Multi-model exploration of groundwater quality and potential health risk assessment in Jajpur district, Eastern India.

The presence of fluoride and nitrate is a serious groundwater quality issue in India impacting human health. In the present study, 14 different hydrochemical parameters for 76 groundwater samples collected from the Jajpur district of Odisha, India, were evaluated. Entropy-weighted water quality index (EWQI), fixed-weight groundwater quality index (GWQI), principal component analysis (PCA), and rotated factor loading-based water quality index (PCWQI) were employed to assess groundwater quality. About 65.79 ± 4.68%, 33.55 ± 3.95%, and 0.66 ± 0.76% of the samples were rated as "excellent," "good," or "medium" quality, respectively, across the four different water quality indices, with a nominal rating discrepancy of 13.15%. Though 86% of samples consistently received excellent or good ratings across all WQI frameworks, concentrations of F- and NO3- in 36.8% and 11.84% of the samples exceeded the WHO permissible limit. In health risk assessment, about 38.15% of samples surpassed the F- hazard quotient (HQ > 1) posing non-carcinogenic health risks for children. The non-carcinogenic health risks due to NO3- were evident in 55.26% and 11.84% of samples for children and adults, respectively. The higher concentration of NO3- in some of the water samples, together with its positive correlation with HCO3-, may worsen groundwater pollution. The moderate correlation between Ca2+ and HCO3- (r = 0.410) and the insignificant correlation between Mg2+ and HCO3- (r = 0.234) suggests calcite dissolution is far more common than dolomite.

Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges.

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an "optimal" model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.

Structural variability, implementational irregularities in mathematical modelling of polyhydroxyalkanoates (PHAs) production-A state-of-the-art review.

The rate and extent of microbial polyhydroxyalkanoates (PHAs) production rely on the availability of substrates, growth of microbial biomass, and intracellular accumulation of polymer under nitrogen-limited conditions. The dynamics of PHAs production captured through various structured or unstructured models can be extended to design an optimal feeding strategy for process intensification. Large variability in process assumptions, choices of kinetics, and model complexity is expected depending on substrate(s), microbial metabolism, and discretization of the process under consideration. This communication attempts to review the estimation of stoichiometric yield coefficients, metabolic modelling, and choices of unstructured kinetics in microbial PHA production. Implementational irregularities in parameter estimation and quality check in modelling exercises have also been reviewed. It is observed that the scope of the majority of the "modelling" studies is confined to the estimation of stoichiometric parameters with limited utility. In dynamic models, microbial growth is often described using either Monod or logistic variants, while PHAs production adopts a Luedeking-Piret expression with or without substrate inhibition. Though model selection, regression with experimental data, parameter estimation, and model validation are integral parts of the exercise, very few provide sufficient coverage on all those aspects. Application of the model to control or optimize the bioprocess has rarely been attempted.

Dynamic modelling of growth and flavonoid production from Ocimum tenuiflorum suspension culture.

A structured-segregated dynamic model for biomass growth, sucrose utilization and flavonoid production in Ocimum tenuiflorum suspension culture is proposed, considering a dynamic heterogeneous population of viable active, viable nonactive and dead cell. The sucrose hydrolysis (into glucose and fructose), substrate uptake by biomass and intracellular flavonoid production are modelled using Contois kinetics, a competitive double-substrate Monod, and Luedeking-Piret model, respectively. The conversion of active to viable-nonactive biomass has been formulated as a function of the total substrate and biomass concentrations. Parameters for the dynamic model are evaluated while minimizing the sum of square errors between modelled and measured biomass, cell viability, glucose, fructose and intracellular flavonoid contents. Bootstrap confidence intervals and dynamic relative sensitivity analysis of these model parameters are presented. The knowledge gained from the population-based model in plant suspension culture can provide the basic framework for prediction and optimization of the bioprocess system for phytochemical production.

Adsorption, Bioavailability and Microbial Toxicity of Diclofenac in Agricultural Soil.

Persistence and environmental implication of pharmaceuticals in agricultural soil is determined depending on adsorption, bioavailability and toxicity. This study aims to assess adsorption/partitioning behaviour of diclofenac (DCF) and its impact on microbial activity in four agricultural soils, differing in pH, organic carbon content, and cation exchange capacity. Results from batch studies suggests that soil/water partition coefficients of DCF are essentially nonlinear, i.e. depends on drug amount (p = 0.001), and positively correlated with soil organic carbon (p = 0.008). The adsorption data can effectively be modelled using Freundlich isotherm (regression coefficients between 0.84 and 0.90). In soil incubation studies, DCF could not be detected after 6 days of spiking (20 µg/g) in all soil types, including abiotic control. This suggests an interplay of combined biotic/abiotic process in DCF removal. Though microbial activity (based on tetrazolium reduction) declined with incubation time, but was not correlated with DCF exposure, particularly in soils rich in organic carbon.

Potential of metabolic engineering in bacterial nanosilver synthesis.

The widespread applications of silver nanoparticles in present days demand an industrial-scale production process. The ability of bacteria to synthesise silver nanoparticles can be exploited to overcome many shortcomings associated with conventional production processes, such as high cost and nanoparticle toxicity. However, lack of a standardised protocol and suboptimal yield remain a major obstacle for bacterial synthesis route. A potential, yet unexplored, solution to this problem could be envisioned through rewiring of the metabolic network to direct cellular resources towards the product of interest. Mathematical modelling of metabolic pathway is the key to understand and manipulate the cellular metabolism for enhanced production of desired metabolite(s). The present study provides a perspective on the scope of metabolic engineering approaches to enhance bacterial synthesis of silver nanoparticles.

Redox-mediator-free degradation of sulfathiazole and tetracycline using Phanerochaete chrysosporium.

The removal of two of the most commonly used antibiotics, tetracycline (TC) and sulfathiazole (STZ), using laccase-producing Phanerochaete chrysosporium was studied in liquid-phase batch experiments in the absence of any synthetic redox mediator. The removal of STZ and TC from single antibiotic spikes varied from 97.8% to 15.4% and 98.8% to 31%, respectively, with increasing initial doses of 10-250 mg L-1 within 14 days of incubation. The enzyme activity of P. chrysosporium was only minimally influenced by the concentrations of these antibiotics. The degradation of antibiotics initiated before an appreciable extracellular enzyme activity was noted in the fungal culture. The appearance of low-molecular weight molecular fragments from parent antibiotics in liquid chromatography-mass spectrometry confirmed the biodegradation process.

Degradation of the long-resistant pharmaceutical compounds carbamazepine and diatrizoate using mixed microbial culture.

The microbial degradation of two recalcitrant pharmaceutical compounds, carbamazepine (CBZ) and diatrizoate (DTZ), was studied in laboratory batch experiments. We used a defined mixed microbial culture comprising four distinct microbial species that were previously known to have high decomposition capacity toward recalcitrant substances. Biological decomposition in liquid phase cultures for either CBZ or DTZ, or in a combination of the two, was conducted for 12 days. DTZ and CBZ were degraded by 43.2% and 60%, respectively from an initial concentration of 100 µg L(-1). When degradation was assessed using a mixture of the two compounds, the initial degradation rates of CBZ and DTZ were lower than those observed in the single-compound study. However, the final cumulative removal efficiency was very similar. The extent of dissolved organic carbon (DOC) removal was correlated with the degradation of the pharmaceuticals.

Impact of Acetic Acid Supplementation in Polyhydroxyalkanoates Production by Cupriavidus necator Using Mixture-Process Design and Artificial Neural Network.

The trend in bioplastic application has increased over the years where polyhydroxyalkanoates (PHAs) have emerged as a potential candidate with the advantage of being bio-origin, biodegradable, and biocompatible. The present study aims to understand the effect of acetic acid concentration (in combination with sucrose) as a mixture variable and its time of addition (process variable) on PHA production by Cupriavidus necator. The addition of acetic acid at a concentration of 1 g l-1 showed a positive influence on biomass and PHA yield; however, the further increase had a reversal effect. The addition of acetic acid at the time of incubation showed a higher PHA yield, whereas maximum biomass was achieved when acetic acid was added after 48 h. Genetic algorithm (GA) optimized artificial neural network (ANN) was used to model PHA concentration from mixture-process design data. Fitness of the GA-ANN model (R2: 0.935) was superior when compared to the polynomial model (R2: 0.301) from mixture design. Optimization of the ANN model projected 2.691 g l-1 PHA from 7.245 g l-1 acetic acid, 12.756 g l-1 sucrose, and the addition of acetic acid at the time of incubation. Sensitivity analysis indicates the inhibitory effect of all the predictors at higher levels. ANN model can be further used to optimize the variables while extending the bioprocess to fed-batch operation.

Assessment of a biostimulated or bioaugmented calcification system with Bacillus pasteurii in a simulated soil environment.

To evaluate how native soil microorganism and nutrients interact in a bioaugmented or biostimulated calcification process, batch experiments were conducted in saturated soil extract either amended with Bacillus pasteurii and/or NB-NH4Cl media (nutrient broth, 3.0 g/L; NH4Cl, 10.0 g/L; NaHCO3, 2.12 g/L). The NB-NH4Cl medium was chosen out of three potential candidates, where B. pasteurii precipitated the maximum amount of calcium (>95 %) as calcite in 12 h of incubation. The addition of B. pasteurii into saturated soil extract marginally improved the calcification to 4.26 % compared to the unamended control (3.2 %). Calcification with B. pasteurii in autoclaved soil extract was even better (18.75 %). However, the addition of the NB-NH4Cl medium into the soil extract could significantly improve the calcification irrespective of whether B. pasteurii was added (88.46 %) or not (77.04 %). It suggests that soil microbial activity was not limiting even though soil nutrient was inadequate. It also indicates a possible negative interaction between soil microorganisms and B. pasteurii under nutrient-limited conditions.

Characterization of co-digestion of industrial sludges for biogas production by artificial neural network and statistical regression models.

The characteristics and impact of industrial sludges of paper, chemical, petrochemical, automobile, and food industries situated in the Ulsan Industrial Complex, Ulsan, Republic of Korea in co-digestion for biogas production were assessed by artificial neural network (ANN) and statistical regression models. The regression model was based on a simplex-centroid mixture design and the ANN was based on a resilient back-propagation algorithm (topology 5-7-1). Using connection weights and bias of the trained ANN model, the impact of each sludge of co-digestion was assessed using Garsons' algorithm. Results suggested that the modelling and predictability of ANN were superior to the regression model with accuracy (A(f)) 1.01, bias (B(f)) 1.00, root mean square error 3.56, and standard error of prediction 2.51%. Sludge from the chemical industry showed the highest impact on specific methane yield (SMY(VS)) with a relative importance of 28.59% followed by sludges from paper (20.07%), food (19.59%), petrochemical (15.92%), and automobile (15.82%) industries. The interactions between diverse industrial sludges were successfully modelled and partitioned into various synergistic and antagonistic effects on SMY(VS). Synergistic interactions between the chemical industry sludge and either petrochemical or food industry sludges on SMY(VS) were detected. However, strong negative interaction between automobile sludge and other sludges was observed. This study indicates that though the ANN model performed better in prediction and impact assessments, the regression model reveals the synergistic and antagonistic interactions among sludges.

Dynamic Model Selection and Optimal Batch Design for Polyhydroxyalkanoate (PHA) Production by Cupriavidus necator.

Mathematical modelling of microbial polyhydroxyalkanoates (PHAs) production is essential to develop optimal bioprocess design. Though the use of mathematical models in PHA production has increased over the years, the selection of kinetics and model identification strategies from experimental data remains largely heuristic. In this study, PHA production from Cupriavidus necator utilizing sucrose and urea was modelled using a parametric discretization approach. Product formation kinetics and relevant parameters were established from urea-free experimental sets, followed by the selection of growth models from a batch containing both sucrose and urea. Logistic growth and Luedeking-Piret model for PHA production was selected based on regression coefficient (R2: 0.941), adjusted R2 (0.930) and AICc values (-42.764). Model fitness was further assessed through cross-validation, confidence interval and sensitivity analysis of the parameters. Model-based optimal batch startup policy, incorporating multi-objective desirability, suggests an accumulation of 2.030 g l-1 of PHA at the end of 120 h. The modelling framework applied in this study can be used not only to avoid over-parameterization and identifiability issues but can also be adopted to design optimal batch startup policies.

Modulating the Effect of ß-Sitosterol Conjugated with Magnetic Nanocarriers to Inhibit EGFR and Met Receptor Cross Talk.

The cross-talk between the EGFR (Epidermal Growth Factor Receptor) and MET (Hepatocyte Growth Factor Receptor) poses a significant challenge in the field of molecular signaling. Their intricate interplay leads to dysregulation and contributes to cancer progression and therapeutic resistance. ß-Sitosterol (BS), a plant sterol with promising anticancer properties, shows increased research on its potential as a chemopreventive agent. However, significant modifications are required to deliver BS in cancer cells due to its lower efficacy. The present work aims to design a carrier-mediated delivery system specifically targeting cancer cells with EGFR and MET receptor cross-talk. Surface modification of BS was performed with superparamagnetic iron oxide nanoparticles (SPIONs), polyethylene glycol (PEG), and poly(N-isopropylacrylamide) (PNIPAM) to enhance the delivery of BS at the target site. BS was conjugated with SPIONs (BS-S), PNIPAM (BS-SP), PEG, and PNIPAM (BS-SPP) polymers, respectively, and the conjugated complexes were characterized. Results showed an increase in size, stability, and monodispersity in the following order, BS-S, BS-SP, and BS-SPP. The drug encapsulation efficiency was observed to be highest in BS-SPP (82.5%), compared to BS-S (61%) and BS-SP (74.9%). Sustained drug release was achieved in both BS-SP (82.6%) and BS-SPP (83%). The IC 50 value of BS, BS-S, BS-SP, and BS-SPP towards MCF 7 was 242 µg/mL,197 µg/mL, 168 µg/mL, and 149 µg/mL, HEPG2 was 274 µg/mL, 261 µg/mL, 233 µg/mL and 207 µg/mL and NCIH 460 was 191 µg/mL, 185 µg/mL, 175 and 164 µg/mL, indicating highest inhibition towards NCIH 460 cells. Our results conclude that ß-sitosterol conjugated with SPION, PEG, and PNIPAM could be a potential targeted therapy in inhibiting EGFR and MET receptor-expressing cancer cells.

Inconsistency of PCA-based water quality index - Does it reflect the quality?

The formalization of a stable water quality index (WQI) from measured hydrogeochemical parameters is essential for the identification and classification of water resources. In the principal component analysis (PCA) based WQI approach, the parameter weight is derived using either PC loading or rotated factor loading from a large number of samples pooled for WQI measurement. The PCA-based approach is paradoxical, as the calculated WQI rating of a sample would rather be dependent on the size, and composition of the population. Though this issue is well anticipated, no attempt has been made to regularize or measure the extent of WQI disagreement. In the present study, the WQI of 106 groundwater samples analyzed for 12 different hydrochemical parameters were modelled using PC loading or rotated factor loading (referred to as PCQ-1, PCQ-2, respectively) approach. Analysis reveals PCQ-1 to be positively biased in 78 % of samples and rating disagreements were evident in 9.43 % of samples. WQI of the data set was estimated using repeated (1000) random non-overlapping 2 to 5-fold data partitioning (containing 21 to 83 samples in each fold) adopting either an in-sample (test set) or out-sample (train set) modelling approach. The mean of WQI deviations in repeated resampling from the reference (i.e., using the entire dataset) has been positive in most of the samples using the PCQ-1 model, irrespective of the fold partition size. The median root mean square deviation values of the data set increased with the number of fold partitioning for in-sample calibration for both PCQ-1 and PCQ-2 approaches. The exclusion of a single water quality parameter from the PCA model can cause up to a 60 % deviation of the WQI score in some water samples. The cross-validation and Monte Carlo resampling approach can serve as a framework to test the stability of PCA-based WQI.

Multivariate regression and artificial neural network modelling of sugar yields from acid pretreatment and enzymatic hydrolysis of lignocellulosic biomass.

Reducing sugar generation from lignocellulosic biomass (LCB) is closely linked with biomass characteristics, pretreatment and enzymatic hydrolysis conditions. In this study curated experimental data from literature was used to develop multivariate regression and artificial neural network (ANN) model considering nine predictors (i.e., cellulose, hemicellulose, lignin content, cellulose-lignin ratio, acid concentration, temperature, time, pretreatment severity, and enzyme concentration). Selected reduced polynomial model (R2: 0.891, Adj. R2: 0.849) suggests positive influence of acid and enzyme, while negative influence of treatment severity, temperature and time on reducing sugar generation. Genetic algorithm-optimized ANN model offered excellent fitness for LCB hydrolysis on training (R2: 0.997), validation (R2: 0.984), and test sets (R2: 0.967). Sensitivity analysis of the ANN predictors suggests lignin and to some extent hemicellulose contents can be inhibitory. Though polynomial models can have simple interpretation, use of optimized ANN offers better predictability in dataset with diverse biomass compositions.

Optimization of low-grade coal and refuse-derived fuel blends for improved co-combustion behavior in coal-fired power plants.

This study is aimed at utilizing three waste materials, i.e., solid refuse fuel (SRF), tire derived fuel (TDF), and sludge derived fuel (SDF), as eco-friendly alternatives to coal-only combustion in co-firing power plants. The contribution of waste materials is limited to ≤5% in the composition of the mixed fuel (coal + waste materials). Statistical experimental design and response surface methodology are employed to investigate the effect of mixed fuel composition (SRF, TDF, and SDF) on gross calorific value (GCV) and ash fusion temperature (AFT). A quadratic model is developed and statistically verified to apprehend mixed fuel constituents' individual and combined effects on GCV and AFT. Constrained optimization of fuel blend, i.e., GCV >1,250 kcal/kg and AFT >1,200 °C, using the polynomial models projected the fuel-blend containing 95% coal with 3.84% SRF, 0.35% TDF, and 0.81% SDF. The observed GCV of 5,307 kcal/kg and AFT of 1225 °C for the optimized blend were within 1% of the model predicted values, thereby establishing the robustness of the models. The findings from this study can foster sustainable economic development and zero CO2 emission objectives by optimizing the utilization of waste materials without compromising the GCV and AFT of the mixed fuels in coal-fired power plants.

Characterization of refuse derived fuel samples prepared from municipal solid waste in Vellore, India.

The objective of this work was to explore an alternative way to manage the non-biodegradable and non-recyclable fraction of municipal solid waste (MSW) in Vellore city, India. Refuse-derived fuel (RDF) samples with different proportions of plastic, thermocol, foam and jute straw were formulated. The RDF samples were characterized in the form of heating values (proximate and ultimate analysis), surface properties through X-ray diffraction (XRD) and thermal stability through thermogravimetric analysis (TGA). The measured higher heating values (HHV) of four RDF samples varying between 6032 and 6168 kcal/kg were effectively modelled using various empirical models for the prediction of HHV based on their elemental analysis. Higher absolute weight loss in TGA was evident in samples with jute straw while the highest rate of weight loss was noted in samples with a higher proportion of thermocol. Results from this preliminary investigation of RDF samples prepared from non-biodegradable and non-recyclable fractions of MSW warrants an exhaustive analysis of a larger pool of samples to project appropriate RDF composition for better energy recovery.

Assessment of groundwater geochemistry using multivariate water quality index and potential health risk in industrial belt of central Odisha, India.

Groundwater in India has been shown to have a variety of water quality issues, including fluoride, nitrate, and uranium pollution, all of which pose a health risk to humans. In the present study, a total of 106 groundwater samples from the Angul district of Odisha, an industrialized region in India, were analyzed for 14 different hydrochemical parameters. In almost 30%, 34.9%, and 4.7% of the groundwater samples, the concentrations of F-, NO3- and uranium, respectively, exceeded the permissible limit set by WHO. In addition to the fixed-weight groundwater quality index (GWQI), the entropy-weighted water quality index (EWQI), the principal component analysis (PCA) factor (or rotated factor) loading based water quality index (PCWQI) and human health risk assessment were used. Depending on the models, about 19.1 ± 0.9%, 70.5 ± 1.9% and 10.38 ± 1.9% of water samples were classified as "Excellent", "Good" and "Medium" quality, respectively, across four water quality indexes with a nominal rating disagreement of 11.3%. More than 90% of samples are unanimously classified as excellent or good across the WQI rating. For children and adults, approximately 54.7% and 24.5% of samples exceeded the permitted limit for F-, (hazard quotient HQ > 1), posing non-carcinogenic health hazards, respectively. In contrast, 71.7% and 34.9% of NO3- samples respectively, surpassed the allowed limit and caused non-carcinogenic health concerns for children and adults. In terms of carcinogenic HQ values, about 13.2% and 7.5% of samples exhibit an uranium related carcinogenic health risk in children and adults, respectively. The existence of significant amounts of Cl -, NO3-, and especially HCO3- ions in groundwater in some samples, as well as their positive interdependence, may increase uranium pollution in the future through uranium dissolution.

Dynamic modelling of tetrazolium-based microbial toxicity assay-a parametric proxy of traditional dose-response relationship.

Microbial toxicity of test substances in tetrazolium assay is often quantified while referring to their IC50 values. However, the implication of such an estimate is very limited and can differ across studies depending on prevailing test conditions. In this work, a factorial design-based end-point microbial toxicity assay was performed, which suggests a significant interaction (P= 0.041) between inoculum and tetrazolium dose on formazan production. Subsequently, a dynamic model framework was utilized to capture the nonlinearities in biomass, substrate, formazan profiles and to project the toxicant inhibition parameter as a robust alternative to IC50 value. Microbial growth, glucose uptake and formazan production in the presence or absence of toxicant (Cu2+) from designed batch experiments were used for sequential estimation of model parameters, and their confidence intervals. A logistic growth model with multiplicative inhibition terms for formazan content and toxicant concentration fits the experimental data reasonably well (R2>0.96). Dynamic relative sensitivity analysis revealed that both microbial growth and formazan production profiles were sensitive to toxicant inhibition parameter. The modelling framework not only provides a better insight into the underlying toxic effect but also offers a stable toxicity index for the test substances that can be extended to design a versatile, robust in vitro assay system.

Process optimization, metabolic engineering interventions and commercialization of microbial polyhydroxyalkanoates production - A state-of-the art review.

BACKGROUND: Microbial polyhydroxyalkanoates (PHAs) produced using renewable resources could be the best alternative for conventional plastics. Despite their incredible potential, commercial production of PHAs remains very low. Nevertheless, sincere attempts have been made by researchers to improve the yield and economic viability of PHA production by utilizing low-cost agricultural or industrial wastes. In this context, the use of efficient microbial culture or consortia, adoption of experimental design to trace ideal growth conditions, nutritional requirements, and intervention of metabolic engineering tools have gained significant attention. PURPOSE AND SCOPE: This review has been structured to highlight the important microbial sources for PHA production, use of conventional and non-conventional substrates, product optimization using experimental design, metabolic engineering strategies, and global players in the commercialization of PHA in the past two decades. The challenges about PHA recovery and analysis have also been discussed which possess indirect hurdle while expanding the horizon of PHA-based bioplastics. SUMMARY: Selection of appropriate microorganism and substrate plays a vital role in improving the productivity and characteristics of PHAs. Experimental design-based bioprocess, use of metabolic engineering tools, and optimal product recovery techniques are invaluable in this dimension. CONCLUSION: Optimization strategies, which are being explored in isolation, need to be logically integrated for the successful commercialization of microbial PHAs.