及聚合度与官能团过量之间的定性及定量关系进行了讨论。针对现有教学方法易引发学生误解和混淆的情况,定义了新的参数和发展了相应改进的计算方程。新的计算方程的计算过程清晰、参数定义明确、易于学生理解掌握,在实践中取得了良好的教学效果。     

An emerging strategy for tracing the anti-erectile dysfunction components of raw and salt-processed semens of Astragali Complanati by combinatory liquid chromatography–mass spectrometry-based quantitative analysis,efficacy assessment on impotent rats,and partial least squares regression

Semens of Astragali Complanati own anti-erectile dysfunction effect; however, the components which contribute to the anti-erectile dysfunction effect remain unclear. This work raised a strategy that integrates liquid chromatography coupled mass spectrometry-based quantitative analysis, anti-erectile dysfunction assessment on impotent rats, and their relationship analysis for pinpointing anti-erectile dysfunction components from semens of Astragali Complanati. For simultaneous quantification of seven major components in raw and salt-processed semens of Astragali Complanati, an accurate and reliable liquid chromatography–mass spectrometry method was developed under multiple reaction monitoring mode. Of note, chloramphenicol was employed as the internal standard. The method showed good linearity and repeatability, where the recovery rates of each component ranged from 98.1 to 104.7%, and the precisions of intra- and interday were all within 3.4%. The method has been used for quantification of the seven major components in 10 batches of raw and salt-processed semens of Astragali Complanati. Then, the anti-erectile dysfunction effects of raw and salt-processed semens of Astragali Complanati were evaluated on impotent rats. Gray relationship analysis and partial least squares regression were combined for elucidating the relationship. As a result, complanatuside, astragalin, complanatoside B, and kaempferol were found to be responsible for anti-erectile dysfunction effect of Astragali Complanati.     

支持向量回归在人体血红蛋白无创检测中的应用

采用线性渐变滤光片(Linear variable filter, LVF),优化设计高性能、便携式的人体血液成分近红外检测设备,研究了支持向量回归(Support vector regression, SVR)模型对人体血红蛋白(Hemoglobin, Hb)的预测能力及稳定性,以实现贫血疾病的无创诊断.无创采集100位志愿者食指前端光谱信息并划分定标集、验证集1和2.应用网格搜索方法优选惩罚参数与核函数参数c=5.28, g=0.33,用以建立稳健的SVR模型.随后,分别对验证集1和2中Hb水平进行定量分析.实验结果表明: 预测标准偏差(RMSEP) 分别为10.20 g/L和10.85 g/L,相对预测标准偏差(R-RMSEP) 为6.85%和7.48%,测量精度较高且SVR模型对不同样品的适应性较强,基本满足临床检测要求.基于SVR算法自行设计的LVF型近红外光谱检测设备在贫血症的无创诊断中有着良好的应用前景.     

在线固相萃取-超高效液相色谱-线性离子阱串联质谱法直接测定水中10种藻类毒素

建立了全自动在线固相萃取-超高效液相色谱-线性离子阱串联质谱法直接测定水中10种藻类毒素的方法.利用程序实现多次进样,通过在线固相萃取对藻类毒素进行富集,然后切换六通阀,将富集的目标物冲洗至分析柱进行分离后,进入线性离子阱质谱检测.10种藻类毒素在相应的浓度范围内线性关系良好,相关系数均大于0.99,检出限在0.0015~0.0050μg/L之间,3个浓度水平(0.02、0.10和1.00μg/L)的加标回收率为83.7%~98.5%.结果表明,在线固相萃取极大简化了前处理过程,线性离子阱串联质谱法提高了痕量藻类毒素测定的灵敏度,增强子离子扫描(EPI)谱库的建立为藻类毒素的确证提供保障.本方法适用于水体中多种藻类毒素的快速确证和定量测定.     

基于拓扑指数对含氧有机化合物闪点的定量构效关系研究

闪点是衡量化合物易燃程度的物理量,也是可燃性液体的一个重要安全性指标。本文对含氧有机化合物(包括醇、酚、醚、醛、酮、羧酸、酯等)的闪点与分子结构之间的关系进行分析,提取并计算了顶点度-距离指数(VDI)、奇偶指数(OEI)、边度-距离指数(EDI)、分子体积参数(MVI)和氢键指示变量(HB)等分子结构参数,并结合分子隐氢图顶点数(N)的2/3次方(N~(2/3)),对训练集中81个含氧有机化合物的闪点进行定量结构-性质相关分析。结果表明,由N~(2/3)、VDI、EDI、OEI、MVI和HB等6个参数对闪点建立的多元线性回归模型的相关系数(R)为0.9909,标准偏差(s)为6.39K,平均相对误差(ARD)为1.60%。用该模型对测试集中20个含氧有机化合物的闪点进行了预测,ARD为1.84%。