Many endophytes have beneficial effects on plants and can be exploited in biotechnological applications. Studies hypothesize that only 0.001–1% of all plant-associated bacteria are cultivable. Moreover, even after successful isolations, many endophytic bacteria often show reduced regrowth capacity. This research aimed to optimize isolation processes and culturing these bacteria afterwards. We compared several minimal and complex media in a screening. Beside the media themselves, two gelling agents and adding plant extract to media were investigated to enhance the number and diversity of endophytes as well as the growth capacity when regrown after isolation.In this work, 869 medium delivered the highest numbers of cultivable bacteria, as well as the highest diversity. When comparing gelling agents, no differences were observed in the numbers of bacteria. Adding plant extract to the media lead to a slight increase in diversity. However, when adding plant extract to improve the regrowth capacity, sharp increases of viable bacteria occurred in both rich and minimal media.
This Report presents the results from EFSA project RC/EFSA/AMU/2016/01 related to the implementation of machine learning techniques for literature reviews and systematic reviews in EFSA. An overview of the different steps of a systematic review is provided, along with possible ways for automation. Although it was found that most steps could benefit from automation, it was also observed that some steps require more sophisticated methods than those encompassed within the machine learning framework. Availability of data and methodology allowed for the development of an automatic screening tool based on several machine learning techniques. The developed shiny R application can be used for the screening of abstracts and full texts. Properties of machine learning techniques are discussed in this Report together with their most important advantages and disadvantages. The latter discussion includes both general properties, as well as context-specific properties based on their performance in three case studies. Although creating a universal automatic data extraction tool was considered to be infeasible in this stage, this step of the systematic review was addressed to allow the reviewer to scan the uploaded pdf files for certain words or string of words. Based on observations from the performed case studies, recommendations were made regarding which methods are preferred in specific situations. More explicitly, a discussion is made about the performance of the classifiers with respect to the magnitude of the pool of papers to be screened as well as to the amount of imbalance, referring to the proportion of relevant and irrelevant papers. Finally, it was concluded that the results presented in this report provide proof that the developed shiny application could be efficiently used in combination with other software such as DistillerSR. © European Food Safety Authority, 2018Key words: Systematic Reviews, Machine Learning, screening, data extraction, Sensitivity, Specificity Disclaimer: The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Reproduction is authorised provided the source is acknowledged. Machine Learning Techniques for Literature and Systematic Reviewswww.efsa.europa.eu/publications 3 EFSA Supporting publication 2018:EN-1427The present document has been produced and adopted by the bodies identified above as author. This task has been carried out exclusively by the author in the context of a contract ...
Antimicrobial resistance has become one of the main public health burdens of the last decades, and monitoring the development and spread of non-wild-type isolates has therefore gained increased interest. Monitoring is performed based on the minimum inhibitory concentration (MIC) values, which are collected through the application of dilution experiments. In order to account for the unobserved population heterogeneity of wild-type and non-wild-type isolates, mixture models are extremely useful. Instead of estimating the entire mixture globally, it was our major aim to provide an estimate for the wild-type first component only. The characteristics of this first component are not expected to change over time, once the wild-type population has been confidently identified for a given antimicrobial. With this purpose, we developed a new method based on the multinomial distribution, and we carry out a simulation study to study the properties of the new estimator. Because the new approach fits within the likelihood framework, we can compare distinct distributional assumptions in order to determine the most suitable distribution for the wild-type population. We determine the optimal parameters based on the AIC criterion, and attention is also paid to the model-averaged approach using the Akaike weights. The latter is thought to be very suitable to derive specific characteristics of the wild-type distribution and to determine limits for the wild-type MIC range. In this way, the new method provides an elegant means to compare distinct distributional assumptions and to quantify the wild-type MIC distribution of specific antibiotic-bacterium combinations.
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