Publications by authors named "Shijin Ren"

Gastrointestinal (GI) symptoms, the primary acute effect of the essential micronutrient copper, paradoxically occur at lower exposure levels than hepatotoxicity, the primary chronic effect. We developed a remedial action criterion (RAC) for copper to protect against GI symptoms, which primarily relate to the stomach copper concentration, and subside within an hour. Using Monte Carlo methods, we generated a distribution of RACs protective against GI symptoms for a 1 h exposure (hourly RACs) based on soil ingestion rate, volume of liquid and food in the stomach, and bioaccessibility.

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A three-stage system was developed to automate a batchwise toxicity testing protocol designed for assessing wastewater toxicity to activated sludge. The three-stage system used the luminescent bacterium Shkl. The three stages were cell storage, cell activation, and continuous toxicity testing.

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The development of a bioluminescent sensor organism (Shk1) that was created for assessing wastewater toxicity was reported several years ago. In order to establish a test battery to better characterize wastewater toxicity, additional luminescent sensor organisms were later created. The present study focused on one promising candidate (PM6), a Pseudomonas spp.

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Reducing bioassay variability by identifying sources of variation and controlling important parameters in assay protocol was demonstrated in this study. The variability of a bioassay based on a luminescent bacterium was examined as an example. This assay involved the growth of cells, storage at a low temperature, activation, and exposure to a test sample, and the assay response was bioluminescence inhibition.

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Toxicants in municipal sewage treatment plant (STP) influent wastewater may inhibit the biological activity of the activated sludge and cause treatment plant process upsets. Such process upsets may be avoided if influent wastewater is monitored for toxicity and protective actions are taken when toxicity is detected. A comprehensive review of the methods that can be used for assessing wastewater toxicity to biological treatment systems was conducted several years ago and the resultant report was published in 2000 by Water Environment Research Foundation (WERF).

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Two-level factorial experiments were employed in this study for understanding and predicting the toxicity of binary and ternary metal mixtures. Toxicity of metal mixtures with concentrations between the respective EC10 and EC80 values was experimentally measured. Models were fit to the experimental data and the resultant models were of high quality as reflected by R2 (coefficient of determination).

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Statistical models have long been used for reliability analysis and risk assessment. In the present study, an accelerated life-test model was used to analyze a set of dose-time-response data obtained with the terrestrial isopod Porcellio scaber. Survival data were experimentally obtained by exposing P.

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The use of regression methods for classifying and predicting the mechanisms of toxic action of phenols was investigated in this study. Multiresponse regression was conducted using a total of six linear and nonlinear regression methods: simple linear regression (LinReg), logistic regression (LogReg), generalized additive model (GAM), locally weighted regression scatter plot smoothing (LOWESS), multivariate adaptive regression splines (MARS), and projection pursuit regression (PPR). A database containing phenols acting by four mechanisms (polar narcosis, weak acid respiratory uncoupling, proelectrophilicity, and soft electrophilicity) was used to assess the performances of the six regression methods in the multiresponse regression approach.

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In ecotoxicology, mechanism-based quantitative structure-activity relationships (QSARs) are usually developed with higher quality than QSARs without regard to toxicity mechanism. Correctly determining the mechanism of a compound, which is not always easy, is required to use mechanism-based QSARs for toxicity prediction. The mechanism determination step may introduce extra errors in addition to the intrinsic prediction errors of mechanism-based QSARs, thus compromising these QSARs' performance compared with QSARs regardless of mechanism.

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Response surface models based on multiple linear regression had previously been developed for the toxicity of aromatic chemicals to Tetrahymena pyriformis. However, a nonlinear relationship between toxicity and one of the molecular descriptors in the response surface model was observed. In this study, response surface models were established using six nonlinear modeling methods to handle the nonlinearity exhibited in the aromatic chemicals data set.

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In this study, the use of decision tree in classifying and predicting the aquatic toxicity mechanisms of phenols was investigated. Four mechanisms including polar narcosis, respiratory uncoupling, pro-electrophilicity, and soft electrophilicity were involved. Using molecular descriptors as splitting variables, a three level decision tree with six terminal nodes was obtained.

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The most successful quantitative structure-activity relationships have been developed by separating compounds by their mechanisms of toxic action (MOAs). However, to correctly determine the MOA of a compound is often not easy. We investigated the usefulness of discriminant analysis and logistic regression in determining MOAs.

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Heavy metals are known to be inhibitory and toxic to the activated-sludge microbial community in biological wastewater treatment plants. Toxicity screening of aqueous mixtures of these heavy metal ions in plant influent could use both chemical and biological methods. As a biological method, luminescent bacterial bioreporters offer the advantages of a simple test procedure and rapid response.

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In aquatic toxicity testing, no single test species is sensitive to all toxicants. Therefore, test batteries consisting of several individual assays are becoming more common. The organisms in a test battery should be representative of the entire system of interest.

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Luminescent bacterium Shk1 was created for the purpose of testing and screening the toxicity of activated sludge wastewater treatment plant influent to avoid toxic shock to the wastewater treatment plant microorganisms. The toxicity of a number of organic compounds was tested using an assay employing Shk1. Because these compounds exhibit toxicity by mechanisms of both polar and nonpolar narcosis, their toxicity cannot be properly modeled together using a quantitative structure-activity relationship model based on the logarithm of the octanol-water partition coefficient (log K(ow)).

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Toxicity assays based on bioluminescent bacteria have several advantages including a quick response and an easily measured signal. The Shk1 assay is a procedure for wastewater toxicity testing based on the bioluminescent bacterium Shk1. Using the Shk1 assay, the toxicity of 98 organic chemicals were measured and EC50 values were obtained.

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In this study, the use of solute descriptors (the McGowan's characteristic volume V(X), the excess molar refraction R, the dipolarity/dipolarizability pi(H), the effective or summation hydrogen-bond acidity summation operatoralpha(H) and the effective or summation hydrogen-bond basicity summation operatorbeta(H)) in classifying and predicting the non-polar, polar, and ester narcosis toxicity mechanisms for organic compounds was investigated. Discriminant analysis was performed and the significant discriminating variables were found to be R, pi(H), sigma alpha(H), and (sigma beta (H))(2), the latter of which was created to aid the mechanism classifications. Cross-validation of the non-linear discriminant functions showed a small total error rate of approximately 5% which was reduced to approximately 2% when seven compounds with uncertain a priori mechanism designations were removed.

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The most successful quantitative structure-activity relationships (QSARs) have been developed by separating toxicants by their mechanisms of action (MOAs). However, since the activity of a chemical compound on an organism is dependent upon several physical, chemical and biological factors, among which interactions may also exist, the MOA of a compound is not easily determined. In this study, the use of discriminant analysis and logistic regression in distinguishing between narcotic and reactive compounds was investigated.

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