Correlation of inducible nitric oxide synthase (iNOS) inhibition with TNF-α, caspase-1, FasL and TLR-3 in pathogenesis of rabies in mouse model

The role of inflammatory cytokines such as interleukin-1α/β (IL-1α/β), IL-6, IL-10, tumour necrosis factor-alpha (TNF-α), interferons, nitric oxide (NO) and inducible nitric oxide synthase (iNOS) in pathogenesis of rabies is being actively pursued. Presently, levels of certain immune molecules in pathogenesis of rabies in mice have been investigated. CVS strain of rabies infection resulted in early increase in iNOS, TNF-α, caspase-1, Fas ligand (FasL) and toll-like receptor-3 (TLR-3) mRNA levels in brain, and nitric oxide levels in serum. The severity of clinical signs and microscopic lesions largely correlated with NO levels. Aminoguanidine (AG; iNOS inhibitor) decreased NO production with delay in development of clinical signs and increase in survival time. Prolonged survival time correlated with reduced viral load evident by real-time PCR, reduced fluorescent signals of rabies antigen in brain and reduced immunohistochemistry signals in neuronal cytoplasm. These parameters suggested that nitric oxide did influence the rabies virus replication. Inhibition of iNOS by AG administration led to decreased expression of TNF-α, caspase-1, FasL and TLR-3 mRNA levels suggesting that increase in NO levels in rabies virus infection possibly contributed to development of disease through inflammation, apoptosis and immune-evasive mechanisms.

Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches

In this study, we established nonlinear quantitative-structure toxicity relationship (QSTR) models for predicting the toxicities of chemical pesticides in multiple aquatic test species following the OECD (Organization for Economic Cooperation and Development) guidelines. The decision tree forest (DTF) and decision tree boost (DTB) based QSTR models were constructed using a pesticides toxicity dataset in Selenastrum capricornutum and a set of six descriptors. Other six toxicity data sets were used for external validation of the constructed QSTRs. Global QSTR models were also constructed using the combined dataset of all the seven species. The diversity in chemical structures and nonlinearity in the data were evaluated. Model validation was performed deriving several statistical coefficients for the test data and the prediction and generalization abilities of the QSTRs were evaluated. Both the QSTR models identified WPSA1 (weighted charged partial positive surface area) as the most influential descriptor. The DTF and DTB QSTRs performed relatively better than the single decision tree (SDT) and support vector machines (SVM) models used as a benchmark here and yielded R(2) of 0.886 and 0.964 between the measured and predicted toxicity values in the complete dataset (S. capricornutum). The QSTR models applied to six other aquatic species toxicity data yielded R(2) of >0.92 (DTF) and >0.97 (DTB), respectively. The prediction accuracies of the global models were comparable with those of the S. capricornutum models. The results suggest for the appropriateness of the developed QSTR models to reliably predict the aquatic toxicity of chemicals and can be used for regulatory purpose.

Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches

Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The proposed AI models can be useful tools in screening the chemicals for their binding affinities toward carbon for their safe management.

Redox signalling to nuclear regulatory proteins by reactive oxygen species contributes to oestrogen-induced growth of breast cancer cells

Background:

17β-Oestradiol (E2)-induced reactive oxygen species (ROS) have been implicated in regulating the growth of breast cancer cells. However, the underlying mechanism of this is not clear. Here we show how ROS through a novel redox signalling pathway involving nuclear respiratory factor-1 (NRF-1) and p27 contribute to E2-induced growth of MCF-7 breast cancer cells.

Methods:

Chromatin immunoprecipitation, qPCR, mass spectrometry, redox western blot, colony formation, cell proliferation, ROS assay, and immunofluorescence microscopy were used to study the role of NRF-1.

Results:

The major novel finding of this study is the demonstration of oxidative modification of phosphatases PTEN and CDC25A by E2-generated ROS along with the subsequent activation of AKT and ERK pathways that culminated in the activation of NRF-1 leading to the upregulation of cell cycle genes. 17β-Oestradiol-induced ROS by influencing nuclear proteins p27 and Jab1 also contributed to the growth of MCF-7 cells.

Conclusions:

Taken together, our results present evidence in the support of E2-induced ROS-mediated AKT signalling leading to the activation of NRF-1-regulated cell cycle genes as well as the impairment of p27 activity, which is presumably necessary for the growth of MCF-7 cells. These observations are important because they provide a new paradigm by which oestrogen may contribute to the growth of breast cancer.

In vitro validation of self designed "universal human Influenza A siRNA"

The genomic variability of Influenza A virus (IAV) makes it difficult for the existing vaccines or anti-influenza drugs to control. The siRNA targeting viral gene induces RNAi mechanism in the host and silent the gene by cleaving mRNA. In this study, we developed an universal siRNA and validated its efficiency in vitro. The siRNA was designed rationally, targeting the most conserved region (delineated with the help of multiple sequence alignment) of M gene of IAV strains. Three level screening method was adopted, and the most efficient one was selected on the basis of its unique position in the conserved region. The siRNA efficacy was confirmed in vitro with the Madin Darby Canine Kidney (MDCK) cell line for IAV propagation using two clinical isolates i.e., Influenza A/H3N2 and Influenza A/pdmH1N1. Of the total 168 strains worldwide and 33 strains from India, 97 bp long (position 137-233) conserved region was identified. The longest ORF of matrix gene was targeted by the selected siRNA, which showed 73.6% inhibition in replication of Influenza A/pdmH1N1 and 62.1% inhibition in replication of Influenza A/H3N2 at 48 h post infection on MDCK cell line. This study provides a basis for the development of siRNA which can be used as universal anti-IAV therapeutic agent.

Nonlinear QSAR modeling for predicting cytotoxicity of ionic liquids in leukemia rat cell line: an aid to green chemicals designing

Safety assessment and designing of safer ionic liquids (ILs) are among the priorities of the chemists and toxicologists today. Computational approaches have been considered as appropriate methods for prior safety assessment of chemicals and tools to aid in structural designing. The present study is an attempt to investigate the chemical attributes of a wide variety of ILs towards their cytotoxicity in leukemia rat cell line IPC-81 through the development of nonlinear quantitative structure-activity relationship (QSAR) models in the light of the OECD principles for QSAR development. Here, the cascade correlation network (CCN), probabilistic neural network (PNN), and generalized regression neural networks (GRNN) QSAR models were established for the discrimination of ILs in four categories of cytotoxicity and their end-point prediction using few simple descriptors. The diversity and nonlinearity of the considered dataset were evaluated through computing the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data. The predictive power of these models was established through a variety of stringent parameters recommended in QSAR literature. The classification QSARs rendered the accuracy of >86%, and the regression models yielded correlation (R(2)) of >0.90 in test data. The developed QSAR models exhibited high statistical confidence and identified the structural elements of the ILs responsible for their cytotoxicity and, hence, could be useful tools in structural designing of safer and green ILs.

Predicting the hazardous dose of industrial chemicals in warm-blooded species using machine learning-based modelling approaches

The hazardous dose of a chemical (HD50) is an emerging and acceptable test statistic for the safety/risk assessment of chemicals. Since it is derived using the experimental toxicity values of the chemical in several test species, it is highly cumbersome, time and resource intensive. In this study, three machine learning-based QSARs were established for predicting the HD50 of chemicals in warm-blooded species following the OECD guidelines. A data set comprising HD50 values of 957 chemicals was used to develop SDT, DTF and DTB QSAR models. The diversity in chemical structures and nonlinearity in the data were verified. Several validation coefficients were derived to test the predictive and generalization abilities of the constructed QSARs. The chi-path descriptors were identified as the most influential in three QSARs. The DTF and DTB performed relatively better than SDT model and yielded r(2) values of 0.928 and 0.959 between the measured and predicted HD50 values in the complete data set. Substructure alerts responsible for the toxicity of the chemicals were identified. The results suggest the appropriateness of the developed QSARs for reliably predicting the HD50 values of chemicals, and they can be used for screening of new chemicals for their safety/risk assessment for regulatory purposes.

Estimating sensory irritation potency of volatile organic chemicals using QSARs based on decision tree methods for regulatory purpose

Volatile organic compounds (VOCs) are among the priority atmospheric pollutants that have high indoor and outdoor exposure potential. The toxicity assessment of VOCs to living ecosystems has received considerable attention in recent years. Development of computational methods for safety assessment of chemicals has been advocated by various regulatory agencies. The paper proposes robust and reliable quantitative structure-activity relationships (QSARs) for estimating the sensory irritation potency and screening of the VOCs. Here, decision tree (DT) based classification and regression QSARs models, such as single DT, decision tree forest (DTF), and decision tree boost (DTB) were developed using the sensory irritation data on VOCs in mice following the OECD principles. Structural diversity and nonlinearity in the data were evaluated through the Euclidean distance and Brock-Dechert-Scheinkman statistics. The constructed QSAR models were validated with external test data and the predictive performance of these models was established through a set of coefficients recommended in QSAR literature. The performance of all three classification and regression QSAR models was satisfactory, but DTF and DTB performed relatively better. The classification and regression QSAR models (DTF, DTB) rendered classification accuracies of 98.59 and 100 %, and yielded correlations (R(2)) of 0.950 and 0.971, respectively in complete data. The lipoaffinity index and SwHBa were identified as the most influential descriptors in proposed QSARs. The developed QSARs performed better than the previous studies. The developed models exhibited high statistical confidence and identified the structural properties of the VOCs responsible for their sensory irritation, and hence could be useful tools in screening of chemicals for regulatory purpose.

Role and users’ approach to social networking sites (SNSs): a study of universities of North India

Purpose

– The purpose of this paper is to evaluate and assess the awareness and extent of the use of social networking sites (SNSs) by the students and research scholars of universities of North India.

Design/methodology/approach

– The study is a questionnaire-based survey on the usage of SNSs among the students and research scholars of the universities of North India. The data of the study were collected through questionnaires, which were personally distributed to the identified population, i.e. undergraduate students, postgraduate students and research scholars, by the authors. The survey was based on a sample of 610 questionnaires; of which, 486 questionnaires were received, having a response rate of 79.67 per cent.

Findings

– The study showed that all the respondents were found to be aware of and making use of such applications in their academic affairs. It was revealed from the study that Facebook is the most popular SNS by all categories of respondents. To determine the purpose of SNSs, it emerged that SNSs are mostly used for entertainment and communication. The study also found that the majority of respondents were aware about the security aspects of SNSs. It signifies that excessive time consumption and fear of misusing personal information were the major hurdles in the way of accessing SNSs.

Research limitations/implications

– The study covers the students and research scholars of select universities of North India. It also signifies the use of SNSs in their research and academic environment.

Originality/value

– The paper provides reliable and authentic data. The study is worth, justifiable and enlightens the salient findings on the topic, which will be very useful for researchers in this area.

Mapping of yellow mosaic virus (YMV) resistance in soybean (Glycine max L. Merr.) through association mapping approach

Yellow Mosaic Virus (YMV) is a serious disease of soybean. Resistance to YMV was mapped in 180 soybean genotypes through association mapping approach using 121 simple sequence repeats (SSR) and four resistance gene analogue (RGA)-based markers. The association mapping population (AMP) (96 genotypes) and confirmation population (CP) (84 genotypes) was tested for resistance to YMV at hot-spot consecutively for 3 years (2007-2009). The genotypes exhibited significant variability for YMV resistance (P < 0.01). Molecular genotyping and population structure analysis with 'admixture' co-ancestry model detected seven optimal sub-populations in the AMP. Linkage disequilibrium (LD) between the markers extended up to 35 and 10 cM with r2 > 0.15, and >0.25, respectively. The 4 RGA-based markers showed no association with YMV resistance. Two SSR markers, Satt301 and GMHSP179 on chromosome 17 were found to be in significant LD with YMV resistance. Contingency Chi-square test confirmed the association (P < 0.01) and the utility of the markers was validated in the CP. It would pave the way for marker assisted selection for YMV resistance in soybean. This is the first report of its kind in soybean.

QSTR modeling for predicting aquatic toxicity of pharmacological active compounds in multiple test species for regulatory purpose

High concentrations of pharmacological active compounds (PACs) detected in global drinking water resources and their toxicological implications in aquatic life has become a matter of concern compelling for the development of reliable QSTRs (qualitative/quantitative structure-toxicity relationships) for their risk assessment. Robust QSTRs, such as decision treeboost (DTB) and decision tree forest (DTF) models implementing stochastic gradient boosting and bagging algorithms were established by experimental toxicity data of structurally diverse PACs in daphnia using molecular descriptors for predicting toxicity of new untested compounds in multiple test species. Developed models were rigorously validated using OECD recommended internal and external validation procedures and predictive power tested with external data of different trophic level test species (algae and fish). Classification QSTRs (DTB, DTF) rendered accuracy of 98.73% and 97.47%, respectively in daphnia and 84.38%, 85.94% (algae), 78.46% and 79.23% (fish). On the other hand, the regression QSTRs (DTB, DTF) yielded squared correlation coefficient values of 0.831, 0.852 (daphnia), 0.534, 0.556 (algae) and 0.620, 0.637 (fish). QSTRs developed in this study passed the OECD validation criteria and performed better than reported earlier for predicting toxicity of PACs, and can be used for screening the new untested compounds for regulatory purpose.

Qualitative and quantitative structure-activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals

In this study, structure-activity relationship (SAR) models have been established for qualitative and quantitative prediction of the blood-brain barrier (BBB) permeability of chemicals. The structural diversity of the chemicals and nonlinear structure in the data were tested. The predictive and generalization ability of the developed SAR models were tested through internal and external validation procedures. In complete data, the QSAR models rendered ternary classification accuracy of >98.15%, while the quantitative SAR models yielded correlation (r(2)) of >0.926 between the measured and the predicted BBB permeability values with the mean squared error (MSE) <0.045. The proposed models were also applied to an external new in vitro data and yielded classification accuracy of >82.7% and r(2) > 0.905 (MSE < 0.019). The sensitivity analysis revealed that topological polar surface area (TPSA) has the highest effect in qualitative and quantitative models for predicting the BBB permeability of chemicals. Moreover, these models showed predictive performance superior to those reported earlier in the literature. This demonstrates the appropriateness of the developed SAR models to reliably predict the BBB permeability of new chemicals, which can be used for initial screening of the molecules in the drug development process.

Effect of yoga regimen on lung functions including diffusion capacity in coronary artery disease patients: A randomized controlled study

BACKGROUND

Lung functions are found to be impaired in coronary artery disease (CAD), congestive heart failure, left ventricular dysfunction, and after cardiac surgery. Diffusion capacity progressively worsens as the severity of CAD increases due to reduction in lung tissue participating in gas exchange.

AIMS AND OBJECTIVES

Pranayama breathing exercises and yogic postures may play an impressive role in improving cardio-respiratory efficiency and facilitating gas diffusion at the alveolo-capillary membrane. This study was done to see the effect of yoga regimen on lung functions particularly diffusion capacity in CAD patients.

MATERIALS AND METHODS

A total of 80 stable CAD patients below 65 years of age of both sexes were selected and randomized into two groups of 40 each. Group I CAD patients were given yoga regimen for 3 months which consisted of yogic postures, pranayama breathing exercises, dietary modification, and holistic teaching along with their conventional medicine while Group II CAD patients were put only on conventional medicine. Lung functions including diffusion capacity were recorded thrice in both the groups: 0 day as baseline, 22(nd) day and on 90(th) day by using computerized MS medisoft Cardio-respiratory Instrument, HYP'AIR Compact model of cardio-respiratory testing machine was manufactured by P K Morgan, India. The recorded parameters were statistically analyzed by repeated measures ANOVA followed by Tukey's test in both the groups. Cardiovascular parameters were also compared before and after intervention in both the groups.

RESULTS

Statistically significant improvements were seen in slow vital capacity, forced vital capacity, peak expiratory flow rate, maximum voluntary ventilation, and diffusion factor/ transfer factor of lung for carbon monoxide after 3 months of yoga regimen in Group I. Forced expiratory volume in 1(st) sec (FEV1), and FEV1 % also showed a trend toward improvement although not statistically significant. HR, SBP and DBP also showed significant improvement in Group-I patients who followed yoga regimen.

CONCLUSIONS

Yoga regimen was found to improve lung functions and diffusion capacity in CAD patients besides improving cardiovascular functions. Thus, it can be used as a complimentary or adjunct therapy along with the conventional medicine for their treatment and rehabilitation.

Predicting aquatic toxicities of benzene derivatives in multiple test species using local, global and interspecies QSTR modeling approaches

A flow diagram showing QSTR modeling strategy for aquatic toxicity prediction of benzene derivatives in multiple test species.

Spore immobilization and its analytical performance for monitoring of aflatoxin M1 in milk

Immobilization of Bacillus megaterium spores on Eppendorf tubes through physical adsorption has been used in the detection of aflatoxin M1 (AFM1) in milk within real time of 45 ± 5 min using visual observation of changes in a chromogenic substrate. The appearance of a sky-blue colour indicates the absence of AFM1 in milk, whereas no colour change indicates the presence of AFM1 in milk at a 0.5 ppb Codex maximum residue limit. The working performance of the immobilized spores was shown to persist for up to 6 months. Further, spores immobilized on 96-well black microtitre plates by physical adsorption and by entrapment on sensor disk showed a reduction in detection sensitivity to 0.25 ppb within a time period of 20 ± 5 min by measuring fluorescence using a microbiological plate reader through the addition of milk and fluorogenic substrate. A high fluorescence ratio indicated more substrate hydrolysis due to spore-germination-mediated release of marker enzymes of spores in the absence of AFM1 in milk; however, low fluorescence ratios indicated the presence of AFM1 at 0.25 ppb. Immobilized spores on 96-well microtitre plates and sensor disks have shown better reproducibility after storage at 4 °C for 6 months. Chromogenic assay showed 1.38% false-negative and 2.77% false-positive results while fluorogenic assay showed 4.16% false-positive and 2.77% false-negative results when analysed for AFM1 using 72 milk samples containing raw, pasteurized, and dried milk. Immobilization of spores makes these chromogenic and fluorogenic assays portable, selective, cost-effective for real-time detection of AFM1 in milk at the dairy farm, reception dock, and manufacturing units of the dairy industry.

Occurrence of pharmaceuticals in urban wastewater of north Indian cities and risk assessment

Six pharmaceuticals of different categories, such as nonsteroidal anti-inflammatory drugs (ibuprofen, ketoprofen, naproxen, diclofenac), anti-epileptic (carbamazepine), and anti-microbial (trimethoprim), were investigated in wastewater of the urban areas of Ghaziabad and Lucknow, India. Samples were concentrated by solid phase extraction (SPE) and determined by high-performance liquid chromatography (HPLC) methods. The SPE-HPLC method was validated according to the International Conference on Harmonization guidelines. All the six drugs were detected in wastewater of Ghaziabad, whereas naproxen was not detected in Lucknow wastewater. Results suggest that levels of these detected drugs were relatively higher in Ghaziabad as compared to those in Lucknow, and diclofenac was the most frequently detected drug in both the study areas. Detection of these drugs in wastewater reflects the importance of wastewater inputs as a source of pharmaceuticals. In terms of the regional distribution of compounds in wastewater of two cities, higher spatial variations (coefficient of variation 112.90-459.44%) were found in the Lucknow wastewater due to poor water exchange ability. In contrast, lower spatial variation (162.38-303.77%) was observed in Ghaziabad. Statistical analysis results suggest that both data were highly skewed, and populations in two study areas were significantly different (p < 0.05). A risk assessment based on the calculated risk quotient (RQ) in six different bioassays (bacteria, duckweed, algae, daphnia, rotifers, and fish) showed that the nonsteroidal anti-inflammatory drugs (NSAIDs) posed high (RQ >1) risk to all the test species. The present study would contribute to the formulation of guidelines for regulation of such emerging pharmaceutical contaminants in the environment.

Viral aetiology of acute lower respiratory tract illness in hospitalised paediatric patients of a tertiary hospital: one year prospective study

CONTEXT

Acute lower respiratory tract infections (ALRI), ranked as the second leading cause of death are the primary cause of hospitalisation in children. Viruses are the most important causative agents of ALRI.

AIM

To study the viral aetiology of ALRI in children at a tertiary care hospital.

SETTING AND DESIGN

One year prospective observational study in a tertiary care hospital of King George's Medical University, Lucknow.

MATERIAL AND METHODS

Nasopharyngeal aspirate (NPA) was collected from children admitted with signs and symptoms of ALRI who were aged 0-14 years. Samples were transported to the laboratory at 4°C in viral transport media and processed for detection of respiratory syncytial virus (RSV) A and B, influenza virus A and B, adenovirus (ADV), human Boca virus (HBoV), human metapneumo virus (hMPV) and parainfluenzavirus 1, 2, 3 and 4 using mono/multiplex real-time polymerase chain reaction (RT-PCR). STATA was used for statistical analysis.

RESULTS

In one year, 188 NPAs were screened for respiratory viruses, of which 45.7% tested positive. RSV was most commonly detected with 21.3% positivity followed by measles virus (8.5%), influenza A virus (7.4%), ADV (5.3%), influenza B virus (1.6%), hMPV (1.1%) and HBoV (0.5%). Month wise maximum positivity was seen in December and January. Positivity rate of RSV was highest in children aged < 1 year, which decreased with increase in age, while positive rate of influenza virus increased with increasing age.

CONCLUSION

The occurrence of viral predominance in ALRI is highlighted.

Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.

Investigating hydrochemistry of groundwater in Indo-Gangetic alluvial plain using multivariate chemometric approaches

Groundwater hydrochemistry of an urban industrial region in Indo-Gangetic plains of north India was investigated. Groundwater samples were collected both from the industrial and non-industrial areas of Kanpur. The hydrochemical data were analyzed using various water quality indices and nonparametric statistical methods. Principal components analysis (PCA) was performed to identify the factors responsible for groundwater contamination. Ensemble learning-based decision treeboost (DTB) models were constructed to develop discriminating and regression functions to differentiate the groundwater hydrochemistry of the three different areas, to identify the responsible factors, and to predict the groundwater quality using selected measured variables. The results indicated non-normal distribution and wide variability of water quality variables in all the study areas, suggesting for nonhomogenous distribution of sources in the region. PCA results showed contaminants of industrial origin dominating in the region. DBT classification model identified pH, redox potential, total-Cr, and λ 254 as the discriminating variables in water quality of the three areas with the average accuracy of 99.51 % in complete data. The regression model predicted the groundwater chemical oxygen demand values exhibiting high correlation with measured values (0.962 in training; 0.918 in test) and the respective low root mean-squared error of 2.24 and 2.01 in training and test arrays. The statistical and chemometric approaches used here suggest that groundwater hydrochemistry differs in the three areas and is dominated by different variables. The proposed methods can be used as effective tools in groundwater management.

Multispecies QSAR modeling for predicting the aquatic toxicity of diverse organic chemicals for regulatory toxicology

The research aims to develop multispecies quantitative structure-activity relationships (QSARs) modeling tools capable of predicting the acute toxicity of diverse chemicals in various Organization for Economic Co-operation and Development (OECD) recommended test species of different trophic levels for regulatory toxicology. Accordingly, the ensemble learning (EL) approach based classification and regression QSAR models, such as decision treeboost (DTB) and decision tree forest (DTF) implementing stochastic gradient boosting and bagging algorithms were developed using the algae (P. subcapitata) experimental toxicity data for chemicals. The EL-QSAR models were successfully applied to predict toxicities of wide groups of chemicals in other test species including algae (S. obliguue), daphnia, fish, and bacteria. Structural diversity of the selected chemicals and those of the end-point toxicity data of five different test species were tested using the Tanimoto similarity index and Kruskal-Wallis (K-W) statistics. Predictive and generalization abilities of the constructed QSAR models were compared using statistical parameters. The developed QSAR models (DTB and DTF) yielded a considerably high classification accuracy in complete data of model building (algae) species (97.82%, 99.01%) and ranged between 92.50%-94.26% and 92.14%-94.12% in four test species, respectively, whereas regression QSAR models (DTB and DTF) rendered high correlation (R(2)) between the measured and model predicted toxicity end-point values and low mean-squared error in model building (algae) species (0.918, 0.15; 0.905, 0.21) and ranged between 0.575 and 0.672, 0.18-0.51 and 0.605-0.689 and 0.20-0.45 in four different test species. The developed QSAR models exhibited good predictive and generalization abilities in different test species of varied trophic levels and can be used for predicting the toxicities of new chemicals for screening and prioritization of chemicals for regulation.