scholarly journals Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

2022 ◽  
Author(s):  
Mohammad Bozlul Karim ◽  
Shigehiko Kanaya ◽  
Md. Altaf-Ul-Amin
BioScience ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Lydia Yohana Safitri

Endophytic fungi are microorganisms found in healthy plant tissue but not pathogenic to plants, and produce secondary metabolites same host plant. Secondary metabolites can be used as a drug. The possibility of endophytic fungi are found in bamboo, because bamboo betung (Dendrocalamus asper Backer.) Has high potential as a drug, because it contains flavonoids, and phenolic coumarin. These compounds are secondary metabolites that produce antimicrobial substances that are used in the pharmaceutical field and are found in plant tissue. To avoid excessive use of medicinal plants that could lead to the extinction of these plants, then used the role of endophytic fungi are capable of producing secondary metabolites to the fullest. In addition, the endophytic fungus in plant tissue can be more than one type of fungal endophyte that the higher production of secondary metabolites. This study aims to determine the types of isolates of endophytic fungi on the leaves of Dendrocalamus asper and know the antibacterial potency against bacterial endophyte fungus Staphylococcus aureus. The results showed that there were thirteen isolates of endophytic fungi were isolated from the leaves of Dendrocalamus asper. Ten isolates of endophytic fungi have potential as an antibacterial against S. aureus and three isolates of endophytic fungi did not show any antibacterial activity. Key Word: Endophytic fungi, Secondary metabolites, antibacterial activity.


Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler

Author(s):  
P.L. Nikolaev

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.


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