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Find video protocols related to scientific articles indexed in Pubmed.
Biological control of Xanthomonas Oryzae pv. Oryzae causing rice bacterial blight disease by Streptomyces toxytricini VN08-A-12, isolated from soil and leaf-litter samples in Vietnam.
Biocontrol Sci
PUBLISHED: 09-26-2014
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A total of 2690 actinomycete strains were screened as potential biological control agents in controlling rice bacterial blight (BB) in Vietnam. From these microorganisms, seventeen actinomycete strains were found to be capable of inhibiting all 10 major Xoo races isolated from Xoo-infected rice leaves. One strain, namely VN08-A-12, contained effective characteristics in selectively inhibiting all 10 races in vitro, but did not inhibit most of the other tested microorganisms. Therefore, VN08-A-12 was subsequently selected for rice field trials for two seasons on two rice cultivars SS1 and KD18. Results showed VN08-A-12 was not only able to reduce Xoo lesion lengths in the two rice cultivars (lesion length reduction of up to 38.3%), but it also significantly reduced Xoo-related yield loss in infected rice cultivars from the field (yield loss reduction of up to 43.2%). Interestingly, the culture of this strain also increased the rice yield in healthy rice cultivars (from 2.66% to 16.98% for SS1 and from 3.11% to 5.94% for KD18 cultivar). The strain VN08-A-12 was shown to be identical to Streptomyces toxytricini. To our knowledge, this is the first study reporting S. toxytricini as a beneficial biological agent for the control of BB in rice.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.