Salicylic acid nanoparticles (SANPs) improve growth and phytoremediation efficiency of Isatis cappadocica Desv., under As stress

Arsenic (As) is a toxic metalloid dispersed in the environment and it cause serious toxicity to plants. Salicylic acid (SA) plays an important role in many plant growth processes involved in plant defence against heavy metal or metalloid stress. In the present study, for the first time, chitosan nanoparticles was synthesised, loaded by SA and the positive role of SA on growth and phytoremediation efficiency of Isatis cappadocica against As toxicity were evaluated. The highest arsenate treatment (1200 µM) caused a decrease in plant biomass that, however, its combined application with salicylic acid nanoparticles (SANPs) substantially increase in shoot height, root length and their biomass production compared with As stressed plants. The pretreatment of SANPs by increasing arsenate supply, simultaneously increased the As concentration in roots and shoots of I. cappadocica which reached a maximum of 705 and 1188 mg/kg, respectively. This results suggest that high levels of As stress induce stress status in I. cappadocica which SANPs pretreatment application limit these toxic effects of As. Therefore, SANPs has a significant beneficial effect on the growth and phytoremediation efficiency of I. cappadocica subjected to As stress.Inspec keywords: nanoparticles, nanobiotechnology, botany, toxicology, arsenic, biological techniques, polymersOther keywords: salicylic acid nanoparticles, SANP, plant growth efficiency, phytoremediation efficiency, Isatis cappadocica Desv, arsenic stress, toxic metalloid, plant toxicity, plant defence, metalloid stress, chitosan nanoparticles, plant biomass, shoot height, root length, biomass production, I. cappadocica, As     

FT-PDC: an enhanced hybrid congestion-aware fault-tolerant routing technique based on path diversity for 3D NoC

In recent years, using three-dimensional Network-on-Chip (3D-NoC) has increased due to its high performance and integration of processing elements. However, as technology evolves, dimension of processing elements and the number of connections are decreasing and this increases their sensitivity to external factors. Therefore, fault detection is one of the most important challenges of designing 3D-NoC because even a transistor not working well may cause the whole system not to work. Many methods have been provided so far to make three-dimensional NoC tolerant against fault, but most of these methods suffer from some disadvantages. For example, their fault detection models are not strong enough or they do not consider the network traffic. Regarding these problems, in this article, a routing technique called fault-tolerant routing algorithm based on path diversity and congestion (FT-PDC) for three-dimensional mesh connectivity based on NoC is provided that has some features like finding the shortest current path, considering fault in vertical and horizontal links, and considering path diversity and congestion. In addition to these features, FT-PDC could solve the problems of other methods to a great extent considering network traffic conditions and sending the packet from a path in which traffic conditions are two hops better than the other paths. These features have led to the superiority of the proposed method over other methods. The simulation results in the Noxim simulator show that latency and throughput of FT-PDC compared to similar recent methods have significantly improved.

     

Rapid and highly efficient synthesis of thioureas in biocompatible basic choline hydroxide

A straightforward and convenient synthesis of symmetrical thiourea derivatives by the reaction of primary amines and carbon disulfide in biocompatible basic choline hydroxide is presented. A variety of biologically important thiourea derivatives can be obtained in good to excellent yields without a tedious work-up under mild reaction conditions. A series of primary aliphatic and aromatic amines with different substituted functional groups have been converted to thiourea derivatives under milder reaction conditions and short reaction times.     

Automatic fire detection based on soft computing techniques: review from 2000 to 2010

Automatic fire detection system is a system that is capable of assessing environmental factors and their effects on the environment as well as predicting the occurrence of fire in the early stages and even before the outbreak. There are two perspectives in fire detection: fire detection in forests or jungles and fire detection in occupied or residential areas. Automatic fire detection has attracted increased attention due to its importance in decreasing fire damage. There are many studies that have considered appropriate techniques for early fire detection. In recent years researches have been studying technical developments in this field aimed at exploiting wireless communications networks, detection systems and fire prediction systems design. In this paper the automatic fire detection researches using intelligent techniques from 2000 to 2010 is reviewed. We could classify researches to four categories: fire detectors, reduce false alarms systems, fire data analysis and fire predictors. We also classify the intelligent techniques outlined in the researches for each category.     

A PSO-based model to increase the accuracy of software development effort estimation

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation difficult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is difficult to determine. In such situations, efficient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identification of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is flexible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could significantly improve the performance of existing estimation models.     

Evaluation of Factors Influencing Microwave Osmotic Dehydration of Apples Under Continuous Flow Medium Spray (MWODS) Conditions

Microwave osmotic dehydration under continuous flow medium spray (MWODS) conditions is an innovative concept with high potential for enhancing moisture loss as well as improving product quality. Quantification of mass transfer kinetics under different processing conditions is important for managing and optimizing the osmotic dehydration process. A response surface methodology was used for evaluating and quantifying the moisture loss and solids gain kinetics of apples during the MWODS process. Experiments were designed according to a central composite rotatable design with all independent variables included at five levels (sucrose concentration, 33.3–66.8°Brix; medium temperature, 33.3–66.8 °C; medium flow rate, 2,120–3,480 ml/min; and medium contact time, 5–55 min). The process responses were moisture loss (ML), solids gain (SG), and weight reduction (WR) and were related to process variables using second-order polynomial regression models. The lack of fit was not significant (p?>?0.05) for any of the developed models. For ML, SG, and WR, the medium contact time was the most significant factor during the MWODS process followed by medium temperature and sucrose concentration. The effect of medium flow rate was only significant with moisture loss and weight reduction. The quantity of ML, SG, or WR achieved over a 30 min treatment time was chosen as the drying rate. These rates were shown to be responsive to the osmotic treatments increasing with sucrose concentration, medium flow rate, and medium temperature.     

Optimisation study of gum extraction from Basil seeds (Ocimum basilicum L.)

Basil seed ( Ocimum basilicum L.) is cultivated in large quantities in different regions of Iran. This seed has reasonable amounts of gum with good functional properties which is comparable with commercial food hydrocolloids. A central composite rotatable design was applied to evaluate the effects of temperature, pH and water/seed ratio on the yield, apparent viscosity and protein content of water-extracted Basil seed gum. All of the variables significantly ( P  < 0.05) affected the extraction yield, whereas the effect of water/seed ratio on apparent viscosity and the effects of pH and water/seed ratio on protein content were not significant ( P  > 0.05). Numerical optimisation determined the optimum extraction conditions based on the highest yield and viscosity and the lowest protein content as being temperature 68.71 °C, pH 8.09 and water/seed ratio 65.98:1. Power law model well described non-Newtonian pseudoplastic behaviour of BSG. Flow behaviour index ( n ) and consistency index ( K ) of 1% crude and pure BSG samples were 0.306, 0.283 and 17.46, 20.22 Pa s ⁿ , respectively.     

Highly Selective Cyanide Coated-Wire Electrode Based on a Recently Synthesized Co(II) Complex With the N,N′ -Bis(2-Quinolinecarboxamido)- 1,2-Benzene Applying Batch and Flow Injection Analysis Techniques

A poly(vinyl chloride) (PVC)-based coated wire electrode based on [Co(II)bqb] (bqb = N,N'-bis(2-quinolinecarboxamido)-1,2-benzene) as a novel sensing material for determination of trace amounts of cyanide ions is successfully developed. The effect of electrode substrate, membrane composition and pH of the working solution on the behavior of the sensor was investigated. The electrode was also used in flow injection potentiometry by a home-made flow cell. The electrode revealed Nernstian response towards cyanide anion over the concentration ranges 3.2times10^-7 to 2.0times10^-4 molldrL^-1 and 5.0times10^-6 to 1.0times10^-3 molldrL^-1 applying batch and flow injection analysis (FIA), respectively. The lower detection limits are 3.2times10^-7 molldrL^-1 and 5.0times10^-6 molldrL^-1 for batch and FIA, respectively. The electrode shows a short response time (<5 s) in the whole concentration range. The selectivity of the electrode in comparison with most of cyanide selective electrodes is high. The flow cell is simple to construct and free from memory effect problems over long periods of use. The proposed electrode was successfully applied for the determination of cyanide in commercially available spring water under both batch and flow injection conditions. A comparative study revealed no significant difference between ASTM method and the proposed technique.     

Functional Heterodimerization between the G Protein-Coupled Receptor GPR17 and the Chemokine Receptors 2 and 4: New Evidence

GPR17, a G protein-coupled receptor, is a pivotal regulator of myelination. Its endogenous ligands trigger receptor desensitization and downregulation allowing oligodendrocyte terminal maturation. In addition to its endogenous agonists, GPR17 could be promiscuously activated by pro-inflammatory oxysterols and chemokines released at demyelinating lesions. Herein, the chemokine receptors CXCR2 and CXCR4 were selected to perform both in silico modelling and in vitro experiments to establish their structural and functional interactions with GPR17. The relative propensity of GPR17 and CXCR2 or CXCR4 to form homo- and hetero-dimers was assessed by homology modelling and molecular dynamics (MD) simulations, and co-immunoprecipitation and immunoenzymatic assay. The interaction between chemokine receptors and GPR17 was investigated by determining receptor-mediated modulation of intracellular cyclic adenosine monophosphate (cAMP). Our data show the GPR17 association with CXCR2 or CXCR4 and the negative regulation of these interactions by CXCR agonists or antagonists. Moreover, GPR17 and CXCR2 heterodimers can functionally influence each other. In contrast, CXCR4 can influence GPR17 functionality, but not vice versa. According to MD simulations, all the dimers reached conformational stability and negative formation energy, confirming the experimental observations. The cross-talk between these receptors could play a role in the development of the neuroinflammatory milieu associated with demyelinating events.     

Multi-scale modelling of elastic moduli of trabecular bone

We model trabecular bone as a nanocomposite material with hierarchical structure and predict its elastic properties at different structural scales. The analysis involves a bottom-up multi-scale approach, starting with nanoscale (mineralized collagen fibril) and moving up the scales to sub-microscale (single lamella), microscale (single trabecula) and mesoscale (trabecular bone) levels. Continuum micromechanics methods, composite materials laminate theory and finite-element methods are used in the analysis. Good agreement is found between theoretical and experimental results.