Determination of arsenic species in rice grains using HPLC-ICP-MS

Determination of As species in rice is necessary because inorganic As species (arsenate (As^V) and arsenite (As^III)) are more toxic than organic As (monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA)). This study evaluated the As species in Korean and USA rice grains. Levels were determined using microwave extraction and high performance liquid chromatography coupled with inductively coupled plasma-mass spectrometry (HPLC-ICPMS). Arsenite and DMA were the major species detected in Korean and USA rice. The percentage of inorganic As was 76.94% (54.50–87.86%) for Korean rice and 69.28% (52.94–72.92%) for USA samples. The order and percentage of As species observed in Korean and USA rice were As^III (70%)>DMA (24%)>As^V (5%)>MMA (1%), and As^III (64%)>DMA (28%)>As^V(5%)>MMA (3%), respectively. The As^III concentrations were not significantly different in Korean rice grains, compared to USA grains. The high As^III predominance indicates an elevated toxic effect of As in rice grains and needs further attention.     

Synergistic extraction of Nd(III) with mixture of 8-hydroxyquinoline and its derivative with di-2-ethyl hexyl phosphoric acid in different diluents

Solvent extraction of neodymium (III) from chloride medium with synergistic mixture of 8-hydroxyquinoline or 2-methyl-8-hydroxyquinoline and di-2-ethyl hexyl phosphoric acid was investigated in different diluents. Effect of various experimental parameters such as pH, extractant concentration, diluents and temperature was studied. Role of diluents on extraction of neodymium is explained in terms of activity coefficient adopting Hildebrand regular solution theory. The negative value of enthalpy change suggests that the extraction of ternary system is exothermic. The IR data for the extracted species also substantiated the nature of extracted species as observed by slope analysis method.     

Rats fed blended oils containing coconut oil with groundnut oil or olive oil showed an enhanced activity of hepatic antioxidant enzymes and a reduction in LDL oxidation

The effects of feeding Wistar rats with blended and interesterified oils of coconut (CO):groundnut (GNO) or CO:olive (OLO) on liver antioxidant enzyme activities and susceptibility of LDL to oxidation were studied. The hepatic lipid peroxidation (LPO) levels in the rats fed CO:GNO blend and interesterified oils were increased by 31% and 21%, when compared to the rats given CO. The superoxide dismutase activity was increased by 31% and 28%, and catalase (CAT) activity was increased by 37% and 39%, respectively, in rats given blends and interesterified oils of CO:GNO, as compared to those given CO. The activities of glutathione peroxidase (GPx) were increased by 17% and 20% in rats given CO:GNO blend and interesterified oils. Glutathione transferase (GST) level was also found to be increased by 26% and 31% after feeding blended and interesterified oils of CO:GNO, compared to those given CO. The LDL oxidation, which was elevated by feeding GNO, was found to be reduced by 10% and 14%, respectively, in the groups fed blended and interesterified oils of CO:GNO. Similarly, CO:OLO blended oils enhanced SOD, CAT, GPx and GST activities by 34%, 43%, 27% and 23%, respectively, compared to the rats given CO-containing diets. The corresponding increases in theses antioxidant enzyme activities when CO:OLO interesterified oils were fed to rats were 38%, 50%, 28% and 26%, respectively, when compared to rats given CO-containing diets. There was a significant reduction in hepatic LPO as well as oxidation of LDL, when blended and interesterified oils of CO:OLO were fed to rats. The results demonstrated that blended and interesterified oils of CO with GNO or OLO enhance hepatic antioxidant enzymes, decreased lipid peroxidation in liver and reduced the susceptibility of LDL to oxidation.     

Sorption Behavior of Y(III) from Chloride Medium with Polymer Composites Containing Di-2-ethyl Hexyl Phosphoric Acid and Multiwall Carbon Nanotube

Extractant impregnated polyethersulfone beads have been prepared by phase inversion method and investigated for yttrium recovery from aqueous medium. The effect of experimental parameters on yttrium sorption has been studied. Quantitative sorption of yttrium (> 90%) was attained after 8 hours of equilibration time in the case of D2EHPA impregnated composite beads. Analysis of sorption data by different kinetic and diffusion models suggested that the sorption of Y(III) followed the pseudo-second order model. Stability tests with polymeric composite beads by multiple cycles of sorption and desorption of Y(III) have established the feasibility of reusing the beads for sorption of metal ions.     

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques.     

Development of computer‐aided approach for brain tumor detection using random forest classifier

The nonlinear development of cells in brain region forms the abnormal patterns in brain in the form of tumors. It is necessary to detect and diagnose the brain tumors in an automated manner using computer‐aided approaches at large population areas. The noises in brain magnetic resonance image is detected and reduced as preprocessing steps and then grey level co‐occurrence matrix are now extracted from the preprocessed brain image. In this article, random forest classifier‐based brain tumor detection and segmentation methodology is proposed to classify the brain image into normal or abnormal. The proposed brain tumor detection and segmentation system is analyzed in terms of sensitivity, specificity, false‐positive rate, false‐negative rate, likelihood ratio positive, and likelihood ratio negative.     

Fusion of artificial neural networks for learning capability enhancement: Application to medical image classification

Artificial neural network (ANN) is one of the commonly used tools for computational applications. The specific advantages of ANN are high accuracy, less convergence time, less computational complexity, and so forth. However, all these merits are not available in the same ANN. Even though back propagation neural (BPN) networks are accurate, their computational complexity is significantly high. BPN networks are also not stable. On the other hand, Hopfield neural network (HNN) is better than BPN in terms of computational efficiency. But the accuracy of HNN is low. In this work, a modified ANN is proposed to overcome this specific problem. The modified ANN is a fusion of BPN and HNN. The technical concepts of BPN and HNN are mixed in the training algorithm of the proposed back propagation‐Hopfield network (BPHN). The objective of this fusion is to improve the performance of conventional ANN. Magnetic resonance brain image classification experiments are used to analyse the proposed BPHN. Experimental results have suggested improvement in the learning process of the proposed BPHN. A comparative analysis with the conventional networks is performed to validate the performance of the proposed approach.     

Steganalysis of BCH code based stego schemes

The class of stego systems based on error correcting codes is one of the alternatives to conventional steganographic systems. In the context of code based steganography, the BCH code based stego system offers a very smart solution based on the hardness of syndrome decoding. It has been shown that syndrome decoding problem is able to resist the existing steganalysis attacks. In this paper, we introduce an attack against these systems using a new syndrome decoding method. This scheme also generates a direct estimate of the secret message bit locations by exploiting the embedding patterns. This new attack notably points out that BCH code based stego system with its original parameters do not provide sufficient security.