Dynamics of Bacterial Community Structure in the Rhizosphere and Root Nodule of Soybean: Impacts of Growth Stages and Varieties

Bacterial communities in rhizosphere and root nodules have significant contributions to the growth and productivity of the soybean (Glycine max (L.) Merr.). In this report, we analyzed the physiological properties and dynamics of bacterial community structure in rhizosphere and root nodules at different growth stages using BioLog EcoPlate and high-throughput sequencing technology, respectively. The BioLog assay found that the metabolic capability of rhizosphere is in increasing trend in the growth of soybeans as compared to the bulk soil. As a result of the Illumina sequencing analysis, the microbial community structure of rhizosphere and root nodules was found to be influenced by the variety and growth stage of the soybean. At the phylum level, Actinobacteria were the most abundant in rhizosphere at all growth stages, followed by Alphaproteobacteria and Acidobacteria, and the phylum Bacteroidetes showed the greatest change. But, in the root nodules Alphaproteobacteria were dominant. The results of the OTU analysis exhibited the dominance of Bradyrhizobium during the entire stage of growth, but the ratio of non-rhizobial bacteria showed an increasing trend as the soybean growth progressed. These findings revealed that bacterial community in the rhizosphere and root nodules changed according to both the variety and growth stages of soybean in the field.     

Optimization of reactive SMB and Varicol systems

A comprehensive optimization study on a simulated moving bed reactor (SMBR) system is reported in this article for the direct synthesis of methyl tertiary butyl ether (MTBE) from tertiary butyl alcohol (TBA) and methanol. The applicability of the Varicol process, which is based on non-synchronous shift of the inlet and outlet ports, is explored for the first time for a reactive system. Multi-objective (two and three objective functions) optimization has been performed for both existing as well as design stage for SMBR and Varicol systems and their efficiencies are compared. The optimization problem involves relatively large number of decision variables; both continuous variables, such as flow rates in various sections and length of the columns and discrete variables, such as number of columns and column configuration. Pareto optimal solutions are obtained. It is observed that a five-column Varicol performs better than an equivalent five-column SMBR and its performance is nearly equal to that of a six-column SMBR in terms of purity and yield of MTBE and minimal eluent consumption. This is an important inference as it enables the reduction of fixed and operating costs while at the same time helps to achieve high purity and yield of the desired product and conversion of the limiting reactant. A state-of-the-art optimization technique, viz., non-dominated sorting genetic algorithm (NSGA), which allows handling of these complex optimization problems, is employed for this study. This is the first time that, not only the separating potential of Varicol has been extended to reaction systems, but also was optimized for multiple objectives.     

Organometallic L-Alanine Cadmium Iodide Crystals for Optical Device Fabrication

Single crystals of L-alanine cadmium iodide (LACI) were grown by the slow evaporation technique at room temperature. A single-crystal X-ray diffraction (SXRD) model was used to evaluate the crystal structure of the as-grown LACI crystal. The energy dispersive X-ray (EDX) analysis and ultraviolet-visible-near infrared (UV-vis-NIR) transmittance studies were carried out, and the results reveal the presence of elements in the title compound. From the transmittance data, the optical bandgap as a function of photon energy was estimated, and the different optical constants were calculated. A fluorescence study was performed for the LACI crystal. Thermogravimetric and differential thermal analyses have also been studied to investigate the thermal property of the LACI crystal. The efficiency of the second harmonic generation (SHG) of the title crystal was investigated. The magnetic and electrical properties were estimated by the vibrating sample magnetometer (VSM) analysis and impedance study, respectively.     

Characterisation of sol–gel method synthesised MgZnFe2 O4 nanoparticles and its cytotoxic effects on breast cancer cell line,MDA MB‐231 in vitro

In this study, nanocrystalline magnesium zinc ferrite nanoparticles were successfully prepared by a simple sol–gel method using copper nitrate and ferric nitrate as raw materials. The calcined samples were characterised by differential thermal analysis/thermogravimetric analysis, Fourier transform infrared spectroscopy and X‐ray diffraction. Transmission electron microscopy revealed that the average particle size of the calcined sample was in a range of 17–41 nm with an average of 29 nm and has spherical size. A cytotoxicity test was performed on human breast cancer cells (MDA MB‐231) and (MCF‐7) at various concentrations starting from (0 µg/ml) to (800 µg/ml). The sample possessed a mild toxic effect toward MDA MB‐231 and MCF‐7 after being examined with MTT (3‐[4, 5‐dimethylthiazol‐2‐yl]‐2, 5 diphenyltetrazolium bromide) assay for up to 72 h of incubation. Higher reduction of cells viability was observed as the concentration of sample was increased in MDA MB‐231 cell line than in MCF‐7. Therefore, further cytotoxicity tests were performed on MDA MB‐231 cell line.Inspec keywords: sol‐gel processing, nanoparticles, nanofabrication, magnesium compounds, zinc compounds, toxicology, biological organs, cancer, cellular biophysics, nanomedicine, calcination, differential thermal analysis, Fourier transform infrared spectra, X‐ray diffraction, transmission electron microscopy, particle size, organic compoundsOther keywords: sol‐gel method, cytotoxic effects, breast cancer cell line, MDA MB‐231 in vitro, nanocrystalline magnesium zinc ferrite nanoparticles, copper nitrate, ferric nitrate, raw materials, calcined samples, differential thermal analysis, thermogravimetric analysis, Fourier transform infrared spectroscopy, X‐ray diffraction, transmission electron microscopy, average particle size, cytotoxicity testing, human breast cancer cells, mild toxic effect, 3‐[4,5‐dimethylthiazol‐2‐yl]‐2,5 diphenyltetrazolium bromide) assay, cell viability, MCF‐7, MDA MB‐231 cell line, size 17 nm to 41 nm     

Random walks for selected boolean implication and equivalence problems

This paper is concerned with the design and analysis of a random walk algorithm for the 2CNF implication problem (2CNFI). In 2CNFI, we are given two 2CNF formulas f₁{\phi_{1}} and f₂{\phi_{2}} and the goal is to determine whether every assignment that satisfies f₁{\phi_{1}} , also satisfies f₂{\phi_{2}} . The implication problem is clearly coNP-complete for instances of kCNF, k ≥ 3; however, it can be solved in polynomial time, when k ≤ 2. The goal of this paper is to provide a Monte Carlo algorithm for 2CNFI with a bounded probability of error. The technique developed for 2CNFI is then extended to derive a randomized, polynomial time algorithm for the problem of checking whether a given 2CNF formula Nae-implies another 2CNF formula.     

Solids transport models comparison and fine‐tuning for horizontal,low concentration flow in single‐phase carrier fluid

We determined and fine‐tuned the solids transport models appropriate for predicting the single‐phase carrier fluid velocity to transport solid particles in conduits for horizontal, low concentration flow. A database with 538 experimental data points was compiled. A literature review was performed to determine the data ranges, forces, and mechanisms used to develop 44 models, and their velocity predictions were compared against the database using statistics. Using the dimensionless forms of the models and the data, the model parameters were adjusted to improve their accuracy and identify the dominant forces. At low concentrations: for liquid/solid flow from a bed of solids and gas/solid flow from the bottom of pipelines, the particle weight, and inertial and viscous forces dominate; for gas/solid flow from a bed of solids, the particle weight, and inertial, viscous, and adhesive forces play a role; and gaps exist in the data for large‐diameter pipes and high‐density gases. © 2013 American Institute of Chemical Engineers AIChE J, 60: 76–122, 2014     

Antihyperlipidemic effect of diosmin: A citrus flavonoid on lipid metabolism in experimental diabetic rats

The present study was hypothesized to evaluated the antihyperlipidemic effect of diosmin (DS) on lipid metabolism in experimental diabetic rats. Diabetes was induced male albino Wistar rats by intraperitoneal administration of streptozotocin (STZ) (45 mg/kg b.w.) 15 min after the ip administration of nicotinamide (NA) (110 mg/kg b.w.). DS were administered to diabetic rats intragastrically at 100 mg/kg b.w. for 45 days. The levels of plasma and tissue lipids (cholesterol, triglycerides) (TGs), free fatty acids (FFAs) and phospholipids (PLs), low density, very low-density lipoproteins (LDL and VLDL), and high-density-cholesterol (HDL-C) were measured. The activities of 3-hydroxy 3-methylglutaryl coenzyme A (HMG-CoA) reductase, lipoprotein lipase (LPL) and lecithin cholesterol acyl transferase (LCAT) were assayed. The levels of plasma and tissue lipids decreased with significant increase in HDL-C levels. The altered activities of lipid metabolic enzymes were restored to near normal. The present findings suggest that DS can potentially ameliorate lipid abnormalities in experimental diabetes.     

Maintaining associativity between form feature models

The promise of features technology was that the task domains would have access to task specific product data through feature based models. This is an important requirement in a distributed and concurrent design environment, where data of part geometry has to be shared between different task domains.Associativity between feature models implies the automatic updating of different feature models of a part after changes are made in one of its feature models. The proposed algorithm takes multiple feature models of a part as input and modifies other feature models to reflect the changes made to a feature in a feature model. The proposed algorithm updates feature volumes in other feature models and then classifies the updated volumes to obtain the updated feature model. The spatial arrangement of feature faces and adjacency relationship between features are used to isolate features in a view that are affected by the modification. Feature volumes are updated based on the classification of the feature volume of the modified feature with respect to feature volumes of the model being updated. The algorithm is capable of handling all types of feature modifications namely, feature deletion, feature creation, and changes to feature location and parameters. In contrast to current art in automatic updating of feature models, the proposed algorithm does not use an intermediate representation, does not re-interpret the feature model from a low level representation and handles interacting features. The present work considers modifications to form features only. Modification of constraints and application attributes are under investigation. Results of implementation on typical cases are presented.     

From short-term memory to semantics-a computational model

Clinical disorders of language, known as aphasia, cause impaired comprehension of speech in written and spoken forms. This impairment is due to the patients inability to process semantics that arise from sequence independent co-occurrence of words with content in a short-term memory (STM) of preceding words. If W_i is the immediately forthcoming word in input to the patient, STM, in the context of this disorder, consists of a window, STM_Win, that contains the k words that immediately precede W_i. We use a generative approach to model semantics that ensue from the co-occurrence of W_i and STM_Win, and view these semantics as the output of a random process with parameters . The model uses supervised learning to maximize the likelihood of , given labeled content in STM_Win. Experimental validation on standard text classification data sets gives an accuracy that is comparable to, or better than, that obtained using support vector machines (SVMs).