Out-of-time-ordered correlator in the one-dimensional Kuramoto-Sivashinsky and Kardar-Parisi-Zhang equations.

The out-of-time-ordered correlator (OTOC) has emerged as an interesting object in both classical and quantum systems for probing the spatial spread and temporal growth of initially local perturbations in spatially extended chaotic systems. Here, we study the (classical) OTOC and its "light cone" in the nonlinear Kuramoto-Sivashinsky (KS) equation, using extensive numerical simulations. We also show that the linearized KS equation exhibits a qualitatively similar OTOC and light cone, which can be understood via a saddle-point analysis of the linearly unstable modes. Given the deep connection between the KS (deterministic) and the Kardar-Parisi-Zhang (KPZ, which is stochastic) equations, we also explore the OTOC in the KPZ equation. While our numerical results in the KS case are expected to hold in the continuum limit, for the KPZ case it is valid in a discretized version of the KPZ equation. More broadly, our work unravels the intrinsic interplay between noise/instability, nonlinearity, and dissipation in partial differential equations (deterministic or stochastic) through the lens of OTOC.

Graphene-Enhanced Ion Transport in Dual-Conducting Composite Films of Polyacetylene and an Imidazolium Iodide Ionic Liquid.

Dual-conducting polymer films were synthesized by dispersing graphene in an aqueous solution of poly(vinyl alcohol) and 1-propyl-3-methylimidazolium iodide ([C3mim]I) ionic liquid and thermally converting the poly(vinyl alcohol) to polyene in the presence of hydroiodic acid catalyst. The electrical and mechanical properties of the resulting free-standing films of the nanocomposite, containing different concentrations of graphene, were analyzed using electrochemical impedance spectroscopy (EIS) and dynamic mechanical analysis (DMA), respectively. Nyquist plots (imaginary vs real components of the frequency-dependent impedance) showed two characteristic arcs representing the composite's electronic and ionic conduction pathways. The conductivity values corresponding to both charge transport mechanisms increased with temperature and the graphene concentration. The enhancement in electronic conductivity is expected because of graphene's high electron mobility. Interestingly, ionic conductivity also showed a significant increase with graphene concentration, approximately triple the extent of the rise in the electronic conductivity, even though the loss and storage moduli of the films increased. (Generally, a higher modulus results in lower ionic conductivities in ionic gels.) Molecular dynamics simulations of the three-component system provided some insights into this unusual behavior. Mean square displacement data showed that the diffusion of the iodide anions was relatively isotropic. The iodide diffusion coefficient was higher in a blend with 5 vol % graphene than in blends with 3 vol % graphene or no graphene. The improvement is attributed to the interfacial effects of the graphene on the free volume of the blend. Furthermore, an exclusion of the iodide ions from the vicinity of graphene was observed in the radial distribution function analysis. The increase in the effective concentration of iodide due to this exclusion and the increase in its diffusion coefficient because of the excess free volume are the primary reasons for the observed enhancement in ionic conductivity by adding graphene.

A Decomposition Analysis to Understand the Wealth-Based Inequalities in Child Vaccination in Rural Southern Assam: A Cross-Sectional Study.

Background: The socio-environmental aspects of southern Assam reflect a general pattern of backwardness. Moreover, child healthcare resources in the region are inadequately used, leading to low vaccination coverage. Given this background, this paper attempted to comprehend wealth-based inequality in full vaccination in rural areas of southern Assam. Methodology: Based on a multistage cluster sampling approach, 360 children of 12-23 months were selected from the study area. To identify the predictors of a child, a non-linear model was estimated by using the generalized linear model (GLM) approach followed by Erreygers decomposition technique to quantify the wealth inequality in the obtained predictors in explaining the disparity in full vaccination. Result: The Bacillus Calmette-Guérin (BCG) vaccination recorded the highest vaccination coverage, at nearly 90% and the lowest was observed for the measles vaccine, around 61 percent. Slightly more than half of the eligible children (54 percent) were vaccinated against all the Universal Immunization Programme (UIP)-recommended vaccines. The decomposition analysis revealed that the occupation of the child's father, maternal age, birth order of the child, and health-seeking behavior such as antenatal care (ANC) were the prime factors related to inequality in full vaccination in the region. Conclusion: Vaccination coverage in the region has improved over time, however, full vaccination is concentrated towards the economically advantaged section of the society in rural southern Assam. Targeted, context-specific, and expanded government initiatives could aid in addressing the overall wealth-related full vaccination inequalities in the valley.

Comparative genomics reveals the diversity of CRISPR-Cas locus in Azotobacter organisms.

Clustered regularly interspaced short palindromic repeats (CRISPRs) are known to provide adaptive immunity to bacteria against invading bacteriophages. In recent years, CRISPR-based technologies have been used for creating improved plant varieties; however, the indigenous CRISPR-Cas elements of plant growth-promoting bacteria are usually neglected. These indigenous genetic cassettes have evolved over millions of years and have shaped the bacterial genome. Therefore, these genetic loci can be used to study the adaptive capability of the bacteria in the environment. This study aims to bioinformatically analyze the genomes of a common free-living nitrogen-fixing Azotobacter spp. to assess their CRISPR-Cas diversity. Strains of Azotobacter vinelandii and Azotobacter chroococcum were found to harbor a large number of spacers. The phylogeny of different Cas and Cse1 proteins revealed a close evolutionary relationship among A. chroococcum B3, A. chroococcum NCIMB 8003 locus II, and A. vinelandii DJ locus I. The secondary structure of the hairpin loop of the repeat was also analyzed, and a correlation was derived between the structural stability of the hairpin loop and the number of spacers acquired by the CRISPR loci. These findings revealed the diversity and evolution of the CRISPR sequences and Cas proteins in Azotobacter species. Although the adaptive immune system of bacteria against bacteriophage, CRISPR-Cas, has been identified in many bacteria, studies of plant growth-promoting bacteria have been neglected. These indigenous CRISPRs have shaped the genome over millions of years and their study can lead to the understanding of the genome composition of these organisms. Our results strengthen the idea of using A. chroococcum and A. vinelandii as biofertilizer strains as they possess more spacers with highly stable repeat sequences, thereby imparting them higher chance of survival against mobile genetic elements like phages and plasmids.

Revisiting the Use of Quantum Chemical Calculations in LogPoctanol-water Prediction.

The partition coefficients of drug and drug-like molecules between an aqueous and organic phase are an important property for developing new therapeutics. The predictive power of computational methods is used extensively to predict partition coefficients of molecules. The application of quantum chemical calculations is used to develop methods to develop structure-activity relationship models for such prediction, either based on molecular fragment methods, or via direct calculation of solvation free energy in solvent continuum. The applicability, merits, and shortcomings of these developments are revisited here.

Accumulation of Heavy Metals in Long-Evans Rat Through Feeding Fishes of Buriganga River and Their Histopathological Evaluation.

The study was conducted to evaluate the impacts of industrial discharges, including from tanneries, in terms of heavy metal loads in fin-fish of the Buriganga river, Dhaka, Bangladesh, and to find out the ultimate effects of feeding those heavy metal-contaminated fishes to Long-Evans rats to examine rats' heavy metal accumulation along with viable architectural alterations on various organs through histopathological screenings. Being the most edible by the populace, Labeo rohita was chosen for this purpose. Labeo rohita was collected from five different spots in the river close to the industrial outlets, covering three seasons: monsoon, winter, and summer throughout the year. Experiments unearthed that the deposition of heavy metals inside fishes always surpassed the standard margin, implying a pernicious aquatic milieu. The empirical data suggests that the mean concentrations of Cu, Pb, Cr, and Cd were higher than the permissible limits of WHO and FAO after feeding heavy metal-contaminated fishes for a period of 120 days. Seasonal variations were also taken into account, and it was noticed that rats fed with fish collected in winter had the highest heavy metal concentrations (P < 0.05). Significant architectural changes were observed during histopathological screening of rats' livers and kidneys characterized by necrosis, inflammation, and congestion. After gauging the obtained data, it might be concluded that consumption of heavy metal-contaminated fish entails accumulation of heavy metals, which causes histopathological changes in rats' livers and kidneys, including necrosis and severe structural deformational changes compared to those of control groups.

Understanding the Liquid States of Cyclic Hydrocarbons Containing N, O, and S Atoms via the 3D-RISM-KH Molecular Solvation Theory.

The 3D-reference interaction site model (3D-RISM) molecular solvation theory in combination with the Kovalenko-Hirata (KH) closure is extended to seven heterocyclic liquids to understand their liquid states and to test the performance of the theory in solvation free energy (SFE) calculations of solutes in select solvents. The computed solvent site distribution profiles were compared with the all-atom molecular dynamics (MD) simulations, showing comparable performances. The computational results were compared against the structural parameters for liquids, whenever available, as well as against the experimental SFEs. The liquids are found to have local ordered structures held together via weak interactions in both the RISM and MD simulations. The 3D-RISM-KH computed SFEs are in good agreement with the benchmark values for the tetrahydrothiophene-S,S-dioxide, and showed comparatively larger deviations in the case of the SFEs in the tetrahydrofuran continuum.

Polymerase chain reaction-based snake origin tracing in commercial venom crystals by targeting the mitochondrial D-loop.

Snake venom is a valuable raw material for numerous therapeutic formulations because of its life-saving pharmacological potential. However, due to their high price, fake "snake venoms" have captured a significant portion of the global market, and there is currently no reliable reported DNA-based method available for quickly distinguishing between fakes and originals. Therefore, in this study, a set of newly designed snake-specific universal primers targeting mitochondrial D-loop fragments were employed to detect snake origins in commercial venom crystals by only simplex polymerase chain reaction analysis. Under the optimal thermal cycling conditions, only the 145-149 bp snake-specific mitochondrial D-loop fragments from pure and mixed backgrounds were amplified by the newly designed primers. Specificity was achieved by confirming no DNA amplification occurred in the DNA admixture of ten different chordates, and universality by individual DNA amplification of nine different snakes. The primers that efficiently amplified the minimum mitochondrial DNA contained in a total of 10-2 ng in a 10.0 µl reaction were also successfully able to detect the snake origin in commercial cobra venom crystals. These findings suggest that the newly designed primers can be used to differentiate the original and fake commercial snake venom crystals in order to achieve the highest standards of snake venom-based medications through amplifying the snake-specific mitochondrial D-loop fragments.

Octanol-Water Partition Coefficients of Fluorinated Drug Molecules with Continuum Solvation Models.

In this work, we have examined the efficiency of continuum solvation models, used with density functional theory methods, in calculating octanol-water partition coefficients (logP) of 56 fluorine containing drug molecules. The solvation model based on density model computed logP values that are in good agreement with the benchmark values. The conductor-like polarizable continuum models computed results have issues in predicting correct trend, often with reversal of sign from benchmark. The choice of basis set does not show significant effect, and the selection of atomic radii affects geometry convergence during calculations.

Biomolecular Simulations with the Three-Dimensional Reference Interaction Site Model with the Kovalenko-Hirata Closure Molecular Solvation Theory.

The statistical mechanics-based 3-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) molecular solvation theory has proven to be an essential part of a multiscale modeling framework, covering a vast region of molecular simulation techniques. The successful application ranges from the small molecule solvation energy to the bulk phase behavior of polymers, macromolecules, etc. The 3D-RISM-KH successfully predicts and explains the molecular mechanisms of self-assembly and aggregation of proteins and peptides related to neurodegeneration, protein-ligand binding, and structure-function related solvation properties. Upon coupling the 3D-RISM-KH theory with a novel multiple time-step molecular dynamic (MD) of the solute biomolecule stabilized by the optimized isokinetic Nosé-Hoover chain thermostat driven by effective solvation forces obtained from 3D-RISM-KH and extrapolated forward by generalized solvation force extrapolation (GSFE), gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties were obtained in this new quasidynamics protocol. The multiscale OIN/GSFE/3D-RISM-KH algorithm was implemented in the Amber package and well documented for fully flexible model of alanine dipeptide, miniprotein 1L2Y, and protein G in aqueous solution, with a solvent sampling rate ~150 times faster than a standard MD simulation in explicit water. Further acceleration in computation can be achieved by modifying the extent of solvation layers considered in the calculation, as well as by modifying existing closure relations. This enhanced simulation technique has proven applications in protein-ligand binding energy calculations, ligand/solvent binding site prediction, molecular solvation energy calculations, etc. Applications of the RISM-KH theory in molecular simulation are discussed in this work.

Cloning and high-level expression of monomeric human superoxide dismutase 1 (SOD1) and its interaction with pyrimidine analogs.

Superoxide dismutase 1 (SOD1) is known to be involved in the pathogenesis of Amyotrophic Lateral Sclerosis (ALS) and is therefore considered to be an important ALS drug target. Identifying potential drug leads that bind to SOD1 and characterizing their interactions by nuclear magnetic resonance (NMR) spectroscopy is complicated by the fact that SOD1 is a homodimer. Creating a monomeric version of SOD1 could alleviate these issues. A specially designed monomeric form of human superoxide dismutase (T2M4SOD1) was cloned into E. coli and its expression significantly enhanced using a number of novel DNA sequence, leader peptide and growth condition optimizations. Uniformly 15N-labeled T2M4SOD1 was prepared from minimal media using 15NH4Cl as the 15N source. The T2M4SOD1 monomer (both 15N labeled and unlabeled) was correctly folded as confirmed by 1H-NMR spectroscopy and active as confirmed by an in-gel enzymatic assay. To demonstrate the utility of this new SOD1 expression system for NMR-based drug screening, eight pyrimidine compounds were tested for binding to T2M4SOD1 by monitoring changes in their 1H NMR and/or 19F-NMR spectra. Weak binding to 5-fluorouridine (FUrd) was observed via line broadening, but very minimal spectral changes were seen with uridine, 5-bromouridine or trifluridine. On the other hand, 1H-NMR spectra of T2M4SOD1 with uracil or three halogenated derivatives of uracil changed dramatically suggesting that the pyrimidine moiety is the crucial binding component of FUrd. Interestingly, no change in tryptophan 32 (Trp32), the putative receptor for FUrd, was detected in the 15N-NMR spectra of 15N-T2M4SOD1 when mixed with these uracil analogs. Molecular docking and molecular dynamic (MD) studies indicate that interaction with Trp32 of SOD1 is predicted to be weak and that there was hydrogen bonding with the nearby aspartate (Asp96), potentiating the Trp32-uracil interaction. These studies demonstrate that monomeric T2M4SOD1 can be readily used to explore small molecule interactions via NMR.

Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?

The parallel artificial membrane permeability assay (PAMPA), a non-cellular lab-based assay, is extensively used to measure the permeability of pharmaceutical compounds. PAMPA experiments provide a working mimic of a molecule passing through cells and PAMPA values are widely used to estimate drug absorption parameters. There is an increased interest in developing computational methods to predict PAMPA permeability values. We developed an in silico model to predict the permeability of compounds based on the PAMPA assay. We used the three-dimensional reference interaction site model (3D-RISM) theory with the Kovalenko-Hirata (KH) closure to calculate the excess chemical potentials of a large set of compounds and predicted their apparent permeability with good accuracy (mean absolute error or MAE = 0.69 units) when compared to a published experimental data set. Furthermore, our in silico PAMPA protocol performed very well in the binary prediction of 288 compounds as being permeable or impermeable (precision = 94%, accuracy = 93%). This suggests that our in silico protocol can mimic the PAMPA assay and could aid in the rapid discovery or screening of potentially therapeutic drug leads that can be delivered to a desired tissue.

Assessment of Heavy Metal Concentration in Water, Sediment and Common Fish Species of Dhaleshwari River in Bangladesh and their Health Implications.

The present study emphasized on evaluating the extent of pollution of Dhaleshwari River in Bangladesh due to the discharge of heavy metals from tanneries and other industries along with the health risks associated with the consumption of the heavy metals accumulated fish. For this purpose, three spots of Dhaleshwari River which are in the vicinity of the industrial outlet were selected for evaluating the seasonal status of heavy metals in water, sediment, and organs of three common fish species. Average concentrations of metals in water and sediment were in the order of Cr > Cd > Pb > Cu > As and Cr > Pb > Cu > As > Cd respectively. The average HM concentrations in water and sediment exceeded WHO and USEPA standards suggesting serious pollution to the aquatic environment. In fish organs, metal concentrations were in the order of Cu > Cr > Pb > Cd > As. Accumulation was highest in gills and lowest in muscles. Fish muscles had a relatively higher concentration of heavy metals (except As) exceeding the safe limits of FAO and WHO. Seasonal variation was also observed in water for all metals (p < 0.01), in sediment for Cu and As (p < 0.05), and in fish for Cr, Cd, and Cu (p < 0.05); higher concentrations were observed in winter. Bioconcentration factor analysis indicated that Cu, Pb, and Cr were more concentrated in fish. Health risk assessment reveals that the carcinogenic risk of Cr is associated with the consumption of contaminated fish species of the studied area.

Application of the Approximate 3D-Reference Interaction Site Model (RISM) Molecular Solvation Theory to Acetonitrile as Solvent.

The integral equation formalism based reference interaction site model (RISM) molecular solvation theory is applied to pure liquid acetonitrile and water-acetonitrile binary mixtures of different compositions. Solvate formation of d- and f-block ions by ACN is also calculated to check applicability of the RISM theory. The generalized Amber force field (GAFF) and the universal force field (UFF) parameters were found to be suitable for applications with the RISM theory for acetonitrile solvent. The presence of local microsolvated clusters for water-acetonitrile mixtures is validated in the RISM calculations.

One-dimensional Kardar-Parisi-Zhang and Kuramoto-Sivashinsky universality class: Limit distributions.

Tracy-Widom and Baik-Rains distributions appear as universal limit distributions for height fluctuations in the one-dimensional Kardar-Parisi-Zhang (KPZ) stochastic partial differential equation (PDE). We obtain the same universal distributions in the spatiotemporally chaotic, nonequilibrium, but statistically steady state of the one-dimensional Kuramoto-Sivashinsky (KS) deterministic PDE, by carrying out extensive pseudospectral direct numerical simulations to obtain the spatiotemporal evolution of the KS height profile h(x,t) for different initial conditions. We establish, therefore, that the statistical properties of the one-dimensional (1D) KS PDE in this state are in the 1D KPZ universality class.

Bioremediation of malachite green dye by two bacterial strains isolated from textile effluents.

Globally, water pollution from the textile industries is an alarming issue. Malachite Green dye of the triphenylmethane group is an extensively used dye in the fabric industries that is emitted through textile wastewater. This study aimed to isolate and characterize potential Malachite Green (MG) dye degrading bacteria from textile effluents. Different growth and culture parameters such as temperature, pH and dye concentration were optimized to perform the dye-degradation assay using different concentrations of MG dye in the mineral salt medium. A photo-electric-colorimeter was used to measure the decolorizing activity of bacteria at different time intervals after aerobic incubation. Two potential bacterial strains of Enterobacter spp. CV-S1 (accession no: MH450229) and Enterobacter spp. CM-S1 (accession no: MH447289) were isolated from textile effluents exhibiting potential MG dye decoloring efficiency. Further, the RAPD analysis and 16S rRNA sequencing confirmed the genetic differences of the isolated strains. Enterobacter sp CV-S1 and Enterobacter sp CM-S1 can completely decolor MG dye up to 15 mg/L under shaking condition without any requirement of sole carbon source. Thus, these two bacteria have the potency to be utilized in the textile wastewater treatment plant.

Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors.

Development of novel in silico methods for questing novel PgP inhibitors is crucial for the reversal of multi-drug resistance in cancer therapy. Here, we report machine learning based binary classification schemes to identify the PgP inhibitors from non-inhibitors using molecular solvation theory with excellent accuracy and precision. The excess chemical potential and partial molar volume in various solvents are calculated for PgP± (PgP inhibitors and non-inhibitors) compounds with the statistical-mechanical based three-dimensional reference interaction site model with the Kovalenko-Hirata closure approximation (3D-RISM-KH molecular theory of solvation). The statistical importance analysis of descriptors identified the 3D-RISM-KH based descriptors as top molecular descriptors for classification. Among the constructed classification models, the support vector machine predicted the test set of Pgp± compounds with highest accuracy and precision of ~ 97% for test set. The validation of models confirms the robustness of state-of-the-art molecular solvation theory based descriptors in identification of the Pgp± compounds.

The role of hydration effects in 5-fluorouridine binding to SOD1: insight from a new 3D-RISM-KH based protocol for including structural water in docking simulations.

Misfolded Cu/Zn superoxide dismutase enzyme (SOD1) shows prion-like propagation in neuronal cells leading to neurotoxic aggregates that are implicated in amyotrophic lateral sclerosis (ALS). Tryptophan-32 (W32) in SOD1 is part of a potential site for templated conversion of wild type SOD1. This W32 binding site is located on a convex, solvent exposed surface of the SOD1 suggesting that hydration effects can play an important role in ligand recognition and binding. A recent X-ray crystal structure has revealed that 5-Fluorouridine (5-FUrd) binds at the W32 binding site and can act as a pharmacophore scaffold for the development of anti-ALS drugs. In this study, a new protocol is developed to account for structural (non-displaceable) water molecules in docking simulations and successfully applied to predict the correct docked conformation binding modes of 5-FUrd at the W32 binding site. The docked configuration is within 0.58 Å (RMSD) of the observed configuration. The docking protocol involved calculating a hydration structure around SOD1 using molecular theory of solvation (3D-RISM-KH, 3D-Reference Interaction Site Model-Kovalenko-Hirata) whereby, non-displaceable water molecules are identified for docking simulations. This protocol was also used to analyze the hydrated structure of the W32 binding site and to explain the role of solvation in ligand recognition and binding to SOD1. Structural water molecules mediate hydrogen bonds between 5-FUrd and the receptor, and create an environment favoring optimal placement of 5-FUrd in the W32 binding site.

To Pass or Not To Pass: Predicting the Blood-Brain Barrier Permeability with the 3D-RISM-KH Molecular Solvation Theory.

Predicting the ability of chemical species to cross the blood-brain barrier (BBB) is an active field of research for development and mechanistic understanding in the pharmaceutical industry. Here, we report the BBB permeability of a large data set of compounds by incorporating molecular solvation energy descriptors computed by the 3D-RISM-KH molecular solvation theory. We have been able to show, for the first time, that the computed excess chemical potential in different solvents can be successfully used to predict permeability of compounds in a binary manner (yes/no) via a minimum-descriptor-based model. Our findings successfully combine the molecular solvation theory with the machine learning approach to address one of the most daunting challenges in predictive structure-activity relationship modeling. The workflow presented in this work is simple enough to be used by nonexperts with ease.

Application of the 3D-RISM-KH molecular solvation theory for DMSO as solvent.

The molecular solvation theory in the form of the Three-Dimensional Reference Interaction Site Model (3D-RISM) with Kovalenko-Hirata (KH) closure relation is benchmarked for use with dimethyl sulfoxide (DMSO) as solvent for (bio)-chemical simulation within the framework of integral equation formalism. Several force field parameters have been tested to correctly reproduce solvation free energy in DMSO, ion solvation in DMSO, and DMSO coordination prediction. Our findings establish a united atom (UA) type parameterization as the best model of DMSO for use in 3D-RISM-KH theory based calculations.