Arsenic bioleaching in medical realgar ore and arsenicbearing refractory gold ore by combination of Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans

Purpose: To develop a novel biotechnological method for removing toxic arsenic from two kinds of representative arsenic-containing ores using different mixed mesophilic acidophiles. Methods: Bioleaching of the two types of arsenic-containing ores by mixed arsenic-unadapted Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans or mixed arsenic-adapted cultures, were carried out. Arsenic bioleaching ratios in the various leachates were determined and compared. Results: The results showed that the maximum arsenic leaching ratio obtained from realgar in the presence of mixed adapted cultures was 28.6 %. However, the maximum arsenic leaching ratio from realgar in the presence of mixed unadapted strains was only 12.4 %. Besides, maximum arsenic leaching ratios from arsenic-bearing refractory gold ore by mixed adapted strains or unadapted strains were 45.0 and 22.9 %, respectively. Oxidation of these two ores by sulfuric acid was insignificant, as maximum arsenic leaching ratios of realgar and arsenic-bearing refractory gold ore in the absence of any bacterium were only 2.8 and 11.2 %, respectively. Conclusion: Arsenic leaching ratio of realgar and refractory gold ore can be enhanced significantly in the presence of arsenic-adapted mesophilic acidophiles.

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.