Formation of water-soluble metal cyanide complexes from solid minerals by Pseudomonas plecoglossicida

A few Pseudomonas species are able to form hydrocyanic acid (HCN), particularly when grown under glycine-rich conditions. In the presence of metals, cyanide can form water-soluble metal complexes of high chemical stability. We studied the possibility to mobilize metals as cyanide complexes from solid minerals using HCN-forming microorganisms. Pseudomonas plecoglossicida was cultivated in the presence of copper- and nickel-containing solid minerals. On powdered elemental nickel, fast HCN generation within the first 12 h of incubation was observed and water-soluble tetracyanaonickelate was formed. Cuprite, tenorite, chrysocolla, malachite, bornite, turquoise, millerite, pentlandite as well as shredded electronic scrap was also subjected to a biological treatment. Maximum concentrations of cyanide-complexed copper corresponded to a solubilization of 42% and 27% when P. plecoglossicida was grown in the presence of cuprite or tenorite, respectively. Crystal system, metal oxidation state and mineral hydrophobicity might have a significant influence on metal mobilization. However, it was not possible to allocate metal mobilization to a single mineral property. Cyanide-complexed gold was detected during growth on manually cut circuit boards. Maximum dicyanoaurate concentration corresponded to a 68.5% dissolution of the total gold added. These findings represent a novel type of microbial mobilization of nickel and copper from solid minerals based on the ability of certain microbes to form HC

An evolutionary modelling approach to predicting stress-strain behaviour of saturated granular soils

Purpose: To develop a unified framework for modelling triaxial deviator stress - axial strain and volumetric strain – axial strain behaviour of granular soils with the ability to predict the entire stress paths, incrementally, point by point, in deviator stress versus axial strain and volumetric strain versus axial strain spaces using an evolutionary-based technique based on a comprehensive set of data directly measured from triaxial tests without pre-processing. 177 triaxial test results acquired from literature were used to develop and validate the models. Models aimed not only to be capable of capturing and generalising the complicated behaviour of soils but also to explicitly remain consistent with expert knowledge available for such behaviour. Methodology: Evolutionary polynomial regression was used to develop models to predict stress - axial strain and volumetric strain – axial strain behaviour of granular soils. EPR integrates numerical and symbolic regression to perform evolutionary polynomial regression. The strategy uses polynomial structures to take advantage of favourable mathematical properties. EPR is a two-stage technique for constructing symbolic models. It initially implements evolutionary search for exponents of polynomial expressions using a genetic algorithm (GA) engine to find the best form of function structure, secondly it performs a least squares regression to find adjustable parameters, for each combination of inputs (terms in the polynomial structure). Findings: EPR-based models were capable of generalizing the training to predict the behaviour of granular soils under conditions that have not been previously seen by EPR in the training stage. It was shown that the proposed EPR models outperformed ANN and provided closer predictions to the experimental data cases. The entire stress paths for the shearing behaviour of granular soils using developed model predictions were created with very good accuracy despite error accumulation. Parametric study results revealed the consistency of developed model predictions, considering roles of various contributing parameters, with physical and engineering understandings of the shearing behaviour of granular soils. Originality/Value: In this paper, an evolutionary-based data-mining method was implemented to develop a novel unified framework to model the complicated stress-strain behaviour of saturated granular soils. The proposed methodology overcomes the drawbacks of artificial neural network-based models with black box nature by developing accurate, explicit, structured and user-friendly polynomial models, and enabling the expert user to obtain a clear understanding of the system

Synthetic dye decolorization by three sources of fungal laccase

Decolorization of six synthetic dyes using three sources of fungal laccase with the origin of Aspergillus oryzae, Trametes versicolor, and Paraconiothyrium variabile was investigated. Among them, the enzyme from P. variabile was the most efficient which decolorized bromophenol blue (100%), commassie brilliant blue (91%), panseu-S (56%), Rimazol brilliant blue R (RBBR; 47%), Congo red (18.5%), and methylene blue (21.3%) after 3 h incubation in presence of hydroxybenzotriazole (HBT; 5 mM) as the laccase mediator. It was also observed that decolorization efficiency of all dyes was enhanced by increasing of HBT concentration from 0.1 mM to 5 mM. Laccase from A. oryzae was able to remove 53% of methylene blue and 26% of RBBR after 30 min incubation in absence of HBT, but the enzyme could not efficiently decolorize other dyes even in presence of 5 mM of HBT. In the case of laccase from T. versicolor, only RBBR was decolorized (93%) in absence of HBT after 3 h incubation. © 2012 Forootanfar et al.; licensee BioMed Central Ltd

Epidemiological aspects of suicide lead to death in Iranian population during 2004-2008; A retrospective study

Suicide is raised in many countries around the world as one of the major problems in medical and social advocacy. The increasing incidence of suicide in the community is sensible and irreparable damage to the body of a society's human resources. Therefore the present study aimed to explore the epidemiological aspects of suicide leads to death in Iranian population during 2004-2008. In a retrospective study a census sampling method was used. All records formed of suicide death in Ilam province during 2004 to 2008 in the Office of the State Coroner were evaluated. Data was collected by a checklist including age, gender, marital status, educational level, occupation, number of families, suicide instrument location of attempted to suicide, place of death and season. SPSS software Package 14 was used to analyze the data of this project. Mean± SD, median and percentages were used to describe the data. The average percentage of suicide lead to death in Ilam province was 18.7 per 100.000 person's .Women and men have a suicide rate roughly equal (50.8 and 49.2). There was a significant relationship between month (P= 0.02), season (P= 0.03), the number of families (P= 0.001) and percentage of suicide lead to death. Self-burning was the most common method used by suicide lead to death. This study showed that suicide remains a serious problem and an increase occurred in suicide in Ilam province in comparison with previous years

Artificial neural network for non-intrusive electrical energy monitoring system

This paper discusses non-intrusive electrical energy monitoring (NIEM) system in an effort to minimize electrical energy wastages. To realize the system, an energy meter is used to measure the electrical consumption by electrical appliances. The obtained data were analyzed using a method called multilayer perceptron (MLP) technique of artificial neural network (ANN). The event detection was implemented to identify the type of loads and the power consumption of the load which were identified as fan and lamp. The switching ON and OFF output events of the loads were inputted to MLP in order to test the capability of MLP in classifying the type of loads. The data were divided to 70% for training, 15% for testing, and 15% for validation. The output of the MLP is either ‘1’ for fan or ‘0’ for lamp. In conclusion, MLP with five hidden neurons results obtained the lowest average training time with 2.699 seconds, a small number of epochs with 62 iterations, a min square error of 7.3872×10-5, and a high regression coefficient of 0.99050

Gas bubbles investigation in contaminated water using optical tomography based on independent component analysis method

This paper presents the results of concentration profiles for gas bubble flow in a vertical pipeline containing contaminated water using an optical tomography system. The concentration profiles for the bubble flow quantities are investigated under five different flows conditions, a single bubble, double bubbles, 25% of air opening, 50% of air opening, and 100% of air opening flow rates where a valve is used to control the gas flow in the vertical pipeline. The system is aided by the independent component analysis (ICA) algorithm to reconstruct the concentration profiles of the liquid-gas flow. The behaviour of the gas bubbles was investigated in contaminated water in which the water sample was prepared by adding 25 mL of colour ingredients to 3 liters of pure water. The result shows that the application of ICA has enabled the system to detect the presence of gas bubbles in contaminated water. This information provides vital information on the flow inside the pipe and hence could be very significant in increasing the efficiency of the process industries

Valle agricola chickpeas: Nutritional profile and metabolomics traits of a typical landrace legume from southern Italy

Chickpea (Cicer arietinum L.) from Valle Agricola is a legume cultivated in Southern Italy whose intake is strictly linked to rural traditions. In order to get new biochemical insight on this landrace and to promote its consumption and marketing, nutritional values (moisture content, total proteins, lipids, total and free amino acids) and metabolic traits are deeply investigated. Valle Agricola chickpea is nutritionally rich in proteins (19.70 g/100 g) and essential amino acids (7.12 g/100 g; ~40% of total). Carbohydrates, whose identity was unraveled by means of UHPLC-HR MS/MS analysis, were almost 60% of chemicals. In particular, a di-galactosylglycerol, a pinitol digalactoside, and a galactosylciceritol were found as constitutive, together with different raffinose-series oligosaccharides. Although lipids were the less constitutive compounds, glycerophospholipids were identified, while among free fatty acids linoleic acid (C18:2) was the most abundant, followed by oleic (C18:1) and palmitic (C16:0) acids. Isoflavones and hydroxybenzoic acid derivatives were also detected. Valle Agricola chickpeas showed very good levels of several mineral nutrients, especially magnesium (164 mg/100 g), potassium (748 mg/100 g), calcium (200 mg/100 g), zinc (4.20 mg/100 g) and manganese (0.45 mg/100 g). The boiling process favorably decreases anti-trypsin and anti-chymotrypsin activities, depleting this precious seed of its intrinsic antinutritional factors