Characterization of coal fly ash and use of plants growing in ash pond for phytoremediation of metals from contaminated agricultural land

Mobilization of heavy metals around coal power plants due to improper disposal of fly ash (FA) and wastewater have led to release of pollutants into the environment. For protection of inimitable natural resources, application of economical and effective technologies is needed such as phytoremediation is cost-effective, ecofriendly and a better option for elimination of metal from contaminated sites. Twelve plant species were sampled from ash dyke of Singrauli and screened for accumulation of metals for this study. Mobilization ratio of metals from soil to plant was evaluated to determine translocation factor. CILLAS analyzer, Raman spectroscopy and SEM-EDX were used for characterization of particle size, functional groups and morphology of fly ash. Results showed mean metal concentrations in contaminated soil for Fe, Mn, Cr, Zn, Ni, Cu, Cd and Pb were 909.4, 60.6, 9.5, 134.8, 13.6, 26.7, 2.9 and 25.4 µg g^?1 respectively. Enrichment factors for soil, root and shoot for a contaminated site were 1.9, 3.8 &; 4.3 for Zn and 2.7, 3.5 &; 3.8 for Cd. Six hyper-accumulators with absorption efficiency >1 viz. I. carnea, S. nigrum, S. munja, T. angustifolia, C. dactylon and P. hysterophorus were identified which may be cultivated successively to reclaim and restore damaged agricultural land.     

Classifying mild traumatic brain injuries with functional network analysis

Background

Traumatic brain injury (TBI) represents a critical health problem of which timely diagnosis and treatment remain challenging. TBI is a result of an external force damaging brain tissue, accompanied by delayed pathogenic events which aggravate the injury. Molecular responses to different mild TBI subtypes have not been well characterized. TBI subtype classification is an important step towards the development and application of novel treatments. The computational systems biology approach is proved to be a promising tool in biomarker discovery for central nervous system injury.

Results

In this study, we have performed a network-based analysis on gene expression profiles to identify functional gene subnetworks. The gene expression profiles were obtained from two experimental models of injury in rats: the controlled cortical impact and the fluid percussion injury. Our method integrates protein interaction information with gene expression profiles to identify subnetworks of genes as biomarkers. We have demonstrated that the selected gene subnetworks are more accurate to classify the heterogeneous responses to different injury models, compared to conventional analysis using individual marker genes selected without network information.

Conclusions

The systems approach can lead to a better understanding of the underlying complexities of the molecular responses after TBI and the identified subnetworks will have important prognostic functions for patients who sustain mild TBIs.

     

Single cell rigidity sensing: A complex relationship between focal adhesion dynamics and large-scale actin cytoskeleton remodeling

ABSTRACT

Many physiological and pathological processes involve tissue cells sensing the rigidity of their environment. In general, tissue cells have been shown to react to the stiffness of their environment by regulating their level of contractility, and in turn applying traction forces on their environment to probe it. This mechanosensitive process can direct early cell adhesion, cell migration and even cell differentiation. These processes require the integration of signals over time and multiple length scales. Multiple strategies have been developed to understand force- and rigidity-sensing mechanisms and much effort has been concentrated on the study of cell adhesion complexes, such as focal adhesions, and cell cytoskeletons. Here, we review the major biophysical methods used for measuring cell-traction forces as well as the mechanosensitive processes that drive cellular responses to matrix rigidity on 2-dimensional substrates.     

Multimodel ensembles of wheat growth: many models are better than one

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC. The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.     

Detection of phase shifts in batch fermentation via statistical analysis of the online measurements: a case study with rifamycin B fermentation

Industrial production of antibiotics, biopharmaceuticals and enzymes is typically carried out via a batch or fed-batch fermentation process. These processes go through various phases based on sequential substrate uptake, growth and product formation, which require monitoring due to the potential batch-to-batch variability. The phase shifts can be identified directly by measuring the concentrations of substrates and products or by morphological examinations under microscope. However, such measurements are cumbersome to obtain. We present a method to identify phase transitions in batch fermentation using readily available online measurements. Our approach is based on Dynamic Principal Component Analysis (DPCA), a multivariate statistical approach that can model the dynamics of non-stationary processes. Phase-transitions in fermentation produce distinct patterns in the DPCA scores, which can be identified as singular points. We illustrate the application of the method to detect transitions such as the onset of exponential growth phase, substrate exhaustion and substrate switching for rifamycin B fermentation batches. Further, we analyze the loading vectors of DPCA model to illustrate the mechanism by which the statistical model accounts for process dynamics. The approach can be readily applied to other industrially important processes and may have implications in online monitoring of fermentation batches in a production facility.     

Taste masking of ondansetron hydrochloride by polymer carrier system and formulation of rapid-disintegrating tablets

The purpose of this research was to mask the intensely bitter taste of ondansetron HCl and to formulate a rapiddisintegrating tablet (RDT) of the taste-maske drug. Taste masking was done by complexing ondansetron HCl with aminoalkyl methacrylate copolymer (Eudragit EPO) in different ratios by the precipitation method. Drug-polymer complexes (DPCs) were tested for drug content, in vitro taste in simulated salivary fluid (SSF) of pH 6.2, and molecular property. Complex that did not release drug in SSF was considered taste-masked and selected for formulation RDTs. The complex with drug-polymer ratio of 8∶2 did not show drug release in SSF; therefore, it was selected. The properties of tablets such as tensile strength, wetting time, water absorption ratio, in vitro disintegration time, and disintegration in the oral cavity were investigated to elucidate the wetting and disintegration characteristics of tablets. Polyplasdone XL-10 7% wt/wt gave the minimum disintegration time. Tablets of batch F4 containing spray-dried mannitol and microcrystalline cellulose in the ratio 1∶1 and 7% wt/wt Polyplasdone XL-10 showed faster disintegration, within 12.5 seconds, than the marketed tablet (112 seconds). Good correlation between in vitro disintegration behavior and in the oral cavity was recognized. Taste evaluation of RDT in human volunteers revealed considerable taste masking with the degree of bitterness below threshold value (0.5) ultimately reaching to 0 within 15 minutes, whereas ondansetron HCl was rated intensely bitter with a score of 3 for 10 minutes. Tablets of batch F4 also revealed rapid drug release (t₉₀, 60 seconds) in SGF compared with marketed formulation (t₉₀, 240 seconds;P<.01). Thus, results conclusively demonstrated successful masking of taste and rapid disintegration of the formulated tablets in the oral cavity. Published: June 22, 2007