Estimation of surface tension of organic compounds using quantitative structure-property relationship

A novel quantitative structure-property relationship （QSPR） model for estimating the solution surface tension of 92 organic compounds at 20℃ was developed based on newly introduced atom-type topological indices. The data set contained non-polar and polar liquids, and saturated and unsaturated compounds. The regression analysis shows that excellent result is obtained with multiple linear regression. The predictive power of the proposed model was discussed using the leave-one-out （LOO） cross-validated （CV） method. The correlation coefficient （R） and the leave-one-out cross-validation correlation coefficient （Rcv） of multiple linear regression model are 0.991 4 and 0.991 3, respectively. The new model gives the average absolute relative deviation of 1.81% for 92 substances. The result demonstrates that novel topological indices based on the equilibrium electro-negativity of atom and the relative bond length are useful model parameters for QSPR analysis of compounds.

Quantitative Structure-Property Relationship on Prediction of Surface Tension of Nonionic Surfactants

A quantitative structure-property relationship (QSPR) study has been made for the prediction of the surface tension of nonionic surfactants in aqueous solution. The regressed model includes a topological descriptor, the Kier & Hall index of zero order (KH0) of the hydrophobic segment of surfactant and a quantum chemical one, the heat of formation (fHD) of surfactant molecules. The established general QSPR between the surface tension and the descriptors produces a correlation coefficient of multiple determination, 2r=0.9877, for 30 studied nonionic surfactants.

A computational toolbox for molecular property prediction based on quantum mechanics and quantitative structure-property relationship

Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemicalbased products.The life cycles of chemical products involve the procedures of conceptual product designs,experimental investigations,sustainable manufactures through appropriate chemical processes and waste disposals.During these periods,one of the most important keys is the molecular property prediction models associating molecular structures with product properties.In this paper,a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions,such as activity coefficient,and so forth.The workflow of framework consists of three steps.In the first step,a database is created for collections of basic molecular information;in the second step,quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties(pseudo experimental data),which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step.The whole framework has been carried out within a molecular property prediction toolbox.Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.

Application of quantum chemical descriptors into quantitative structure-property relationship models for prediction of the photolysis half-life of PCBs in water

Quantitative structure-property relationship(QSPR)models were developed for prediction of photolysis half-life(t_(1/2))of polychlorinated biphenyls(PCBs)in water under ultraviolet(UV)radiation.Quantum chemical descriptors computed by the PM3 Hamiltonian software were used as independent variables.The cross-validated Q^(2)_(cum)value for the optimal QSPR model is 0.966,indicating good prediction capability for lg t_(1/2)values of PCBs in water.The QSPR results show that the largest negative atomic charge on a carbon atom(Q-C)and the standard heat of formation(ΔH_(f))have a dominant effect on t_(1/2)values of PCBs.Higher Q_(C)^(-)values or lowerΔHf values of the PCBs leads to higher lg t_(1/2)values.In addition,the lg t_(1/2)values of PCBs increase with the increase in the energy of the highest occupied molecular orbital values.Increasing the largest positive atomic charge on a chlorine atom and the most positive net atomic charge on a hydrogen atom in PCBs leads to the decrease of lg t_(1/2)values.

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.

Prediction on Critical Micelle Concentration of Nonionic Surfactants in Aqueous Solution:Quantitative Structure-Property Relationship Approach

In order to predict the critical micelle concentration (cmc) of nonionic surfactants in aqueous solution,a quantitative structure-property relationship (QSPR) was found for 77 nonionic surfactants belonging to eight series. The best-regressed model contained four quantum-chemical descriptors,the heat of formation (ΔH),the molecular dipole moment (D),the energy of the lowest unoccupied molecular orbital (E_ LUMO ) and the energy of the highest occupied molecular orbital (E_ HOMO ) of the surfactant molecule; two constitutional descriptors,the molecular weight of surfactant (M) and the number of oxygen and nitrogen atoms (n_ ON ) of the hydrophilic fragment of surfactant molecule; and one topological descriptor,the Kier & Hall index of zero order (KH0) of the hydrophobic fragment of the surfactant. The established general QSPR between lg(cmc) and the descriptors produced a relevant coefficient of multiple determination:R 2=0.986. When cross terms were considered,the corresponding best model contained five descriptors E_ LUMO ,D,KH0,M and a cross term n_ ON ·KH0,which also produced the same coefficient as the seven-parameter model.

Quantitative Correlation of Chromatographic Retention and Acute Toxicity for Alkyl(1-phenylsulfonyl) Cycloalkane Carboxylates and Their Structural Parameters by DFT

Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.

Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics

BACKGROUND As a well-known fact to the public,gestational diabetes mellitus(GDM)could bring serious risks for both pregnant women and infants.During this important investigation into the linkage between GDM patients and their altered expression in the serum,proteomics techniques were deployed to detect the differentially expressed proteins(DEPs)of in the serum of GDM patients to further explore its pathogenesis,and find out possible biomarkers to forecast GDM occurrence.METHODS Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria.Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation,and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry.Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis,and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).RESULTS Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDMgravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteinsassociated with lipid metabolism, coagulation cascade activation, complement system and inflammatory responsein the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serumof GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk ofgestation.CONCLUSION GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complementsystem and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

Chemical composition and quantitative relationship between meteorological condition and fine particles in Beijing

The recent year's monitor results of Beijing indicated that the pollution level of fine particles PM 2.5 showed an increasing trend. To understand pollution characteristics of PM 2.5 and its relationship with the meteorological conditions in Beijing, a one-year monitoring of PM 2.5 mass concentration and correspondent meteorological parameters was performed in Beijing in 2001. The PM 2.5 levels in Beijing were very high, the annual average PM 2.5 concentration in 2001 was 7 times of the National Ambient Air Quality Standards proposed by US EPA. The major chemical compositions were organics, sulfate, crustals and nitrate. It was found that the mass concentrations of PM 2.5 were influenced by meteorological conditions. The correlation between the mass concentrations of PM 2.5 and the relative humidity was found. And the correlation became closer at higher relative humidity. And the mass concentrations of PM 2.5 were negtive-correlated to wind speeds, but the correlation between the mass concentration of PM 2.5 and wind speed was not good at stronger wind.

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned. In the present work, machine learning is used to develop quantitative structure–property relationship models. The fuel chemical structure is represented by molecular descriptors, allowing the linking of important features of the fuel composition and key properties of fuel utilization. Feature selection is employed to select the most relevant features that describe the chemical structure of the fuel and several machine learning algorithms are tested to construct interpretable models. The effectiveness of the methodology is demonstrated through the development of accurate and interpretable predictive models for cetane numbers, with a focus on understanding the link between molecular structure and fuel properties. In this context, matrix-based descriptors and descriptors related to the number of atoms in the molecule are directly linked with the cetane number of hydrocarbons. Furthermore, the results showed that molecular connectivity indices play a role in the cetane number for aromatic molecules. Also, the methodology is extended to predict the cetane number of ester and ether molecules, leveraging the design of alternative fuels towards fully sustainable fuel utilization.

Quantitative identification of relatively highlevel radioactive sandstone in the Upper Triassic Yanchang Formation of the Ordos Basin, China

Most relatively high-level radioactive sandstone(HRSS)reservoir has considerable oil(or gas)resource potential.HRSS is often wrongly identified due to its similar logging response characteristics as mudstone,which leads to the omission of effective reservoirs.In this paper,a quantitative identification method for HRSS is proposed after the analyzing of the response characteristics and relationship between spontaneous potential log and natural gamma-ray log in conventional sandstone and mudstone strata.Take the Upper Triassic Yanchang Formation in Ordos Basin as an example:the responses of spontaneous potential log and the responses of natural gamma-ray log are synchronized and positively correlated in conventional sandstone and mudstone strata,but they are not synchronized in HRSS.Quantitative identification of HRSS was realized based on this synchronization feature,and a"virtual compensation"of natural gamma-ray log was performed.At the same time,logging evaluation method about HRSS has been discussed.The final results shows that this identification method work effectively,and can reduce the misjudgment and omission of effective reservoirs.

Quantitative multiparameter prediction of fault-related fractures： a case study of the second member of the Funing Formation in the Jinhu Sag, Subei Basin

In this paper, the analysis of faults with different scales and orientations reveals that the distribution of fractures always develops toward a higher degree of similarity with faults, and a method for calculating the multiscale areal fracture density is proposed using fault-fracture self-similarity theory. Based on the fracture parameters observed in cores and thin sections, the initial apertures of multiscale fractures are determined using the constraint method with a skewed distribution. Through calculations and statistical analyses of in situ stresses in combination with physical experiments on rocks, a numerical geomechanical model of the in situ stress field is established. The fracture opening ability under the in situ stress field is subsequently analyzed. Combining the fracture aperture data and areal fracture density at different scales, a calculation model is proposed for the prediction of multiscale and multiperiod fracture parameters, including the fracture porosity, the magnitude and direction of maximum permeability and the flow conductivity. Finally, based on the relationships among fracture aperture,density, and the relative values of fracture porosity and permeability, a fracture development pattern is determined.

A related degree-based frequent pattern mining algorithm for railway fault data

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm.However,high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up.In the context of such needs,we propose a related degree-based frequent pattern mining algorithm,named Related High Utility Quantitative Item set Mining(RHUQI-Miner),to enable the effective mining of railway fault data.The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees,reducing redundancy and invalid frequent patterns.Subsequently,it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm.The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process,thus providing data support for differentiated and precise maintenance strategies.

Quantitative prediction of oil saturation of unconsolidated sandstone reservoir based on time-lapse seismic “relative difference method”: Taking Zeta oil field in West Africa as an example

In view of the disadvantage that the absolute difference of time-lapse seismic(the difference between monitoring data and base data) is not only related to the change of oil saturation, but also closely related to the thickness of reservoir, a time-lapse seismic "relative difference method"(the ratio of monitoring data to base data) not affected by the thickness of reservoir but only related to the change of fluid saturation, is proposed through seismic forward modeling after fluid displacement simulation. Given the same change of fluid saturation, the absolute difference of time-lapse seismic conforms to the law of "tuning effect" and seismic reflection of "thin bed", and the remaining oil prediction method based on absolute difference of time-lapse seismic is only applicable to the reservoirs with uniform thickness smaller than the tuning thickness or with thickness greater than the tuning thickness. The relative difference of time-lapse seismic is not affected by reservoir thickness, but only related to the change of fluid saturation. It is applicable to all the deep-sea unconsolidated sandstone reservoirs which can exclude the effect of pressure, temperature, pore type and porosity on seismic. Therefore, the relation between the relative difference of time-lapse seismic and the change of fluid saturation, which is obtained from seismic forward modeling after Gassmann fluid displacement simulation, can be used to quantitatively predict the change of reservoir water saturation and then the distribution of the remaining oil. The application of this method in deep sea Zeta oil field in west Africa shows that it is reasonable and effective.

Quantitative Models for the Structure and Photodegradation of Polycyclic Aromatic Hydrocarbons

Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.

Molecular Structural Characterization and Quantitative Prediction of Reduced Ion Mobility Constants for Diversified Organic Compounds

Based on two-dimensional topological structures, a novel molecular electronegativity interaction vector with hybridization （MEHIV） was developed to describe atomic hybridization state in different molecular environments. Five quantitative models by MEHIV characterization and multiple linear regression modeling were successfully established to predict reduced ion mobility constants （Ko） of alkanes, aromatic hydrocarbons, fatty alcohols, fatty aldehydes and ketones and carboxylic esters. The correlation coefficients Roy by leave-one-out cross-validation are 0.792, 0.787, 0,949, 0.972 and 0.981, respectively, and the standard deviations SDcv are 0.067, 0.086, 0.064, 0.043 and 0.042, respectively. These results suggested that MEHIV is an excellent topological index descriptor with many advantages such as straightforward physicochemical meaning, high characterization competence, convenient expansibility and easy manipulation.

Correlation of Critical Micelle Concentration of Sodium Alkyl Benzenesulfonates with MolecularDescriptors

The hydrophobic-hydrophilic segment geometries of 36 sodium alkyl benzenesulfonates were fully optimized and calculated by abinitio RHF/6-31G（d）, quantum chemical data such as the charge density, the energy of molecular orbital and the dipole moment were obtained. Based on two topological descriptors and one quantum chemical descriptor, a significant quantitative structure-property relationship （QSPR） model for the critical micelle concentration （Cmc） of sodium alkyl benzenesulfonate surfactants was obtained by using the multiple linear regression technique. The good correlation coefficient of Re （0. 980） and cross-validation correlation coefficient Rcv^2 （0. 974） indicate the excellent capability and stability of the regression equation developed. In addition, linear relationships between logarithm of Cmc and the dipole moment of surfaetant hydrophobic hydrophilic segments for each homologous series have also been established with high correlation coefficient.

Identification of protein targets for the antidepressant effects of Kai-Xin-San in Chinese medicine using isobaric tags for relative and absolute quantitation

Kai-Xin-San consists of Ginseng Radix, Polygalae Radix, Acori Tatarinowii Rhizoma, and Poria at a ratio of 3:3:2:2. Kai-Xin-San has been widely used for the treatment of emotional disorders in China. However, no studies have identified the key proteins implicated in response to Kai-Xin-San treatment. In this study, rat models of chronic mild stress were established using different stress methods over 28 days. After 14 days of stress stimulation, rats received daily intragastric administrations of 600 mg/kg Kai-Xin-San. The sucrose preference test was used to determine depression-like behavior in rats, while isobaric tags were used for relative and absolute quantitation-based proteomics to identify altered proteins following Kai-Xin-San treatment. Kai-Xin-San treatment for 2 weeks noticeably improved depression-like behaviors in rats with chronic mild stress. We identified 33 differentially expressed proteins: 7 were upregulated and 26 were downregulated. Functional analysis showed that these differentially expressed proteins participate in synaptic plasticity, neurodevelopment, and neurogenesis. Our results indicate that Kai-Xin-San has an important role in regulating the key node proteins in the synaptic signaling network, and are helpful to better understand the mechanism of the antidepressive effects of Kai-Xin-San and to provide objective theoretical support for its clinical application. The study was approved by the Ethics Committee for Animal Research from the Chinese PLA General Hospital(approval No. X5-2016-07) on March 5, 2016.

Operational Evaluation of the Quantitative Precipitation Estimation by a CINRAD-SA Dual Polarization Radar System

In this paper,a quantitative precipitation estimation based on the hydrometeor classification(HCA-QPE)algorithm was proposed for the first operational S band dual-polarization radar upgraded from the CINRAD/SA radar of China.The HCA-QPE algorithm,localized Colorado State University-Hydrometeor Identification of Rainfall(CSUHIDRO)algorithm,the Joint Polarization Experiment(JPOLE)algorithm,and the dynamic Z-R relationships based on variational correction QPE(DRVC-QPE)algorithm were evaluated with the rainfall events from March 1 to October 30,2017 in Guangdong Province.The results indicated that even though the HCA-QPE algorithm did not use the observed rainfall data for correction,its estimation accuracy was better than that of the DRVC-QPE algorithm when the rainfall rate was greater than 5 mm h-1;and the stronger the rainfall intensity,the greater the QPE improvement.Besides,the HCA-QPE algorithm worked better than the localized CSU-HIDRO and JPOLE algorithms.This study preliminarily evaluated the improved accuracy of QPE by a dual-polarization radar system modified from CINRAD-SA radar.

Different protein expression patterns in rat spinal nerves during wallerian degeneration assessed using isobaric tags for relative and absolute quantitation proteomics profiling

Sensory and motor nerve fibers of peripheral nerves have different anatomies and regeneration functions after injury. To gain a clear understanding of the biological processes behind these differences, we used a labeling technique termed isobaric tags for relative and absolute quantitation to investigate the protein profiles of spinal nerve tissues from Sprague-Dawley rats. In response to Wallerian degeneration, a total of 626 proteins were screened in sensory nerves, of which 368 were upregulated and 258 were downregulated. In addition, 637 proteins were screened in motor nerves, of which 372 were upregulated and 265 were downregulated. All identified proteins were analyzed using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of bioinformatics, and the presence of several key proteins closely related to Wallerian degeneration were tested and verified using quantitative real-time polymerase chain reaction analyses. The differentially expressed proteins only identified in the sensory nerves were mainly relevant to various biological processes that included cell-cell adhesion, carbohydrate metabolic processes and cell adhesion, whereas differentially expressed proteins only identified in the motor nerves were mainly relevant to biological processes associated with the glycolytic process, cell redox homeostasis, and protein folding. In the aspect of the cellular component, the differentially expressed proteins in the sensory and motor nerves were commonly related to extracellular exosomes, the myelin sheath, and focal adhesion. According to the Kyoto Encyclopedia of Genes and Genomes, the differentially expressed proteins identified are primarily related to various types of metabolic pathways. In conclusion, the present study screened differentially expressed proteins to reveal more about the differences and similarities between sensory and motor nerves during Wallerian degeneration. The present findings could provide a reference point for a future investigation into the differences between sensory and motor nerves in Wallerian degeneration and the characteristics of peripheral nerve regeneration. The study was approved by the Ethics Committee of the Chinese PLA General Hospital, China(approval No. 2016-x9-07) in September 2016.