Characterization of glutathione S transferases from the plant-parasitic nematode, Bursaphelenchus xylophilus

We have previously identified two secreted glutathione S-transferases (GST) expressed in the pharyngeal gland cell of Bursaphelenchus xylophilus, which are upregulated post infection of the host. This study examines the functional role of GSTs in B. xylophilus biology. We analysed the expression profiles of all predicted GSTs in the genome and the results showed that they belong to kappa and cytosolic subfamilies and the majority are upregulated post infection of the host. A small percentage is potentially secreted and none is downregulated post infection of the host. One secreted protein was confirmed as a functional GST and is within a cluster that showed the highest expression fold change in infection. This enzyme has a protective activity that may involve host defences, namely in the presence of terpenoid compounds and peroxide products. These results suggest that GSTs secreted into the host participate in the detoxification of host-derived defence compounds and enable successful parasitism.

Improving Bangla ocr output through correction algorithms

Bangla OCR (Optical Character Recognition) is a long deserving software for Bengali community all over the world. Numerous e efforts suggest that due to the inherent complex nature of Bangla alphabet and its word formation process development of high fidelity OCR producing a reasonably acceptable output still remains a challenge. One possible way of improvement is by using post processing of OCR’s output; algorithms such as Edit Distance and the use of n-grams statistical information have been used to rectify misspelled words in language processing. This work presents the first known approach to use these algorithms to replace misrecognized words produced by Bangla OCR. The assessment is made on a set of fifty documents written in Bangla script and uses a dictionary of 541,167 words. The proposed correction model can correct several words lowering the recognition error rate by 2.87% and 3.18% for the character based n- gram and edit distance algorithms respectively. The developed system suggests a list of 5 (five) alternatives for a misspelled word. It is found that in 33.82% cases, the correct word is the topmost suggestion of 5 words list for n-gram algorithm while using Edit distance algorithm the first word in the suggestion properly matches 36.31% of the cases. This work will ignite rooms of thoughts for possible improvements in character recognition endeavour.