A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships （QSRR） models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties （SGBP）, involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance （ACC）. ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonuclcotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.

Quantitative Structure-retention Relationship Study of Volatile Components from Rosa Banksiae Ait

In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.

Quantitative Structure-retention Relationship Study of Polychlorinated Dibenzothiophenes by Molecular Electronegativity Distance Vector(MEDV)

Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.

Quantitative Structure-chromatographic Retention Relationship for Polychlorinated Dibenzothiophenes and Their Corresponding Sulfones

Polychlorinated dibenzothiophenes（PCDTs）and their corresponding sulfone（PCDTO2）compounds are a group of important persistent organic pollutants.In the present study,geometrical optimization and subsequent calculations of electrostatic potentials（ESPs）on molecular surface have been performed for all 135 PCDTs and 135 PCDTO2 congeners at the HF/6-31G*level of theory.A number of statistically-based parameters have been extracted.Linear relationship between gas-chromatographic retention index（RI）and the structural descriptors have been established by multiple linear regression.The result shows that two descriptors derived from positive electrostatic potential on molecular surface, ■ and π,together with the molecular volume(Vmc)and the energy of the lowest unoccupied molecular orbital(ELUMO)can be well used to express the quantitative structure-retention relationship（QSRR）of PCDTs and PCDTO2.Predictive capability of the two models has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient(RCV)of 0.996 and 0.997,respectively.Furthermore,the predictive power of the models is further examined for the external test set.Correlation coefficients（R）between the observed and predicted RI values for the external test set are 0.997 and0.998,respectively,validating the robustness and good prediction of our model.The QSRR model established may provide again a powerful method for predicting chromatographic properties of aromatic organosulfur compounds.

A novel molecular distance edge vector as applied to chemical modeling of quantitative structure-retention relationships:Various gas chromatographic retention behaviors of polychlorinated dibenzofurans on different polarity-varying stationary phases

Based on the identical group as a pseudo atom instead of a typical atom, a novel modified molecular dis-tance-edge (MDE) vector μ was developed in our laboratory to characterize chemical structure of polychlorinated diben-zofurans (PCDFs) congeners and/or isomers. Quantitative structure-retention relationships (QSRRs) between the new VMDE parameters and gas chromatographic (GC) retention behavior of PCDFs were then generated by multiple linear regression (MLR) method for non-polar, moderately polar, and polar stationary phases. Four excellent models with high correlation coefficients, R=0.984-0.995, were proposed for non-polar columns (DB-5, SE-54, OV-101). For the moder-ately polar columns (OV-1701), the correlation coefficient of the developed good model is only 0.958. For the polar col-umns (SP-2300), the QSRR model is poor with R=0.884. Then cross validation with leave-one out of procedure (CV) is performed in high correlation with the non-polar (Rcv=992-0.974) and weakly polar (Rcv=921) columns and in little cor-relation (Rcv=0.834) with the polar columns. These results show that the new μ vector is suitable for describing the re-tention behaviors of PCDFs on non-polar and moderately polar stationary phases and not for the various gas chroma-tographic retention behaviors of PCDFs on the different po-larity-varying stationary phases.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.

The Relationship between the Firm Size and the Quantity of Accountants Employed by the Firm： An Analysis of the 436 Listed Firms in the Chinese Shenzhen Stock Exchange

There are 9.56 million accountants in China, who are working in different size firms and nonprofit organizations. The objective of this article is to examine the quantitative relationship between the firm size and the quantity of accountants working for the firm. In this paper, the employees, sales revenues, and total assets in a firm are employed to measure firm size. The authors collect and analyze the data of 436 listed firms from the Chinese Shenzhen Stock Exchange. The result of this study indicates there is a positive relationship between the firm size and the quantity of accountants employed by the firm. The study also establishes the multiple regression equation, which can be used to predict the quantity of accountants of listed firms. And it provides a way to predict the quantity of accountants of legal organizations.

Simultaneously quantifying hundreds of acylcarnitines in multiple biological matrices within ten minutes using ultrahigh-performance liquid-chromatography and tandem mass spectrometry

Acylcarnitines are metabolic intermediates of fatty acids and branched-chain amino acids having vital biofunctions and pathophysiological significances. Here, we developed a high-throughput method for quantifying hundreds of acylcarnitines in one run using ultrahigh performance liquid chromatography and tandem mass spectrometry (UPLC-MS/MS). This enabled simultaneous quantification of 1136 acylcarnitines (C0–C26) within 10-min with good sensitivity (limit of detection < 0.7 fmol), linearity (correlation coefficient > 0.992), accuracy (relative error < 20%), precision (coefficient of variation (CV), CV < 15%), stability (CV < 15%), and inter-technician consistency (CV < 20%, n = 6). We also established a quantitative structure-retention relationship (goodness of fit > 0.998) for predicting retention time (tR) of acylcarnitines with no standards and built a database of their multiple reaction monitoring parameters (tR, ion-pairs, and collision energy). Furthermore, we quantified 514 acylcarnitines in human plasma and urine, mouse kidney, liver, heart, lung, and muscle. This provides a rapid method for quantifying acylcarnitines in multiple biological matrices.

基于原子类型电性拓扑状态指数的最小点火能预测模型研究

针对危险化学品最小点火能的试验测量存在一定的困难且具有很大不确定性的问题,收集了61种化学品的最小点火能试验值,并仅根据其分子结构计算了原子类型电性拓扑状态指数(ETSI),最终选择了23种ETSI作为分子描述符进行建模。首先,尝试基于61种危险化学品的最小点火能值,通过多元线性回归和支持向量机进行QSPR建模,但所建模型的内部稳定性很差,不满足基本的要求。经分析可能是因为描述符太多而样本相对较少造成的。通过从数据集中删除13个最小点火能值,ETSI的种类减少到14个,并采用相同的方法进行QSPR建模。经验证,新建模型在拟合能力、内部稳定性和外部预测能力3个方面均具有优异的性能,可以在不需要购买任何昂贵的软件和硬件的前提下,方便、快捷地预测化学品的最小点火能。

Relationship between rock uniaxial compressive strength and digital core drilling parameters and its forecast method

The rock uniaxial compressive strength(UCS)is the basic parameter for support designs in underground engineering.In particular,the rock UCS should be obtained rapidly for underground engineering with complex geological conditions,such as soft rock,fracture areas,and high stress,to adjust the excavation and support plan and ensure construction safety.To solve the problem of obtaining real-time rock UCS at engineering sites,a rock UCS forecast idea is proposed using digital core drilling.The digital core drilling tests and uniaxial compression tests are performed based on the developed rock mass digital drilling system.The results indicate that the drilling parameters are highly responsive to the rock UCS.Based on the cutting and fracture characteristics of the rock digital core drilling,the mechanical analysis of rock cutting provides the digital core drilling strength,and a quantitative relationship model(CDP-UCS model)for the digital core drilling parameters and rock UCS is established.Thus,the digital core drilling-based rock UCS forecast method is proposed to provide a theoretical basis for continuous and quick testing of the surrounding rock UCS.

陆相湖盆“源-汇”系统要素定量关系——以渤海湾盆地庙西北地区为例

针对古代陆相湖盆“源-汇”系统定量刻画难度大和拟合模式不统一等问题,以渤海湾盆地庙西北地区古近系沙二段为研究对象,以钻井、薄片、壁芯以及三维地震资料为基础,以层序地层学、沉积学、地貌学和“源-汇”系统的基本理论和研究思路为指导,定量了刻画“源-汇”系统要素,分析了要素间的定量联系。结果表明:渤海湾盆地庙西北地区沙二段搬运通道发育规模与物源高度成正相关关系,物源高度越高,搬运通道规模越大;三角洲发育规模与物源体积成正相关关系,物源体积越大,三角洲发育规模越大;过大或过小的搬运通道宽深比均不利于三角洲的大规模发育。在定量关系研究的基础上,建立了“源-汇”系统要素“JASG”模式,为古代陆相湖盆“源-汇”系统研究提供了定量表征实例。

基于化合物结构预测人体ADME/PK性质的效能评价

目的:整合定量结构性质关系(QSPR)模型预测化合物在人体的吸收、分布、代谢、排泄(ADME)性质参数和基于生理药代动力学(PBPK)模型预测人体药代动力学(PK)曲线的方法,并评价该方法的预测能力。方法:以文献报道的具有体外实测理化、生物药剂学性质和临床观测PK性质的14个化合物作为模型药物。采用ADMET Predictor软件的QSPR模型预测各个化合物的理化与生物药剂学参数,将上述预测的参数加载到GastroPlus软件的PBPK模型中预测各个化合物经口服给药后在人体的PK曲线以及主要PK参数。对比预测与实测ADME/PK参数间的差异,以评估所用模型的预测效能。结果:QSPR模型预测的理化与生物药剂学性质参数与观测值间的绝对值较为接近,两者具有较好的线性关系(大部分参数的相关系数均接近或超过0.7);14个化合物中,有6个化合物(43%)的最大血药浓度(C_(max))预测值落在观测值的2倍误差范围内,9个化合物(64%)的C_(max)落在观测值的3倍误差范围内;有7个化合物(50%)的血药浓度-时间曲线下的面积(AUC)预测值落在观测值的2倍误差范围内,8个化合物(57%)的AUC落在观测值的3倍误差范围内。结论:联合QSPR和PBPK模型可用于评估化合物的ADME性质并进一步预测人体PK特征。经过当前工作的验证,表明该方法具有较高的预测能力。

1,4-二氢吡啶衍生物的QSAR模型构建及制备

采用Hyperchem 8.0软件,通过分子动力学MM+和半经验AM1算法,优化并计算得到30种1,4-二氢吡啶衍生物的17种量子化学参数。运用Statistica 8.0软件中的主成分分析法,结合线性回归分析法,筛选出3个最佳参数与1,4-二氢吡啶衍生物的抗结核活性关联,构建定量构效关系(QSAR)模型。所建多元线性回归方程的相关系数R2为0.75,留一法交叉检验系数q2为0.72,活性实验值与预测值相关性较好。采用该模型设计并制备得到1种具有良好水溶性和潜在抗结核活性的N3,N5-二(对磺酸钾基)苯基-2,6-二甲基-3,5-二甲酰氨基-1,4-二氢吡啶(TM),用1H NMR、IR、MS和元素分析表征结构,并考察了紫外和荧光性能。良好的稳定性和预测能力使该QSAR模型有望用于结核病候选药物的筛选。

角膜胶原交联后基质弹性模量与深度的定量关系

目的探索胶原交联术后4周角膜基质弹性模量与基质深度的定量关系,为交联术前个性化手术设计和术后效果评估提供理论基础。方法选取健康7月龄新西兰白兔7只,任选1只做空白对照组,其余6只左眼行角膜交联术,为实验组,右眼作自身对照。术后4周,取眼球制作角膜基质冷冻切片,沿角膜基质深度方向选取3~4个50μm×50μm的区域进行原子力显微镜压痕实验,使用Sneddon接触模型对压痕曲线进行拟合,获得弹性模量,研究角膜基质弹性模量与基质深度的定量关系。结果胶原交联术后4周,角膜基质弹性模量是其自身对照眼弹性模量的1.59倍。实验组相对弹性模量(E)与基质深度(x)的关系为E=0.66+6.05 e(-x/77.50),拟合优度为0.97。有效的交联深度为190μm±41μm。结论胶原交联术后4周角膜基质的弹性模量随基质深度仍存在指数衰减的规律。本文方法可获得角膜的有效交联深度,为相关的实验提供参考。

地震应急救助需求的模型化处理——来自地震震例的经验分析

地震发生后对灾区的支援和救助是减轻灾民痛苦的重要措施 ,但人们往往需要较长时间才能逐渐了解灾区的真实需要 ,这在一定程度上延误了救灾工作的顺利开展。通过对中国可持续发展信息网下属的《综合自然灾害信息共享》子网提供的大量地震资料的分析和理论研究 ,提出了可以定量化快速确定地震灾区可能的救援需求的一系列关系式。通过在闽南防震减灾示范区的实际应用 ,证实这种根据地震初期经济损失和人员伤亡情况快速得出的灾区救援需求框架 ,可以有效地提示救灾人员进行科学调度 ,缩短救灾人员和救灾物资的集中时间 ,具有一定的实际应用价值。

电拓扑状态预测有机磷酸酯类化合物的气相色谱保留指数

以原子类型电拓扑状态指数(ETSI)有效表征35个有机磷酸酯类化合物(OP)的分子结构,应用基于预测的变量选择与模型化(VSMP)方法建立OP化合物在3种不同固定相上的气相色谱保留指数(RI)与分子结构(ETSI)的定量相关模型.结果表明,影响不同固定相上OP色谱保留的主要结构因素都是由7个ETSI描述子对应的子结构碎片,即:=CH2,≡C—,aaC—,=O,—O—,Cl和Br.其中子结构aaC—,=O和—O与OP化合物母体骨架密切相关,而=CH2,≡C—,—Cl和—Br反映支链或取代基的变化.通过多元线性回归法建立OP化合物在三个固定相上的定量结构-保留相关模型(QSRR)发现,各QSAR模型的估计相关系数均在0.99以上,LOO检验相关系数在0.98以上,表明模型具有良好估计能力与稳定性.应用28个OP训练集样本构建的QSRR模型预测外部7个检验集RI结果表明训练集模型具有良好预测能力.

湖盆浅水三角洲分流河道储层构型模式:以鄂尔多斯盆地东缘延长组野外露头为例

应用野外露头资料,通过选取与物源方向近于垂直观察剖面的精细描述、测量和分析,对鄂尔多斯盆地东缘延河、仕望河露头延长组典型的浅水三角洲沉积分流河道储层构型进行研究。研究结果表明:鄂尔多斯盆地延长组浅水三角洲分流河道按其分布位置和成因可分为主干分流河道(包括深切分流河道)、汊道和末端分流河道3种类型;除深切分流河道外,主干分流河道、汊道和末端分流河道砂体宽度、厚度呈正相关的双对数关系,相关系数高达0.97,即三者宽厚比相近,而深切分流河道宽厚比较小;其内部充填样式包括垂向加积充填和侧向加积充填2种,以垂向加积充填为主,且可进一步分为水平状、同心状和心滩—水道充填3种;分流河道存在侧向拼接—垂向切叠式、侧向拼接—垂向叠加式和孤立式3种复合叠置样式。

雷达定量降水估算在水文模式汛期洪水预报中的应用试验

通过雷达测雨技术获得高时空分辨率的降雨信息,作为水文模型的输入,用以提高水文预报的精度。文章以湖北省白莲河流域为例,利用分组Z-I关系转化雷达反射率为雨强,运用地面雨量站网实测雨量对其进行校准,将不同方法估算得到的雨量结果输入新安江模型进行洪水预报测试,结果表明未校准雷达估算降雨量直接输入水文模型,其结果是不理想的;利用校准后的雷达估算降雨量进行洪水预报,精度得到了很大提高。

流域地貌分形特征与侵蚀产沙定量耦合关系试验研究

流域地貌形态科学、准确和综合量化成为建立具有广泛适用性的流域土壤侵蚀预报模型的关键科学问题之一。分形理论的提出为流域地貌形态定量研究开辟了新的思路。本文依据分形理论,在建立小流域概化模型基础上,采用模拟降雨试验、高精度摄影测量和G IS技术,对流域模型地貌形态分形特征与侵蚀产沙定量耦合关系进行了研究。结果表明,基于消除了降雨特征影响的相对输沙率,可将流域模型侵蚀产沙过程划分为初期、活跃期和稳定期3个阶段;在不同侵蚀产沙时段,流域模型地貌形态分形信息维数Di呈现与相对输沙率Sr基本类似的变化趋势,即先增大再减小、最后趋于平稳;流域模型相对输沙率Sr与地貌形态分形信息维数Di以乘幂形式呈显著正相关,相关指数r2为0.7423;地貌形态分形信息维数Di较好反映了地貌形态特征对流域模型侵蚀产沙过程的影响,可以作为流域模型侵蚀产沙过程预报地貌形态特征综合量化指标。