Ripening of fruit is a very important process but in some fruits early ripening leads to a great damage during long distance transportation. There are various biochemical changes taking place during the phase of ripening of fruit such as changes in respiration, aroma, flavor, ethylene production and activity of cell wall degrading enzymes. Some important cell wall degrading enzymes are Polygalacturonase (PG), Pectin methylesterase (PME), Pectin lyase, RGase. PME is known to act as a cell wall hydrolyzing enzyme, responsible for demethyl esterification of cell wall polygalacturonan. The present study includes the biochemical and molecular characterization of PME from Grand naine variety of Musa acuminata (banana). This study also deals with the in-silico study reflecting inhibition of PME activity in context to delayed ripening in banana. It mainly deals with the identification of a PME1 gene from Grand naine variety of banana. The expression of this gene is related with the process of ripening. The expression of PME1 gene was observed to be peaked on 3rd day in ethylene treated samples of banana but the activity in untreated samples called control was rather slow and then there was a sudden decrease in their activity in both treated as well as untreated samples. With the help of in-silico study, we observed that banana has maximum homology with carrot by using cross species analysis.The designed model has been reported to be of good quality on the basis of its verification and validation. The designed model was observed to be appropriate for docking. The information of binding sites of ligand provides new insights into the predictable functioning of relevant protein.
Large numbers of algorithms have been proposed to solve shortest path query problems for static or timedependent spatial networks; however, these algorithms do not perform well to find the nearest shelter with fastest paths in disaster situations. In disasters, path computed through existing algorithms and saved as the fastest might become damaged. ONSC approach provide optimal path in disaster situation but do not deal with congestion control. To solve this problem, this paper proposes a method to reduce the travelling time with an existing dynamic network model, which is called an event-dependent network, to represent a spatial network in a disaster which help the people to choose the optimal path by giving weight-factor(in percentage) of the congestion in the road network.
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