On the use of electronegativity and electron affinity based pseudo-molecular field descriptors in developing correlations for quantitative structure-activity relationship modeling of drug activities

For quantitative structure-activity relationship (QSAR) modeling in ligand-based drug discovery programs, pseudo-molecular field (PMF) descriptors using intrinsic atomic properties, namely, electronegativity and electron affinity are studied. In combination with partial least squares analysis and Procrustes transformation, these PMF descriptors were employed successfully to develop correlations that predict the activities of target protein inhibitors involved in various diseases (cancer, neurodegenerative disorders, HIV, and malaria). The results show that the present QSAR approach is competitive to existing QSAR models. In order to demonstrate the use of this algorithm, we present results of screening naturally occurring molecules with unknown bioactivities. The pIC₅₀ predictions can screen molecules that have desirable activity before assessment by docking studies.

A methodology of evaluating sustainability index of a biomass processing enterprise: a case study of native cow dung-urine biorefinery

Agriculture and its allied sector contribute significantly to the gross domestic product of every country. Several small-scale enterprises engaged in waste biomass processing have been setup recently. Such industrial setups not only help in solving the waste management issues but also play an important role in offering employment at the grass root level generating a significant social impact along with economic advantage to the local entrepreneur. Hence, assessment of such biomass processing enterprise (BPE) based on economic, environment, and social parameters has become necessary. In this paper, a general framework for sustainability assessment is discussed using a case study of cow dung-urine biorefinery as a representative BPE. Real-time data of BPE has been collected for evaluation and a sustainability index (SI) is evaluated using multicriteria decision method. The SI is calculated as per the weightage assigned and value function of the indicator and criteria. The SI for the BPE was observed to be 0.69 for the chosen set of criteria and indicator and weightages. A sensitivity analysis has been performed to check the dependence of the results on the weightages assigned to various criteria and indicators. It was also observed that the results were more sensitive to the indicators having a low value function.

Process of fruit peel waste biorefinery: a case study of citrus waste biorefinery, its environmental impacts and recommendations

Fruit peels are a rich source of cellulose, hemicellulose, phenolic compounds, and terpenic compounds. Thus, they have the potential to be a novel renewable, sustainable, and low-cost raw material (source) for the production of several value-added products based on framework and concepts such as waste hierarchy that includes biofertilizers, dietary fiber, animal feed, industrial enzymes, substrate for the bioactive compounds production, synthesis of nanomaterials, and clean energy (from residual biomass). With a view of evaluating the environmental burden of biorefinery, a life cycle assessment (LCA) is performed for a representative citrus waste (CW) biorefinery. The functional unit used for LCA was set as 2500 kg of CW processed. The overall GWP was observed to be 937.3 kg CO₂ equivalent per 2500 kg of CW processed. On further analysis of the environmental impact, it was found that different steps contributed significantly, as shown by the various environmental indicator values. Alternative advanced process intensification technologies like microwave and ultrasound-assisted steps replacing the conventional steps when implemented show considerable reduction in environmental indicator values. The variations in the contribution to environmental indicators should be considered during the design and process selection of biorefineries.

Process development of silica extraction from RHA: a cradle to gate environmental impact approach

India is one of the major rice-producing countries. Rice husk is a major agricultural by-product from rice production, which is used as a fuel in boilers. Its use as fuel produces huge amounts of silica-rich rice husk ash (RHA). This paper aims at providing an overall assessment of environmental impacts associated with the extraction of silica from RHA-a process developed by our study group. The functional unit used in this study is production of 100 kg of silica. The analysis included the extraction and transportation of other raw materials; RHA was assumed to be processed at the site. The study was conducted in accordance with the international ISO 14040 procedural framework. LCA is performed using GaBi Education software, and five midpoint indicators are chosen to assess the environmental impacts of silica extraction. The overall climate change (CC) of the extraction process is 7.26 kg CO₂ equivalent per kg of silica produced. A high contribution of calcination to CC is attributed to the use of electricity. The comprehensive environmental impacts of silica-rich RHA resulting from processing of RHA and improvement options to achieve sustainable production are presented. The negative impacts that can be avoided during silica extraction are also discussed. It is observed that calcination is a major contributor to the overall environmental indicators. The work also stresses on the use of renewable energy for electricity generation, which would help in decreasing the overall greenhouse gas emissions during extraction while ensuring waste utilization.

Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data

Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient(CC) of 0.893, low root mean square error (RMSE) and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a)a predictor of the binding energy of cancer related PPI complexes, and (b)a classifier for discriminating PPI complexes related to cancer from those of other diseases.

Characterization and valorization of biomass ashes

In India, farming is the primary source of income for many families. Following each harvest, a huge amount of biomass is generated. These are generally discarded as "agrowaste," but recent reports have indicated several beneficial uses for these biomasses and their ashes. However, before the utilization of biomass ashes (BMAs), their chemical and physical properties need to be investigated (characterized) so as to utilize their potential benefit to the fullest. In this paper, eight different biomass ashes (soybean plant ash, mustard plant ash, maize ash, groundnut plant ash, cotton plant ash, wheat plant ash, pigeon peas ash, and groundnut shell ash) were characterized, and their chemical properties are discussed. Surface chemical composition analysis, proximate analysis, and ultimate analysis were performed on all BMA samples, and properties such as porosity, particle density, bulk density, point of zero charge, BET surface area, water-absorption capacity, and bulk parameters such as surface pH and surface charges were determined. BMAs were characterized by SEM and FTIR. The surface areas of biomass ashes vary from 1.9 to 46 m²/g, and point of zero charge for all BMAs exceed 9.8, which confirmed the alkaline nature of these samples. Based on the chemical composition, BMAs are categorized into four types (S, C, K, and CK), and their utilization is proposed based on the type. BMAs find applications in agriculture and construction industries; glass, rubber, and zeolite manufacturing; and in adsorption (as a source of silica/zeolites). The paper also discusses the research challenges and opportunities in utilization of BMAs.

Mustard plant ash: a source of micronutrient and an adsorbent for removal of 2,4-dichlorophenoxyacetic acid

The work highlights the utilization of an agricultural waste mustard plant ash (MPA) as a soil additive and an adsorbent. MPA was characterized by X-ray fluorescence (XRF), energy-dispersive X-ray spectroscopy (EDX), proximate analysis, CHNS analysis, Brunauer-Emmett-Teller (BET) surface area analysis, zeta potential measurements, Fourier transform infrared (FTIR), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). XRF analysis confirmed the presence of CaO (31.35 %), K₂O (18.55 %), and P₂O₅ (6.99 %), all of which act as micronutrients to plants. EDX also confirms high amount of elemental O, Ca, K, and P. The adsorptive ability of MPA was investigated using a commonly used herbicide, 2,4-dichlorophenoxyacetic acid (2,4-D), as a representative chemical. Batch adsorption experiments were conducted to study the effect of different operational parameters such as adsorbent dose, initial 2,4-D concentration, contact time, and temperature on the adsorption process. Data from experiments were fitted to various kinetic and isothermal models. The pseudo-second-order kinetic model was found to show the best fit (R ² > 0.99), with the highest k ₂ value of the order 10⁵. Based on the study results, dosage of MPA/hectare for different crops has been recommended for effective removal of 2,4-D. To our knowledge, this is the first study in which MPA has been characterized in detail and investigated for dual applications (as an adsorbent and as a soil additive).

Agro-industrial waste: a low cost adsorbent for effective removal of 4-chloro-2-methylphenoxyacetic acid herbicide in batch and packed bed modes

The present work describes the aqueous phase removal of 4-chloro-2-methylphenoxyacetic acid herbicide by rice husk ash (RHA) using batch and packed bed adsorption techniques. The effects of dosage, initial concentration, time, pH, temperature, and particle size of adsorbent in batch compared with effects of influent concentration, flow rate, and bed height in packed bed were studied. The particle size effect reveals that the removal is dependent on chemical composition (silica and carbon content) together with BET surface area of RHA. The aptness of Langmuir isotherm to batch data indicates the favorable adsorption whereas that of Temkin isotherm informs the heterogeneous nature of RHA. The kinetics of adsorption follows the pseudo-second order and Elovich models while thermodynamics of process indicates the exothermic adsorption. Among the models applied in packed bed study, the deactivation kinetic, Yoon-Nelson and bed depth service time (BDST) models are suitable to explain the packed bed adsorption. The adsorption capacity of RHA in packed bed study is found greater than that in batch. The adsorption capacity of RHA determined by the BDST model is 3019 mg/L for 90 % saturation of bed. The adsorption capacity of RHA based on weight is ∼2.3 times and that based on surface area is ∼55.55 times greater than that of granular activated carbon.

Role of Chemical Reactivity and Transition State Modeling for Virtual Screening

Every drug discovery research program involves synthesis of a novel and potential drug molecule utilizing atom efficient, economical and environment friendly synthetic strategies. The current work focuses on the role of the reactivity based fingerprints of compounds as filters for virtual screening using a tool ChemScore. A reactant-like (RLS) and a product- like (PLS) score can be predicted for a given compound using the binary fingerprints derived from the numerous known organic reactions which capture the molecule-molecule interactions in the form of addition, substitution, rearrangement, elimination and isomerization reactions. The reaction fingerprints were applied to large databases in biology and chemistry, namely ChEMBL, KEGG, HMDB, DSSTox, and the Drug Bank database. A large network of 1113 synthetic reactions was constructed to visualize and ascertain the reactant product mappings in the chemical reaction space. The cumulative reaction fingerprints were computed for 4000 molecules belonging to 29 therapeutic classes of compounds, and these were found capable of discriminating between the cognition disorder related and anti-allergy compounds with reasonable accuracy of 75% and AUC 0.8. In this study, the transition state based fingerprints were also developed and used effectively for virtual screening in drug related databases. The methodology presented here provides an efficient handle for the rapid scoring of molecular libraries for virtual screening.

Inverse problem studies of biochemical systems with structure identification of S-systems by embedding training functions in a genetic algorithm

An efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities.

Valorization of banana peel: a biorefinery approach

Banana peel (BP) is an agrowaste produced in large volumes annually, especially by food-processing industries; however, its disposal is of significant concern. However, recent research suggests that BP is a valuable source of bioactive compounds, which can be converted into value-added products. This article reviews the conversion process of value-added products from BP and provides an outline on the chemical composition of BP and its possible applications. In addition, we also discuss the utilization of BP as a substrate to produce animal feed, biofertilizer, dietary fibers, clean energy, industrial enzymes, as well as its use in the synthesis of nanomaterials. Based on the research conducted so far, it is obvious that BP has a wide variety of applications, and thus, developing a biorefinery approach to adequately utilize BP will help realize its economic benefits to the fullest. Based on the valorization of BP, a scheme for BP biorefinery has been proposed. A material balance for BP biorefinery for 1-ton bone-dry BP is presented and our results show that 432 kg of protein or 170 kg of citric acid, 170 kg of pectin, 325 m

Conversion of dried Aspergillus candidus mycelia grown on waste whey to biodiesel by in situ acid transesterification

This study reports optimization of the transesterification reaction step on dried biomass of an oleaginous fungus Aspergillus candidus grown on agro-dairy waste, whey. Acid catalyzed transesterification was performed and variables affecting esterification, viz., catalyst methanol and chloroform concentrations, temperature, time, and biomass were investigated. Statistical optimization of the transesterification reaction using Plackett-Burman Design showed biomass to be the predominant factor with a 12.5-fold increase in total FAME from 25.6 to 320mg. Studies indicate that the transesterification efficiency in terms of conversion is favored by employing lower biomass loadings. A. candidus exhibited FAME profiles containing desirable saturated (30.2%), monounsaturated (31.5%) and polyunsaturated methyl esters (38.3%). The predicted and experimentally determined biodiesel properties (density, kinematic viscosity, iodine value, cetane number, TAN, water content, total and free glycerol) were in accordance with international (ASTM D6751, EN 14214) and national (IS 15607) standards.

Prediction of Reactivity Ratios in Free Radical Copolymerization from Monomer Resonance–Polarity (Q–e) Parameters: Genetic Programming-Based Models

The principal deficiency of the widely utilized Alfrey–Price (AP) scheme for computing reactivity ratios in the widely used free radical copolymerization is that it ignores important factors, such as the steric effects. This often leads to inaccurate reactivity ratio predictions by AP model. Accordingly, in this study, exclusively data-driven, Q–e parameter-based new models have been developed for the reactivity ratio prediction in free radical copolymerization. In the model development, a novel artificial intelligence formalism known as “genetic programming (GP)” that performs symbolic regression has been employed. The GP-based models possess a different functional form than AP model. Further, parameters of GP-based models were fine-tuned using Levenberg–Marquardt (LM) nonlinear regression method. A comparison of AP, GP and GP-LM as well as artificial neural network (ANN)-based models indicates that GP and GP-LM models exhibit superior reactivity ratio prediction accuracy and generalization performance (with correlation coefficient magnitudes close to or greater than 0.9) when compared with AP and ANN models. The GP-based reactivity ratio prediction models developed here due to their higher accuracy and generalization capability have the potential of replacing the widely used AP models.

Fruit peel waste as a novel low-cost bio adsorbent

Fruit peel waste (FPW) is abundantly available from the agricultural and food processing industry and has been studied in recent past as an adsorbent. This paper critically reviews the reported work and investigates various FPW-pollutant systems. The study includes statistics of FPW generation, modification, characterization, adsorption ability, recovery/regeneration, and modeling (isotherms, kinetics, and thermodynamics) of batch adsorption. It is found that orange and banana peels are the most extensively studied adsorbents, whereas Pb

Efficient lipase purification using reverse micellar extraction

Reverse micellar extraction (RME) of enzyme provides an attractive option for conventional method with the potential to achieve purification and concentration in a single step with high yield. This study presents a methodology for optimization of RME with Pseudomonas lipase as model system. Fold-purification, percent recovery and extraction time were the objective functions while the type and concentration of surfactant, contact time, pH, ionic strength, and the ratio of organic to aqueous phase were the decision variables. Under optimized conditions, the AOT (Aerosol OT (bis 2-ethylhexyl) sodium sulfosuccinate)-isooctane system gave a 15-fold purification, 80% recovery and 2.5-fold concentration of the Pseudomonas lipase with process time of 45 min.

Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence

Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

Sulfur Nanoparticles Synthesis and Characterization from H2S Gas, Using Novel Biodegradable Iron Chelates in W/O Microemulsion

Sulfur nanoparticles were synthesized from hazardous H₂S gas using novel biodegradable iron chelates in w/o microemulsion system. Fe³⁺–malic acid chelate (0.05 M aqueous solution) was studied in w/o microemulsion containing cyclohexane, Triton X-100 andn-hexanol as oil phase, surfactant, co-surfactant, respectively, for catalytic oxidation of H₂S gas at ambient conditions of temperature, pressure, and neutral pH. The structural features of sulfur nanoparticles have been characterized by X-ray diffraction (XRD), transmission electron microscope (TEM), energy dispersive spectroscopy (EDS), diffused reflectance infra-red Fourier transform technique, and BET surface area measurements. XRD analysis indicates the presence of α-sulfur. TEM analysis shows that the morphology of sulfur nanoparticles synthesized in w/o microemulsion system is nearly uniform in size (average particle size 10 nm) and narrow particle size distribution (in range of 5–15 nm) as compared to that in aqueous surfactant systems. The EDS analysis indicated high purity of sulfur (>99%). Moreover, sulfur nanoparticles synthesized in w/o microemulsion system exhibit higher antimicrobial activity (against bacteria, yeast, and fungi) than that of colloidal sulfur.

Immobilization of Candida rugosa lipase on poly(allyl glycidyl ether-co-ethylene glycol dimethacrylate) macroporous polymer particles

Macroporous polymer particles containing surface epoxy groups were synthesized for immobilization of Candida rugosa lipase (CRL). The effect of incorporation of two different sets of monomers [allyl glycidyl ether (AGE) and glycidyl methacrylate (GMA)] and the effect of crosslinking density on immobilization of lipase were studied. AGE-co-EGDM polymers gave higher binding and expression of lipase than GMA-co-EGDM polymers. Optimization of immobilization parameters was done with respect to immobilization time and enzyme loading. Amongst AGE-co-EGDM polymer series, AGE-150 polymer found to give maximum lipase activity yield and therefore evaluated for temperature, pH and storage stability. Under optimum conditions, AGE-150 polymer gave 78.40% of activity yield. Immobilized lipase on AGE-150 showed a broader pH, higher temperature and excellent storage stability.

Nadi Tarangini: a pulse based diagnostic system

Ayurveda is a traditional medicine and natural healing system in India. Nadi-Nidan (pulse-based diagnosis) is a prominent method in Ayurveda, and is known to dictate all the salient features of a human body. In this paper, we provide details of our procedure for obtaining the complete spectrum of the nadi pulses as a time series. The system Nadi Tarangini1 contains a diaphragm element equipped with strain gauge, a transmitter cum amplifier, and a digitizer for quantifying analog signal. The system acquires the data with 16-bit accuracy with practically no external electronic or interfering noise. Prior systems for obtaining the nadi pulses have been few and far between, when compared to systems such as ECG. The waveforms obtained with our system have been compared with these other similar equipment developed earlier, and is shown to contain more details. The pulse waveform is also shown to have the desirable variations with respect to age of patients, and the pressure applied at the sensing element. The system is being evaluated by Ayurvedic practitioners as a computer-aided diagnostic tool.

Arterial pulse system: modern methods for traditional Indian medicine

Ayurveda is one of the most comprehensive healing systems in the world and has classified the body system according to the theory of Tridosha to overcome ailments. Diagnosis similar to the traditional pulse-based method requires a system of clean input signals, and extensive experiments for obtaining classification features. In this paper we briefly describe our system of generating pulse waveforms and use various feature detecting methods to show that an arterial pulse contains typical physiological properties. The beat-to-beat variability is captured using a complex B-spline mother wavelet based peak detection algorithm. We also capture--to our knowledge for the first time--the self-similarity in the physiological signal, and quantifiable chaotic behavior using recurrence plot structures.

Exhaustive Analysis of Frontal Copolymerization of Functionalized Monovinyl and Divinyl Monomers

A series of copolymers of 2-hydroxyethyl methacrylate (HEMA)/glycidyl methacrylate (GMA) and ethylene dimethacrylate (EGDM) were synthesized by frontal polymerization (FP). This study was conducted to investigate the effect of crosslink density, type and concentration of initiator, the use of a complex initiator system, porogen, and diluent on the most relevant parameters of FP, such as sustainability of the front, temperature profile, front velocity, and yield. The products were also characterized for intruded pore volume, pore-size distribution, epoxy-functionality number, and surface morphology. Higher crosslink densities (CLDs) and initiator concentration produced higher front velocities, whereas no trend in front temperature was noted. A complex initiation system was effective in stabilizing and increasing the polymerization yield. Relative to suspension polymerization (SP), FP products synthesized without a solvent were microporous, whereas micro-to-macroporous products were obtained in the presence of a solvent (for HEMA-EGDM polymers). We also present, explain, and discuss the exotic patterns observed under a microscope. We observed two basic types of spatial patterns, namely, planar and nonplanar patterns. The type of planar pattern observed under scanning electron microscopy (SEM) has a spatial impulse that appears as a loop followed by regular periodic motion in the radial and axial directions. This behavior gives rise to a repeating pattern that is a few microns thick. Also, nonplanar patterns, namely, layered concentric rings and winding staircase patterns, were observed under SEM.

Isometric graphing and multidimensional scaling for reaction-diffusion modeling on regular and fractal surfaces with spatiotemporal pattern recognition

Heterogeneous surface reactions exhibiting complex spatiotemporal dynamics and patterns can be studied as processes involving reaction-diffusion mechanisms. In many realistic situations, the surface has fractal characteristics. This situation is studied by isometric graphing and multidimensional scaling (IGMDS) of fractal surfaces for extracting geodesic distances (i.e., shortest scaled distances that obtain edges of neighboring surface nodes and their interconnections) and the results obtained used to model effects of surface diffusion with nonlinear reactions. Further analysis of evolved spatiotemporal patterns may be carried out by IGMDS because high-dimensional snapshot data can be efficiently projected to a transformed subspace with reduced dimensions. Validation of the IGMDS methodology is carried out by comparing results with reduction capabilities of conventional principal component analysis for simple situations of reaction and diffusion on surfaces. The usefulness of the IGMDS methodology is shown for analysis of complex patterns formed on both regular and fractal surfaces, and using generic nonlinear reaction-diffusion systems following FitzHugh Nagumo and cubic reaction kinetics. The studies of these systems with nonlinear kinetics and noise show that effects of surface disorder due to fractality can become very relevant. The relevance is shown by studying properties of dynamical invariants in IGMDS component space, viz., the Lyapunov exponents and the KS entropy for interesting situations of spiral formation and turbulent patterns.