Hai-Ling Liu

Combined photobacterium toxicity of herbicide mixtures containing one insecticide.

To test whether the dose-addition (DA) model can predict the combined toxicity of the mixtures of herbicides that coexisted with insecticide(s), we selected five herbicides (simetryn, prometon, bromacil, velpar, and diquat) and one organophosphorus insecticide (dichlorvos) as the test components. The inhibition toxicities of the six pesticides as well as those of their mixtures to Vibrio qinghaiensis sp.-Q67 were determined by using the microplate toxicity test procedure. The dose-response curves (DRCs) between the observed inhibition toxicities and the doses of the pesticides or the mixtures were modeled by using the nonlinear least square fitting. It was shown that all dose-response relationships were effectively described by the Weibull function. To fully explore the combined toxicities of mixtures including various concentration compositions, we designed three equivalent-effect concentration ratio (EECR) mixtures and six uniform design concentration ratio (UDCR) mixtures. The combined toxicity of a mixture is identified by inspecting whether the DRC predicted by the dose addition (DA) or independent action (IA) locates in the 95% confidence interval of the DRC of the mixture. Furthermore, the possible reason for the three mixtures to depart from the DA action was the very high concentration ratio of diquat in the mixtures.

Evaluation on the toxicity of ionic liquid mixture with antagonism and synergism to Vibrio qinghaiensis sp.-Q67

Ionic liquids (ILs) are a fascinating group of new chemicals with the potential to replace the classical volatile organic solvents, stimulating many applications in chemical industry. In case ILs are released to the environment, possible combined toxicity should be taken into account and it is, however, often neglected up to now. In this paper, therefore, the concentration-response curves (CRCs) of four groups of IL mixtures with various mixture ratios to Vibrio qinghaiensis sp.-Q67 were determined using the microplate toxicity analysis and were compared to the CRCs predicted by an additive reference model, the concentration addition (CA) or independent action (IA), to identify the toxicity interaction. It is showed that most of the IL mixture rays displayed the classical addition while the remaining rays exhibited antagonism or synergism. Moreover, it is found that the pEC₅₀ values of the mixture rays exhibiting antagonism or synergism are well correlated with the mixture ratio of a certain IL therein.

Predicting hormetic effects of ionic liquid mixtures on luciferase activity using the concentration addition model.

The concept of hormesis has generated considerable interest within the environmental and toxicological communities over the past decades. However, toxicological evaluation and prediction of hormesis in mixtures are challenging and only just unfolding. The hormetic effects of ten ionic liquids (ILs), singly and in mixtures in the ratios of their individual EC50, EC10, EC0, and ECm (maximal stimulatory effect concentration), on luciferase luminescence were determined by using microplate toxicity analysis. There was good agreement between the effects observed and predicted by concentration addition (CA) for all four mixtures. This evidence supports the use of CA model as a default approach for assessing the combined effect of chemicals at the molecular level. Focusing on the selected points of the concentration-response curves (CRCs) of mixtures, the mixtures of IL chemicals mixed at concentrations that individually showed stimulatory effects could produce inhibitory or no effects, and the mixture of IL chemicals mixed at concentrations that individually showed no effects could produce significant inhibitory effect. The three interesting phenomena in mixture hormesis may have important implications for current risk assessment practices.

Support vector regression and least squares support vector regression for hormetic dose–response curves fitting

Accurate description of hormetic dose-response curves (DRC) is a key step for the determination of the efficacy and hazards of the pollutants with the hormetic phenomenon. This study tries to use support vector regression (SVR) and least squares support vector regression (LS-SVR) to address the problem of curve fitting existing in hormesis. The SVR and LS-SVR, which are entirely different from the non-linear fitting methods used to describe hormetic effects based on large sample, are at present only optimum methods based on small sample often encountered in the experimental toxicology. The tuning parameters (C and p1 for SVR, gam and sig2 for LS-SVR) determining SVR and LS-SVR models were obtained by both the internal and external validation of the models. The internal validation was performed by using leave-one-out (LOO) cross-validation and the external validation was performed by splitting the whole data set (12 data points) into the same size (six data points) of training set and test set. The results show that SVR and LS-SVR can accurately describe not only for the hermetic J-shaped DRC of seven water-soluble organic solvents consisting of acetonitrile, methanol, ethanol, acetone, ether, tetrahydrofuran, and isopropanol, but also for the classical sigmoid DRC of six pesticides including simetryn, prometon, bromacil, velpar, diquat-dibromide monohydrate, and dichlorvos.

A novel direct equipartition ray design (EquRay) procedure for toxicity interaction between ionic liquid and dichlorvos

Background, aim and scopePollutants always co-exist in the environment. Determining and characterizing the interaction among chemicals is an important issue. Experimental designs (ED) play an important role in evaluating the interactions. The main aim of our study is to provide the test and analysis of the toxicity interaction with a novel ED method.Materials and methodsA novel direct equipartition ray design (EquRay) procedure was proposed to effectively and systematically determine the toxicities of binary mixtures on Vibrio qinghaiensis sp.-Q67. Here, one component is ionic liquid, 1-butyl-2,3-dimethylimidazolium chloride (IL1), 1-butylpyridinium bromide (IL2) or N-hexylpyridinium bis(trifluoromethylsulfonyl)imide (IL3), and another is dichlorvos (DIC). The toxicity interaction was evaluated by comparing experiment and additive model together with three-dimension deviation response surface (DRS) analysis.ResultSelecting CA as a reference model, the binary mixtures exerted less than additive (antagonism). Most of the deviations occurred in the centre portion of the DRS where the dCA (deviation from CA) values are between −15% and −26% for IL1–DIC and IL2–DIC mixtures and −10% and −15% for IL3 and DIC. Selecting IA as a additive model, IL1–DIC and IL2–DIC mixtures exhibited less than additive (antagonism) while IL3–DIC displayed an addition action and the absolute values of dIAs (deviation from IA) were less than 10%.ConclusionA novel EquRay procedure was developed in this study and the EquRay can provide us with the information about the toxicity interaction between binary mixture components (such as DIC and IL) in different concentration regions across different mixture ratios.

The time-dependent hormetic effects of 1-alkyl-3-methylimidazolium chloride and their mixtures on Vibrio qinghaiensis sp. -Q67.

The hormetic effects of ionic liquids (ILs) were paid more ecological attentions. However, the time-dependent hormetic effects of ILs and their mixtures remained to be studied. In this paper, the time-dependent toxicities of five single ILs, 1-ethyl-, 1-butyl-, 1-hexyl-, 1-octyl-, and 1-dodecyl-3-methylimidazolium chlorides (named as [C2mim]Cl, [C4mim]Cl, [C6mim]Cl, [C8mim]Cl, and [C12mim]Cl, respectively), and their five-component mixtures to Vibrio qinghaiensis sp.-Q67 were determined at five exposure time points. For single ILs, [C2mim]Cl displayed significant hormetic effects at 2, 4, 8, and 12h; and [C4mim]Cl exhibited significant hormetic effects at 4, 8 and 12h; while [C6mim]Cl, [C8mim]Cl and [C12mim]Cl have not significant hormetic effects. At the same time point, the longer the side chain is, the larger the inhibition at high concentration is, and the less the stimulation at low concentration is. Meanwhile, the maximum stimulation effects were found between 4 and 8h. All six IL mixtures designed by uniform design ray showed significant hormetic effects at 8 and 12h. By means of the variable selection and modeling method based on the prediction (VSMP), it was found that the higher the concentration of [C2mim]Cl is, the stronger the mixture hormetic effect is and the higher the concentration of [C12mim]Cl is, the weaker the hormetic effect is.

Modeling non-monotonic dose-response relationships: model evaluation and hormetic quantities exploration.

Non-monotonic (biphasic) dose-response relationships, known as hormetic relationships, have been observed across multiple experimental systems. Several models were proposed to describe non-monotonic relationships. However, few studies provided comprehensive description of hermetic quantities and their potential application. In this study, five biphasic models were used to fit five hormetic datasets from three different experimental systems of our lab. The bisection algorithm based on individual monotone functions was proposed to calculate arbitrary hormetic quantities instead of traditional methods (e.g., model reparameterization) which need complex mathematical manipulation. Results showed that all the five biphasic models could describe those datasets fairly well with coefficient of determination ( R(2) adj) greater than 0.95 and root mean square error (RMSE) smaller than 0.10. The best-fit model could be selected based on EC(R10), RMSE, and a supplemental criterion of Akaike information criterion (AIC). Hormetic quantities that trigger 10% stimulation at the left (EC(L10)) and right (EC(R10)) side of stimulatory peak were calculated and emphasized for their implication in hormesis exploration for the first time. Furthermore, the EC(L10), proposed as an alarm threshold for hormesis, was expected to be useful in risk assessment of environmental chemicals. This study lays a foundation in the quantitative description of the low dose hormetic effect and the investigation of hormesis in environmental risk assessment.

Remarkable hormesis induced by 1-ethyl-3-methyl imidazolium tetrafluoroborate on Vibrio qinghaiensis sp.-Q67

Room-temperature ionic liquids (ILs) are a class of the salts in the liquid state. ILs typically consist of a bulky organic cation in combination with various anions and are designed to replace traditional volatile organic solvents in industry. However, it is not neglected that the possible release of ionic liquids into aquatic environments may lead to water pollution. We systematically investigated the effect of ILs on the luminescence of Vibrio qinghaiensis sp.-Q67 (Q67). When the ionic liquid, 1-ethyl-3-methyl imidazolium tetrafluoroborate ([emim]BF(4)), was exposed on Q67, the luminescence of Q67 was stimulated after 12 h and the maximal stimulatory amplitude (Emin) was surprisingly about 1000%. In comparison with the generally reported stimulating amplitude of about 30-60% such great stimulating effect induced by [emim]BF(4) was indeed unexpected. The main aim of this study was to systematically investigate the remarkable hormesis induced by [emim]BF(4). Results showed that there was good reproducibility on the observation of hormesis induced by [emim]BF(4) and such obvious hormesis had a close relation to the test organisms and exposure time. In addition, we confirmed that this surprising phenomenon did not only depend on range and spacing of exposure concentration of ILs but also on their structure components. Only joint influence of both anion, BF(4)(-) and ethyl side chain of cation made [emim]BF(4) induce such remarkable hormesis on Q67. The experimental findings of hormesis induced by [emim]BF(4) provided a good case for the hormesis database.

Significant contributions of ionic liquids containing tetrafluoroborate and trifluoromethanesulfonate to antagonisms and synergisms in multi-component mixtures

Recent toxicity studies on ionic liquids (ILs) are challenging their postulation as green solvents. Previous reports on mixtures containing ILs make it urgent to reveal the responsible components for the toxicity interactions. For that purpose, eight ILs, four consisting of 1-ethyl-3-methylimidazolium ([emim]) and the others of 1-butyl-3-methylimidazolium ([bmim]), were selected as mixture components. The concentrations of eight ILs in mixtures were set up by the uniform design. The inhibition toxicities of single ILs and mixtures to Vibrio qinghaiensis sp.-Q67 were determined by microplate toxicity analysis. Combined toxicity was evaluated by the difference between the effects observed and predicted by the concentration addition model. Using the variable selection and modeling method based on the prediction (VSMP), it was found that the antagonism/synergism induced by the mixtures of eight ILs was related to [emim]BF(4)/[emim]CF(3)SO(3). To further illustrate the toxicity interactions, eight ILs were split into two mixture groups, one containing four [emim]-based ILs and the other four [bmim]-based ILs. The [emim]-group exhibited synergism while [bmim]-group resulted in antagonism. It was interesting that both the synergism and antagonism well related to the concentration ratio of ILs with BF(4)(-). When ILs with BF(4)(-) were deleted from corresponding mixtures, the toxicity interactions (synergism/antagonism) disappeared.

Comparative multiple quantitative structure-retention relationships modeling of gas chromatographic retention time of essential oils using multiple linear regression, principal component regression, and partial least squares techniques.

Quantitative structure-retention relationships (QSRR) models were built for a data set consisting of 96 essential oils and used to predict their gas chromatographic (GC) retention times (t(R)). Multiple linear regression (MLR), principal component regression (PCR), and partial least squares (PLS) have been applied to build different QSRR models by using 13 nonzero E-state indexes and 56 descriptors calculated from TSAR software. The three chemometric methods (MLR, PCR, and PLS) for evaluation of GC t(R) values of essential oils have been compared. The best model based on the whole data set derived from MLR model (model M2) appears to be the best predictive power (r(2)=0.9689 and q(2)=0.9631) for this data set. The whole data set was splitted into a training set consisting of 72 compounds and a test set consisting of 24 compounds. The model based on the training set derived from MLR offered the highest r(2) of 0.9756 and q(2) of 0.9693. The best model base on the training set obtained from PLS not only showed a good internal predictive power (r(2)=0.9703 and q(2)=0.9633) but also offered the highest external predictive power (R(2)=0.9588 and q(2)(ext)=0.9572). The results showed that two E-state indexes (sssCH and sOH) and five molecular connective indices ((1)chi(B), (2)chi(p), (3)chi(C), (4)chi(C), and (6)chi(p)) closely relate to the GC t(R) values of essential oils. The applicability domain of the QSRR models were defined by control leverage values (h*) and the models can be used to predict the unknown compounds falling in this domain.