Serum procalcitonin and cerebrospinal fluid cytokines level in children with meningitis.

AIMS: To determine the level of serum procalcitonin and cerebrospinal fluid cytokines in children with bacterial or viral meningitis and to document the use of these parameters in differential diagnosis. RESULTS: Before the start of antibiotic treatment, serum procalcitonin and tumor necrosis factor alpha levels were found to be higher in acute bacterial meningitis compared with viral meningitis and with the control group. Similarly, cerebrospinal fluid interleukin-6 levels were found to be significantly higher in children with acute bacterial meningitis compared with viral meningitis. However, no significant difference was determined between groups in respect to the cerebrospinal fluid interleukin-8 level. CONCLUSION: Serum procalcitonin and cerebrospinal fluid tumor necrosis factor alpha levels can be used in the early diagnosis of bacterial meningitis. Similarly, they may be useful adjuncts in differential diagnosis of bacterial and viral meningitis

Reactive dye bioaccumulation by fungus Aspergillus niger isolated from the effluent of sugar fabric-contaminated soil

The present study dealt with the decolorization of textile dye Reactive Black-5 by actively growing mycelium of Aspergillus niger MT-1 in molasses medium. It was found that the fungus, which was isolated from the effluent of sugar fabric-contaminated soil, was capable of decolorizing the Reactive Black-5 dye in a wide range of temperature, shaking speed and pH values. The experiments also revealed that highest dye decolorization efficiency was achieved with cheap carbon (molasses sucrose) and nitrogen (ammonium chloride) sources. Under the optimized culture conditions, the complete decolorization (100%) of 0.1 g/L dye was achieved in 60 hours. The dominant mechanism of dye removal by the fungus was found to be probably bioaccumulation. Fungal growth in small uniform pellet form was found to be better for dye bioacumulation. Molass as carbon source increased dye bioaccumulation by stimulating the mycelial growth in small uniform pellet form. The maximum bioaccumulation efficiency of fungus for dye was 91% (0.273 g bioaccumulated dye) at an initial dye concentration of 0.3 g/L in 100 hours. It was shown for the first time in the present study that the effluent of sugar fabric-contaminated soil was a good source of microorganisms, being capable of decolorizing snythetic textile dyes. </jats:p

Application of low magnetic field on inulinase production by Geotrichum candidum

This study evaluates the application of low magnetic field (LMF) on inulinase enzyme production by Geotrichum candidum under solid state fermentation (SSF) using leek as potential carbon source. First, the fermentation conditions were optimized using normal magnetic field grown microorganism. Among eight G. candidum isolates, the most effective strain called G. candidum OC-7 was selected to use in further experiments. In the second part of the study, SSF was carried out under different LMFs (4 and 7 mT). The results showed that inulinase activity was strongly affected by LMF application. The highest enzyme activity was obtained as 535.2 U/g of dry substrate (gds) by 7 mT magnetic field grown G. candidum OC-7. On the contrary, the control had only 412.1 U/gds. Consequently, the use of leek presents a great potential as an alternative carbon source for inulinase production and magnetic field treatment could effectively be used in order to enhance the enzyme production

Design and simulation of a LQG robust controller for an electrical power system

This paper describes a LQG robust controller for the load frequency control of an electrical power system. The controller is used in order to achieve robust stability and good dynamic performance against the variation of power system parameters and its load. The application of the proposed LQG robust control scheme is implemented through the simulation of a single area power system model. The proposed robust controller for power systems stability is designed using Matlab/Simulink program. Simulation results confirm the performance of the proposed controller for the electrical power system

Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization

Artificial Neural Networks (ANNs) have been used in a wide range of applications for complex datasets with their flexible mathematical architecture. The flexibility is favored by the introduction of a higher number of connections and variables, in general. However, over-parameterization of the ANN equations and the existence of redundant input variables usually result in poor test performance. This paper proposes a superstructure-based mixed-integer nonlinear programming method for optimal structural design including neuron number selection, pruning, and input selection for multilayer perceptron (MLP) ANNs. In addition, this method uses statistical measures such as the parameter covariance matrix in order to increase the test performance while permitting reduced training performance. The suggested approach was implemented on two public hyperspectral datasets (with 10% and 50% sampling ratios), namely Indian Pines and Pavia University, for the classification problem. The test results revealed promising performances compared to the standard fully connected neural networks in terms of the estimated overall and individual class accuracies. With the application of the proposed superstructural optimization, fully connected networks were pruned by over 60% in terms of the total number of connections, resulting in an increase of 4% for the 10% sampling ratio and a 1% decrease for the 50% sampling ratio. Moreover, over 20% of the spectral bands in the Indian Pines data and 30% in the Pavia University data were found statistically insignificant, and they were thus removed from the MLP networks. As a result, the proposed method was found effective in optimizing the architectural design with high generalization capabilities, particularly for fewer numbers of samples. The analysis of the eliminated spectral bands revealed that the proposed algorithm mostly removed the bands adjacent to the pre-eliminated noisy bands and highly correlated bands carrying similar information

Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization

Artificial Neural Networks (ANNs) have been used in a wide range of applications for complex datasets with their flexible mathematical architecture. The flexibility is favored by the introduction of a higher number of connections and variables, in general. However, over-parameterization of the ANN equations and the existence of redundant input variables usually result in poor test performance. This paper proposes a superstructure-based mixed-integer nonlinear programming method for optimal structural design including neuron number selection, pruning, and input selection for multilayer perceptron (MLP) ANNs. In addition, this method uses statistical measures such as the parameter covariance matrix in order to increase the test performance while permitting reduced training performance. The suggested approach was implemented on two public hyperspectral datasets (with 10% and 50% sampling ratios), namely Indian Pines and Pavia University, for the classification problem. The test results revealed promising performances compared to the standard fully connected neural networks in terms of the estimated overall and individual class accuracies. With the application of the proposed superstructural optimization, fully connected networks were pruned by over 60% in terms of the total number of connections, resulting in an increase of 4% for the 10% sampling ratio and a 1% decrease for the 50% sampling ratio. Moreover, over 20% of the spectral bands in the Indian Pines data and 30% in the Pavia University data were found statistically insignificant, and they were thus removed from the MLP networks. As a result, the proposed method was found effective in optimizing the architectural design with high generalization capabilities, particularly for fewer numbers of samples. The analysis of the eliminated spectral bands revealed that the proposed algorithm mostly removed the bands adjacent to the pre-eliminated noisy bands and highly correlated bands carrying similar information.</jats:p

Chitosan production by psychrotolerant Rhizopus oryzae in non-sterile open fermentation conditions

A new chitosan producing fungus was locally isolated from soil samples collected around Erzurum,Turkey and identified as Rhizopus oryzae PAS 17 (GenBank accession number KU318422.1). Cultivation in low cost non-sterile conditions was achieved by exploiting its ability to grow at low temperature and pH, thus, undesired microbial contamination could be eliminated when appropriate culture conditions (incubation temperature as 15 degrees C and initial pH of the medium as 4.5) were selected. Medium composition and culture conditions were optimized using Taguchi orthogonal array (OA) design of experiment (DOE). An OA layout of L16 (4(5)) was constructed with five most influensive factors at four levels on chitosan production like, carbon source (molasses), metal ion (Mg2+), inoculum amount, agitation speed and incubation time. The optimal combinations of factors (molasses, 70 ml/l; MgSO4 center dot 7H(2)O, 0.5 g/l; inoculum, 6.7 x 10(6) spores/disc; agitation speed, 150 rpm and incubation time, 8 days) obtained from the proposed DOE methodology was further validated by analysis of variance (ANOVA) test and the results revealed the increment of chitosan and biomass yields of 14.45 and 8.58 folds from its unoptimized condition, respectively. (C) 2016 Elsevier B.V. All rights reserved