Analysis of contributions of herpes simplex virus type 1 UL43 protein to induction of cell-cell fusion

Purpose: To investigate whether UL43 protein, which is highly conserved in alpha- and gamma herpes viruses, and a non-glycosylated transmembrane protein, is involved in virus entry and virus-induced cell fusion. Methods: Mutagenesis was accomplished by a markerless two-step Red recombination mutagenesis system implemented on the Herpes simplex virus 1 (HSV-1) bacterial artificial chromosome (BAC). Growth properties of HSV-1 UL43 mutants were analyzed using plaque morphology and one-step growth kinetics. SDS-PAGE and Western blot was employed to assay the synthesis of the viral glycoproteins. Virus-penetration was assayed to determine if UL43 protein is required for efficient virus entry. Results: Lack of UL43 expression resulted in significantly reduced plaque sizes of syncytial mutant viruses and inhibited cell fusion induced by gBΔ28 or gKsyn20 (p < 0.05). Deletion of UL43 did not affect overall expression levels of viral glycoproteins gB, gC, gD, and gH on HSV-1(F) BAC infected cell surfaces. Moreover, mutant viruses lacking UL43 gene exhibited slower kinetics of entry into Vero cells than the parental HSV-1(F) BAC. Conclusion: Thus, these results suggest an important role for UL43 protein in mediating virus-induced membrane fusion and efficient entry of virion into target cells.

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.