Background: The effect of garlic extracton fungal
growth and keratinolytic activity was studied in
Trichophytonmentagrophytes as one of the major
etiologic agents of human and animal dermatophytosis
in Baghdad and other parts of the World.
Objective: To investigated an alternative
antidermatophyte with minimum side effects which is
plant based and biodegradable natural product
Methods: Culture conditions for 30 isolates of T.
mentagrophytes isolated from human dermatophytosis
from both sexes with ages of 5-63 years in Central
Medical city for the period July 2009 to October 2009
were cultured on specific solid medium.
Results: The aqueous extract of garlic at various
concentrations inhibited the growth of T.
mentagrophytes. This inhibition reached to a maximum
of 100٪ for extract at 10٪concentration. Keratinase
synthesis was also inhibited by the extract about 91٪ at
8 ٪ concentration.
Conclusion: Fungal growth and keratinolytic activity
are important factors in pathogenesis of the
dermatophytes, their inhibition by garlic indicate that
this substance may have potential values for treatment
of human and animal dermatophytosis.
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Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file. In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus,
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... Show MoreNumeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a
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