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.

Prevalence of hepatitis A virus, hepatitis B virus, hepatitis C virus, hepatitis D virus and hepatitis E virus as causes of acute viral hepatitis in North India: a hospital based study

CONTEXT

Acute viral hepatitis (AVH) is a major public health problem and is an important cause of morbidity and mortality.

AIM

The aim of the present study is to determine the prevalence of hepatitis A virus (HAV), hepatitis B virus (HBV), hepatitis C virus (HCV), hepatitis D virus (HDV) and hepatitis E virus (HEV) as causes of AVH in a tertiary care hospital of North India.

SETTINGS AND DESIGN

Blood samples and clinical information was collected from cases of AVH referred to the Grade I viral diagnostic laboratory over a 1-year period.

SUBJECTS AND METHODS

Samples were tested for hepatitis B surface antigen, anti-HCV total antibodies, anti-HAV immunoglobulin M (IgM) and anti-HEV IgM by the enzyme-linked immunosorbent assay. PCR for nucleic acid detection of HBV and HCV was also carried out. Those positive for HBV infection were tested for anti-HDV antibodies.

STATISTICAL ANALYSIS USED

Fisher's exact test was used and a P < 0.05 was considered to be statistically significant.

RESULTS

Of the 267 viral hepatitis cases, 62 (23.22%) patients presented as acute hepatic failure. HAV (26.96%) was identified as the most common cause of acute hepatitis followed by HEV (17.97%), HBV (16.10%) and HCV (11.98%). Co-infections with more than one virus were present in 34 cases; HAV-HEV co-infection being the most common. HEV was the most important cause of acute hepatic failure followed by co-infection with HAV and HEV. An indication towards epidemiological shift of HAV infection from children to adults with a rise in HAV prevalence was seen.

CONCLUSIONS

To the best of our knowledge, this is the first report indicating epidemiological shift of HAV in Uttar Pradesh.

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure-toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R(2)) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R(2) and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals.

A New Robust Method for Nonlinear Regression

BACKGROUND

When outliers are present, the least squares method of nonlinear regression performs poorly. The main purpose of this paper is to provide a robust alternative technique to the Ordinary Least Squares nonlinear regression method. This new robust nonlinear regression method can provide accurate parameter estimates when outliers and/or influential observations are present.

METHOD

Real and simulated data for drug concentration and tumor size-metastasis are used to assess the performance of this new estimator. Monte Carlo simulations are performed to evaluate the robustness of our new method in comparison with the Ordinary Least Squares method.

RESULTS

In simulated data with outliers, this new estimator of regression parameters seems to outperform the Ordinary Least Squares with respect to bias, mean squared errors, and mean estimated parameters. Two algorithms have been proposed. Additionally and for the sake of computational ease and illustration, a Mathematica program has been provided in the Appendix.

CONCLUSION

The accuracy of our robust technique is superior to that of the Ordinary Least Squares. The robustness and simplicity of computations make this new technique more appropriate and useful tool for the analysis of nonlinear regressions.

Robust Logistic and Probit Methods for Binary and Multinomial Regression

In this paper we introduce new robust estimators for the logistic and probit regressions for binary, multinomial, nominal and ordinal data and apply these models to estimate the parameters when outliers or inluential observations are present. Maximum likelihood estimates don't behave well when outliers or inluential observations are present. One remedy is to remove inluential observations from the data and then apply the maximum likelihood technique on the deleted data. Another approach is to employ a robust technique that can handle outliers and inluential observations without removing any observations from the data sets. The robustness of the method is tested using real and simulated data sets.

Nano-QSAR modeling for predicting biological activity of diverse nanomaterials

Case study-1 (diverse metal core NPs); case study-2 (similar metal core NPs); case study-3 (metal oxide NPs); case study-4 (surface modified multi-walled CNTs); case study-5 (fullerene derivatives).

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes.

Effect of physiologically relevant heat shock on development, apoptosis and expression of some genes in buffalo (Bubalus bubalis) embryos produced in vitro

For investigating the effects of physiologically relevant heat shock, buffalo oocytes/embryos were cultured at 38.5°C (control) or were exposed to 39.5°C (Group II) or 40.5°C (Group III) for 2 h once every day throughout in vitro maturation (IVM), fertilization (IVF) and culture (IVC). Percentage of oocytes that developed to 8-cell, 16-cell or blastocyst stage was lower (p < 0.05) and the number of apoptotic nuclei was higher (p < 0.05) for Group III > Group II > controls. At both 8-16-cell and blastocyst stages, relative mRNA abundance of stress-related genes HSP 70.1 and HSP 70.2 and pro-apoptotic genes CASPASE-3, BID and BAX was higher (p < 0.05) in Groups III and II than that in controls with the exception of stress-related gene HSF1. Expression level of anti-apoptotic genes BCL-XL and MCL-1 was also higher (p < 0.05) in Groups III and II than that in controls at both 8-16-cell and blastocyst stages. Among the genes related to embryonic development, at 8-16-cell stage, the expression level of GDF9 was higher (p < 0.05) in Group III than that in controls, whereas that of GLUT1, ZAR1 and BMP15 was not significantly different among the three groups. At the blastocyst stage, relative mRNA abundance of GLUT1 and GDF9 was higher (p < 0.05) in Group II than that in controls, whereas that of ZAR-1 and BMP15 was not affected. The results of this study demonstrate that exposure of buffalo oocytes and embryos to elevated temperatures for duration of time that is physiologically relevant severely compromises their developmental competence, increases apoptosis and affects stress-, apoptosis- and development-related genes.

Predicting adsorptive removal of chlorophenol from aqueous solution using artificial intelligence based modeling approaches

The research aims to develop artificial intelligence (AI)-based model to predict the adsorptive removal of 2-chlorophenol (CP) in aqueous solution by coconut shell carbon (CSC) using four operational variables (pH of solution, adsorbate concentration, temperature, and contact time), and to investigate their effects on the adsorption process. Accordingly, based on a factorial design, 640 batch experiments were conducted. Nonlinearities in experimental data were checked using Brock-Dechert-Scheimkman (BDS) statistics. Five nonlinear models were constructed to predict the adsorptive removal of CP in aqueous solution by CSC using four variables as input. Performances of the constructed models were evaluated and compared using statistical criteria. BDS statistics revealed strong nonlinearity in experimental data. Performance of all the models constructed here was satisfactory. Radial basis function network (RBFN) and multilayer perceptron network (MLPN) models performed better than generalized regression neural network, support vector machines, and gene expression programming models. Sensitivity analysis revealed that the contact time had highest effect on adsorption followed by the solution pH, temperature, and CP concentration. The study concluded that all the models constructed here were capable of capturing the nonlinearity in data. A better generalization and predictive performance of RBFN and MLPN models suggested that these can be used to predict the adsorption of CP in aqueous solution using CSC.

Prevalence of metabolic syndrome among Nepalese type 2 diabetic patients

This study was carried out to establish the prevalence of metabolic syndrome among the type 2 diabetic patients in Nepal. Two hundred twenty one participants aged 26-90 (mean age 53.41 +/- 13.30) years with established type 2 diabetes visiting Kathmandu Model Hospital, Kathmandu, Nepal from August 2011 to November 2011 were included in the study. National Cholesterol Education Adult Treatment Panel III (NCEP ATPIII) definition of the metabolic syndrome with ethnic threshold on abdominal obesity was used. 170 (76.9%) participants were found to have metabolic syndrome. Thirty two (14.5%) participants fulfilled all 5 criteria for metabolic syndrome, 63 (28.5%) participants had four criteria while three criteria were fulfilled by 75 (33.9%) of the participants. Among the patients having metabolic syndrome, hypertension was seen in 89 (52.35%) participants, raised serum triglyceride levels were found in 144 (84.70%) subjects, decreased serum HDL cholesterol levels were found in 119 (70%) participants while in 108 (63.35%) participants increased waist circumference was found. There was a higher frequency of metabolic syndrome in obese (81.58%) and in overweight (79.49%) subjects. This study indicates significant prevalence of metabolic syndrome among type 2 diabetic patients with strong association of obesity.

In vitro culture and morphological characterization of prepubertal buffalo (Bubalus bubalis) putative spermatogonial stem cell

PURPOSE

Spermatogonial stem cells (SSCs) have the unique ability both to self-renew and to produce progeny that undergo differentiation to spermatozoa. The present study has been carried out to develop a method to purify and enrich the pure populations of spermatogonial stem cell like cells in buffalo.

METHODS

The spermatogonial cells were isolated from testes of 3-7 month old buffalo calves and disaggregated by double enzymatic digestion. Mixed population of isolated cells were then plated on Datura stramonium agglutinin (DSA) lectin coated dishes for attachment of Sertoli cells. The desired cells were obtained from suspension medium after 18 h of incubation and then loaded on discontinuous density gradient using percoll (20-65 %) and different types of spermatogonia cells were obtained at interface of each layer. These cells were cultured in vitro.

RESULTS

Spermatogonial cells isolated have spherical outline and two or three eccentrically placed nucleoli, created a colony after proliferation during first week or immediately after passage. After 7-10 days of culture, the resulted developed colonies of spermatogonial cells expressed the spermatogonial specific genes like Plzf and VASA; and other pluripotency related markers viz. alkaline phosphtase, DBA, CD9, CD90, SSEA-1, OCT-4, NANOG and REX-1.

CONCLUSION

Our results show that the isolated putative spermatogonial stem cells exhibit the expression of pluripotency related and spermatogonial specific genes. This study may help to establish a long term culture system for buffalo spermatogonia.

Optimization of nitrate reduction by EDTA catalyzed zero-valent bimetallic nanoparticles in aqueous medium

The present study aims to investigate the EDTA catalyzed reduction of nitrate (NO (3) (-) ) by zero-valent bimetallic (Fe-Ag) nanoparticles (ZVBMNPs) in aqueous medium and to enumerate the effect of temperature, solution pH, ZVBMNPs dose and EDTA concentration on NO (3) (-) reduction. Batch experimental data were generated using a four-factor Box-Behnken design. Optimization modeling was performed using the response surface method for maximizing the reduction of NO (3) (-) by ZVBMNPs. Significance of the independent variables and their interactions were tested by the analysis of variance and t test statistics. The model predicted maximum reduction capacity (340.15 mg g(-1) NO (3) (-) ) under the optimum conditions of temperature, 60 °C; pH 4; dose, 1.0 g l(-1); and EDTA concentration, 2.0 mmol l(-1) was very close to the experimental value (338.62 mg g(-1)) and about 16 % higher than the experimentally determined capacity (291.32 mg g(-1)). Study demonstrated that ZVBMNPs had higher reduction efficiency than Fe(0) nanoparticles for NO (3) (-) . EDTA significantly enhanced the NO (3) (-) reduction by ZVBMNPs. The EDTA catalyzed reduction of NO (3) (-) by ZVBMNPs can be employed for the effective decontamination of water.

Therapeutic effect of yoga in patients with hypertension with reference to GST gene polymorphism

BACKGROUND

Hypertension, a chronic medical condition of increased blood pressure, is a serious public health problem. Environmental and genetic risk factors are known to predispose to hypertension. The present study was designed to investigate the association of glutathione S-transferase (GST) gene polymorphism with oxidative stress in hypertensive patients and the possible beneficial effect of yoga on them.

MATERIALS AND METHODS

Sixty (60) hypertensive individuals, between 30 and 60 years of age, were divided into two groups of 30 each. The yoga group was subjected to 50-60 minutes of yogic practices daily for 42 days, while the control group included the remaining 30 age- and sex-matched hypertensive individuals. GST gene polymorphism was analyzed using multiple allele specific polymerase chain reaction, and oxidative stress parameters were assessed biochemically.

RESULTS

Assessment of blood pressure showed a statistically significant though modest reduction (p<0.05) in the yoga group as compared to the control group. Malondialdehyde was observed to be significantly low (p<0.05), while antioxidant capacity in the form of GST showed an increasing trend and ferric-reducing ability of plasma was significantly increased (p<0.05) in the subjects who practiced yoga.

CONCLUSIONS

In conclusion, yoga has been found to decrease blood pressure as well as the levels of oxidative stress in patients with hypertension.

Buffalo (Bubalus bubalis) ES cell-like cells are capable of in vitro skeletal myogenic differentiation

When buffalo embryonic stem (ES) cell-like cells that expressed surface markers SSEA-4, TRA-1-60, TRA-1-81, CD9 and CD90 and intracellular markers OCT4, SOX2 and FOXD3, as shown by immunofluorescence, and that expressed REX-1 and NUCLEOSTEMIN as confirmed by RT-PCR, were subjected to suspension culture in hanging drops in absence of LIF and buffalo foetal fibroblast feeder layer support, they differentiated to form three-dimensional embryoid bodies (EBs). Of 231 EBs examined on Day 3 of suspension culture, 141 (61.3 ± 3.09%) were of compact type, whereas 90 (38.4 ± 3.12%) were of cystic type. The cells obtained from EBs were found to express NF-68 and NESTIN (ectodermal lineage), BMP-4 and α-skeletal actin (mesodermal lineage), and α-fetoprotein, GATA-4 and HNF-4 (endodermal lineage). When these EBs were cultured on gelatin-coated dishes, they spontaneously differentiated to several cell types such as epithelial- and neuron-like cells. When EBs were cultured in the presence of 1 or 2% DMSO or 10(-8) M or 10(-7) M retinoic acid for 25 days, ES cells could be directed to form muscle cell-like cells, the identity of which was confirmed by expression of α-actinin by immunofluorescence and of MYF-5, MYOD and MYOGENIN genes by RT-PCR. MYOD was first detected on Day 10 in both treatment groups and on Day 15 in controls, whereas MYOGENIN was first detected on Day 10, Day 15 and Day 25 in the presence of retinoic acid, in the presence of DMSO and in controls, respectively. The present study demonstrates the ability of buffalo ES cell-like cells to undergo directed differentiation to cells of skeletal myogenic lineage.

Modeling and optimization of reductive degradation of chloramphenicol in aqueous solution by zero-valent bimetallic nanoparticles

PURPOSE

The present study aims to investigate the individual and combined effects of temperature, pH, zero-valent bimetallic nanoparticles (ZVBMNPs) dose, and chloramphenicol (CP) concentration on the reductive degradation of CP using ZVBMNPs in aqueous medium.

METHOD

Iron-silver ZVBMNPs were synthesized. Batch experimental data were generated using a four-factor statistical experimental design. CP reduction by ZVBMNPs was optimized using the response surface modeling (RSM) and artificial neural network-genetic algorithm (ANN-GA) approaches. The RSM and ANN methodologies were also compared for their predictive and generalization abilities using the same training and validation data set. Reductive by-products of CP were identified using liquid chromatography-mass spectrometry technique.

RESULTS

The optimized process variables (RSM and ANN-GA approaches) yielded CP reduction capacity of 57.37 and 57.10 mg g(-1), respectively, as compared to the experimental value of 54.0 mg g(-1) with un-optimized variables. The ANN-GA and RSM methodologies yielded comparable results and helped to achieve a higher reduction (>6%) of CP by the ZVBMNPs as compared to the experimental value. The root mean squared error, relative standard error of prediction and correlation coefficient between the measured and model-predicted values of response variable were 1.34, 3.79, and 0.964 for RSM and 0.03, 0.07, and 0.999 for ANN models for the training and 1.39, 3.47, and 0.996 for RSM and 1.25, 3.11, and 0.990 for ANN models for the validation set.

CONCLUSION

Predictive and generalization abilities of both the RSM and ANN models were comparable. The synthesized ZVBMNPs may be used for an efficient reductive removal of CP from the water.

Linear and nonlinear modeling approaches for urban air quality prediction

In this study, linear and nonlinear modeling was performed to predict the urban air quality of the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial regression (MPR), and artificial neural network (ANN) approach-based models were constructed to predict the respirable suspended particulate matter (RSPM), SO(2), and NO(2) in the air using the meteorological (air temperature, relative humidity, wind speed) and air quality monitoring data (SPM, NO(2), SO(2)) of five years (2005-2009). Three different ANN models, viz. multilayer perceptron network (MLPN), radial-basis function network (RBFN), and generalized regression neural network (GRNN) were developed. All the five different models were compared for their generalization and prediction abilities using statistical criteria parameters, viz. correlation coefficient (R), standard error of prediction (SEP), mean absolute error (MAE), root mean squared error (RMSE), bias, accuracy factor (A(f)), and Nash-Sutcliffe coefficient of efficiency (E(f)). Nonlinear models (MPR, ANNs) performed relatively better than the linear PLSR models, whereas, performance of the ANN models was better than the low-order nonlinear MPR models. Although, performance of all the three ANN models were comparable, the GRNN over performed the other two variants. The optimal GRNN models for RSPM, NO(2), and SO(2) yielded high correlation (between measured and model predicted values) of 0.933, 0.893, and 0.885; 0.833, 0.602, and 0.596; and 0.932, 0.768 and 0.729, respectively for the training, validation and test sets. The sensitivity analysis performed to evaluate the importance of the input variables in optimal GRNN revealed that SO(2) was the most influencing parameter in RSPM model, whereas, SPM was the most important input variable in other two models. The ANN models may be useful tools in the air quality predictions.

Optimizing removal of ibuprofen from water by magnetic nanocomposite using Box-Behnken design

PURPOSE

The present research aims to optimize the removal of ibuprofen (IBP), a non-steroidal anti-inflammatory, analgesic, and antipyretic drug from the aqueous solution using a synthesized magnetic carbon-iron nanocomposite, and to investigate the individual and combined effects of the independent process variables.

METHOD

Combining the adsorptive capability of carbon and magnetic property of iron, a carbon-iron nanocomposite was synthesized. A four-factor Box-Behnken experimental design-based optimization modeling was performed for maximizing the removal of IBP from water by the nanocomposite using the batch experimental data. A quadratic model was built to predict the responses. Significance of the process variables and their interactions was tested by the analysis of variance and t test statistics.

RESULTS

The experimental maximum removals of IBP from the aqueous solution by carbon and magnetic nanocomposite were 14.74% and 60.39%, respectively. The model predicted maximum removal of 65.81% under the optimum conditions of the independent variables (IBP concentration 80 mg/l; temperature 48°C; pH 2.50; dose 0.6 g/l) was very close to the experimental value (65.12 ± 0.92%). pH of the solution exhibited most significant effect on IBP adsorption.

CONCLUSION

The developed magnetic nanocomposite was found superior than its precursor carbon exhibiting higher removal of IBP from the water and can be easily separated from the aqueous phase under temporary external magnetic field. The developed magnetic nanocomposite may be used for an efficient removal of IBP from the water.

Modeling and optimization of trihalomethanes formation potential of surface water (a drinking water source) using Box-Behnken design

PURPOSE

The present research aims to investigate the individual and interactive effects of chlorine dose/dissolved organic carbon ratio, pH, temperature, bromide concentration, and reaction time on trihalomethanes (THMs) formation in surface water (a drinking water source) during disinfection by chlorination in a prototype laboratory-scale simulation and to develop a model for the prediction and optimization of THMs levels in chlorinated water for their effective control.

METHODS

A five-factor Box-Behnken experimental design combined with response surface and optimization modeling was used for predicting the THMs levels in chlorinated water. The adequacy of the selected model and statistical significance of the regression coefficients, independent variables, and their interactions were tested by the analysis of variance and t test statistics.

RESULTS

The THMs levels predicted by the model were very close to the experimental values (R(2) = 0.95). Optimization modeling predicted maximum (192 μg/l) TMHs formation (highest risk) level in water during chlorination was very close to the experimental value (186.8 ± 1.72 μg/l) determined in laboratory experiments. The pH of water followed by reaction time and temperature were the most significant factors that affect the THMs formation during chlorination.

CONCLUSION

The developed model can be used to determine the optimum characteristics of raw water and chlorination conditions for maintaining the THMs levels within the safe limit.

Effect of yoga practices on pulmonary function tests including transfer factor of lung for carbon monoxide (TLCO) in asthma patients

Prana is the energy, when the self-energizing force embraces the body with extension and expansion and control, it is pranayama. It may affect the milieu at the bronchioles and the alveoli particularly at the alveolo-capillary membrane to facilitate diffusion and transport of gases. It may also increase oxygenation at tissue level. Aim of our study is to compare pulmonary functions and diffusion capacity in patients of bronchial asthma before and after yogic intervention of 2 months. Sixty stable asthmatic-patients were randomized into two groups i.e group 1 (Yoga training group) and group 2 (control group). Each group included thirty patients. Lung functions were recorded on all patients at baseline, and then after two months. Group 1 subjects showed a statistically significant improvement (P<0.001) in Transfer factor of the lung for carbon monoxide (TLCO), forced vital capacity (FVC), forced expiratory volume in 1st sec (FEV1), peak expiratory flow rate (PEFR), maximum voluntary ventilation (MVV) and slow vital capacity (SVC) after yoga practice. Quality of life also increased significantly. It was concluded that pranayama & yoga breathing and stretching postures are used to increase respiratory stamina, relax the chest muscles, expand the lungs, raise energy levels, and calm the body.

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.