Quantitative relationship between surface sedimentary diatoms and water depth in North-Central Bohai Bay,China

To study the quantitative relationship between surface sedimentary diatoms and water depth,67 surface samples were collected for diatom analysis on eight profiles with water depth variation from the muddy intertidal zone to the shallow sea area in North-Central Bohai Bay,China.The results showed that the distribution of diatoms changed significantly in response to the change in water depth.Furthermore,the quantitative relationship between the distribution of dominant diatom species,their assemblages,and the water depth was established.The water depth optima for seven dominant species such as Cyclotella striata/stylorum,Paralia sulcata,and Coscinodiscus perforatus and the water depth indication range of seven diatom assemblages were obtained in the study area above the water depth(elevation)of-10 m.The quantitative relationship between surface sedimentary diatoms and water depth provides a proxy index for diatom-paleo-water depth reconstruction in the strata in Bohai Bay,China.

Quantitative relationship between flagellate abundance and suspended particle density in Huanghai Sea and East China Sea in summer

An investigation was carried out in the Huanghai Sea and the East China Sea to study the quantitative relationship between the abundance of flagellates and the density of suspended particles in the summer of 2001. The results show that the abundance of flagellates varies from 44-12 600 cell/cm^3, and flagellates sometimes constitutes a significant part of suspended particles. The size-spectra of suspended particles can be divided into four categories： flat spectrum, humped spectrum, plankton spectrum and mixed spectrum. In general, the abundance of flagellates varies in proportion to the density of suspended particles. However, their quantitative relations reveal different characteristics in the seawater samples of different types of particle-size spectrum. This is only a preliminary study of the quantitative relationship between flagellates and suspended particles, which might lead to a potential convenient approach to the estimation of flagellate abundance in the sea.

Quantitative relationship between channels and bars in a tidal reach of the lower Yangtze River:Implications for river management

Deep-water navigation channels in the tidal reaches of the lower Yangtze River are affected by water and sediment fluxes that produce complex shoals and unstable channel conditions.The Fujiangsha reach is particularly difficult to manage,as it has many braided channels within the tidal fluctuation zone.In this study,hydrologic and topographic data from the Fujiangsha reach from 2012 to 2017 were used to examine the variations in deposition and erosion,flow diversion,shoals,and channel conditions.Since the Three Gorges Dam became operational and water storage was initiated,the Fujiangsha reach has shown an overall tendency toward erosion.Channels deeper than 10 m accounted for 83.7% of the total erosion of the Fujiangsha reach during 2012-2017.Moreover,the dominant channel-forming sediments have gradually changed from suspended sediments to a mixed load of suspended and bed-load sediments.Deposition volumes of these sediments has varied significantly among different channels,but has mainly occurred in the Fubei channel.Furthermore,periodic variations in the Jingjiang point bar have followed a deposition-erosion-deposition pattern,and the downstream Shuangjian shoal mid-channel bar has been scoured and shortened.Approximately 44.0% of the bed load from the upstream Fujiangsha reach is deposited within the 12.5-m deep Fubei channel.The increased erosion and river flow from the Jingjiang point bar and the Shuangjian shoal during the flood season constituted 59.3% and 40.7%,respectively,of the total amount of siltation in the Fubei channel.

Toxicity of Selected Imidazolium-based Ionic Liquids on Caenorhabditis elegans： a Quantitative Structure-Activity Relationship Study

Due to the large number of ionic liquids （ILs） and their potential environmental risk, assessing the toxicity of ILs by ecotoxicological experiment only is insufficient. Quantitative structure- activity relationship （QSAR） has been proven to be a quick and effective method to estimate the viscosity, melting points, and even toxicity of ILs. In this work, the LC50 values of 30 imidazolium-based ILs were determined with Caenorhabditis elegans as a model animal. Four suitable molecular descriptors were selected on the basis of genetic function approximation algorithm to construct a QSAR model with an R^2 value of 0.938. The predicted lgLC50 in this work are in agreement with the experimental values, indicating that the model has good stability and predictive ability. Our study provides a valuable model to predict the potential toxicity of ILs with different sub-structures to the environment and human health.

Quantitative structure-activity relationship study on the biodegradation of acid dyestuffs

Quantitative structure-biodegradability relationships （QSBRs） were established to develop predictive models and mechanistic explanations for acid dyestuffs as well as biological activities. With a total of four descriptors, molecular weight （MW）, energies of the highest occupied molecular orbital （EHOMO）, the lowest unoccupied molecular orbital （ELUMO）, and the excited state （EES）, calculated using quantum chemical semi-empirical methodology, a series of models were analyzed between the dye biodegradability and each descriptor. Results showed that EHOMO and Mw were the dominant parameters controlling the biodegradability of acid dyes. A statistically robust QSBR model was developed for all studied dyes, with the combined application of EHOMO and Mw. The calculated biodegradations fitted well with the experimental data monitored in a facultative-aerobic process, indicative of the reliable prediction and mechanistic character of the developed model.

A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

Study on the Quantitative Structure-toxicity Relationships for the Selected Esters by Using Molecular Electronegativity Interaction Vector (MEIV)

The molecular electronegativity interaction vector （MEIV） was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression （MLR） and partial least-squares regression （PLS）. The correlation coefficients （R） of the two models were 0.945 and 0.941, respectively. The models were evaluated by performing the cross validation with the leave-one-out （LOO） procedure. The cross-verification correlation coefficients （RCV） of the two models were 0.921 and 0.919, respectively. The results showed that the models constructed in this work could provide estimation stability and favorable predictive ability.

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.

New Descriptors of Amino Acids and Its Applications to Peptide Quantitative Structure-activity Relationship

A new set of descriptors, HSEHPCSV （component score vector of hydrophobic, steric, and electronic properties together with hydrogen bonding contributions）, were derived from principal component analyses of 95 physicochemical variables of 20 natural amino acids separately according to different kinds of properties described, namely, hydrophobic, steric, and electronic properties as well as hydrogen bonding contributions. HSEHPCSV scales were then employed to express structures of angiotensin-converting enzyme inhibitors, bitter tasting thresholds and bactericidal 18 peptide, and to construct QSAR models based on partial least square （PLS）. The results obtained are as follows： the multiple correlation coefficient （R2cum） of 0.846, 0.917 and 0.993, leave-one-out cross validated Q2cm of 0.835, 0.865 and 0.899, and root-mean-square error for estimated error （RMSEE） of 0.396, 0.187and 0.22, respectively. Satisfactory results showed that, as new amino acid scales, data of HSEHPCSV may be a useful structural expression methodology＇for the studies on peptide QSAR （quantitative structure-activity relationship） due to many advantages such as plentiful structural information, definite physical and chemical meaning and easy interpretation.

Simulation of ^(13)C NMR chemical shifts of carbinol carbon atoms using quantitative structure-spectrum relationships

A quantitative structure-spectrum relationship (QSSR) model was developed to simulate 13C nuclear magnetic resonance (NMR) spectra of carbinol carbon atoms for 55 alcohols. The proposed model,using multiple linear regression,contained four descriptors solely extracted from the molecular structure of compounds. The statistical results of the final model show that R2= 0.982 4 and S=0.869 8 (where R is the correlation coefficient and S is the standard deviation). To test its predictive ability,the model was further used to predict the 13C NMR spectra of the carbinol carbon atoms of other nine compounds which were not included in the developed model. The average relative errors are 0.94% and 1.70%,respectively,for the training set and the predictive set. The model is statistically significant and shows good stability for data variation as tested by the leave-one-out (LOO) cross-validation. The comparison with other approaches also reveals good performance of this method.

Quantitative Structure-activity Relationship(QSAR) Study of Toxicity of Substituted Aromatic Compounds to Photobacterium Phosphoreum

With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity relationship（QSAR） models to predict the acute toxicity（-lgEC50） of substituted aromatic compounds to Photobacterium phosphoreum were established.Four molecular descriptors that appear in the MLR model,namely,the second order valence molecular connectivity index（2XV）,the energy of the highest occupied molecular orbital（EHOMO）,the logarithm of n-octyl alcohol/water partition coefficient（logKow） and the Connolly molecular area（MA）,were inputs of the ANN model.The root-mean-square error（RMSE） of the training and validation sets of the ANN model are 0.1359 and 0.2523,and the correlation coefficient（R） is 0.9810 and 0.8681,respectively.The leave-one-out（LOO） cross validated correlation coefficient（Q L2OO） of the MLR and ANN models is 0.6954 and 0.6708,respectively.The result showed that the two methods are complementary in the calculations.The regression method gave support to the neural network with physical explanation,and the neural network method gave a more accurate model for QSAR.In addition,some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.

Quantitative Structure-activity Relationship Study on the Antioxidant Activity of Carotenoids

Carotenoids are a family of effective active oxygen scavengers, which can reduce the danger of occurrence of chronic diseases such as cardiovascular disease, cataract, cancer, and so on. The quantitative structure-activity relationship （QSAR） equation between carotenoids and antioxidant activity was established by quantum chemistry AM1, molecular mechanism （MM＋） and stepwise regression analysis methods, and the model was evaluated by leave-one-out approach. The results showed that the significant molecular descriptors related to the antioxidant activity of carotenoids were the energy difference （E_HL） between the lowest unoccupied molecular orbital （LUMO） and the highest occupied molecular orbital （HOMO） and ionization energy （Eiso）. The model showed a good predictive ability （Q^2 〉 0.5）.

Relationship of quantitative structure and pharmacokinetics in fluoroquinolone antibacterials

AIM： To study the relationship between quantitative structure and pharmacokinetics （QSPkR） of fluorocluinolone antibacterials.METHODS： The pharmacokinetic （PK） parameters of oral fluoroquinolones were collected from the literature. These pharmacokinetic data were averaged, 19 compounds were used as the training set, and 3 served as the test set. Genetic function approximation （GFA） module of Cerius2 software was used in QSPkR analysis.RESULTS： A small volume and large polarizability and surface area of substituents at C-7 contribute to a large area under the curve （AUC） for fluoroquinolones. Large polarizability and small volume of substituents at N-1 contribute to a long half life elimination.CONCLUSION： QSPkR models can contribute to some fluoroquinolones antibacterials with excellent pharmacokinetic properties.

Genotoxicity of substituted nitrobenzenes and the quantitative structure－activity relationship

The genotoxicity of 22 substituted nitrobenzenes were evaluated by the chromosome aberrations test in in vitro human peripheral lymphocytes.18 of 22 compounds exhibit genotoxic activities.Quantitative structure-activity relationship model was established to correlate the genotoxicity of substituted nitrobenzenes with the characteristics of the substituents on benzene ring.

Quantitative Structure-activity Relationship Studies on the Antioxidant Activity and Gap Junctional Communication of Carotenoids

The antioxidant and gap junctional communication（GJC） activities of carotenoids are known to be the two main anticancer mechanisms.Quantitative structure-activity relationship（QSAR） models of the two activities were developed using stepwise regression and multilayer perceptron neural network based on the calculated descriptors of quantum chemistry.The results showed that the significant molecular descriptor related to the antioxidant activity of carotenoids was the HOMO-LUMO energy gap（EHL） and the molecular descriptor related to the GJC was the lowest unoccupied molecular orbital energy（ELUMO）.The two models of antioxidant activity both showed good predictive power,but the predictive power of the neural network QSAR model of antioxidant activity was better.In addition,the two GJC models have similar,moderate predictive power.The possible mechanisms of antioxidant activity and GJC of carotenoids were discussed.

Quantitative Structure-biodegradability Relationship Study about the Aerobic Biodegradation of Some Aromatic Compounds

10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships （QSBR） studies were performed by the multiple linear regression （MLR）, principal component regression （PCR） and back propagation artificial neural network （BP-ANN）, respectively. The root mean square error （RMSE） of the training and validation sets of the BP-ANN model are 0.1363 and 0.0244, the mean absolute percentage errors （MAPE） are 0.1638 and 0.0326, the squared correlation coefficients （R^2） are 0.9853 and 0.9996, respectively. The results show that the BP-ANN model achieved a better prediction result than those of MLR and PCR. In addition, some insights into the structural factors affecting the aerobic biodegradation mechanism were discussed in detail.

Studies on a Novel Characteristic Atom-pair Holographic Code Applied to Quantitative Structure-chromatographic Retention Relationship of Organic Compounds

6 Atomic fragment types of organic compound have been defined, and the multilevel atom-pair frequency matrix has been constructed according to the occurrence number in pairs of atomic fragments with different bond lengths in the molecule. On the basis of them, a novel molecular coding technique： characteristic atom-pair holographic code （CAHC）, is obtained. To some extent, this method exhibits a large number of benefits at the same time. For example, it can calculate 2D molecular topological descriptor easily, operate without difficulty and possess definite physicochemical meaning of 3D molecular structural characterization methods, and may fetch the complicated information of molecule, etc. Therefore, it is appropriate for the study on quantitative structure-property/activity relationship （QSPR/QSAR） of medicines and biological molecules. We attempt in this paper to utilize the method of CAHC to the quantitative prediction of reversed-phase liquid chromatogram （RPLC） retention data of 33 purine derivatives and 24 steroids. The fitting multiple correlation coefficient R2, cross-validated multiple correlation coefficient Q2 and predicted ability Q^2 pred over test set＇s samples of obtained partial least-square （PLS） regression model are respectively 0.990, 0.893 and 0.977, 0.897, 0.941.

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.

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.