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      Plant resistance does not compromise parasitoid-based biocontrol of a strawberry pest

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          Abstract

          Plant nutritional  quality can influence interactions between herbivores and their parasitoids. While most previous work has focused on a limited set of secondary plant metabolites, the tri-trophic effects of overall phenotypic resistance have been understudied. Furthermore, the joint effects of secondary and primary metabolites on parasitoids are almost unexplored. In this study, we compared the performance and survival of the parasitoid species Asecodes parviclava Thompson on wild woodland strawberry ( Fragaria vesca L.) genotypes showing variation in resistance against the parasitoid’s host, the strawberry leaf beetle ( Galerucella tenella L.). Additionally, we related the metabolic profiles of these plant genotypes to the tritrophic outcomes in order to identify primary and secondary metabolites involved in regulating plant potential to facilitate parasitism. We found that parasitoid performance was strongly affected by plant genotype, but those differences in plant resistance to the herbivore were not reflected in parasitoid survival. These findings could be explained in particular by a significant link between parasitoid survival and foliar carbohydrate levels, which appeared to be the most important compounds for parasitism success. The fact that plant quality strongly affects parasitism should be further explored and utilized in plant breeding programs for a synergistic application in sustainable pest management.

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          Most cited references51

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          New approaches for unravelling reassortment pathways

          Background Every year the human population encounters epidemic outbreaks of influenza, and history reveals recurring pandemics that have had devastating consequences. The current work focuses on the development of a robust algorithm for detecting influenza strains that have a composite genomic architecture. These influenza subtypes can be generated through a reassortment process, whereby a virus can inherit gene segments from two different types of influenza particles during replication. Reassortant strains are often not immediately recognised by the adaptive immune system of the hosts and hence may be the source of pandemic outbreaks. Owing to their importance in public health and their infectious ability, it is essential to identify reassortant influenza strains in order to understand the evolution of this virus and describe reassortment pathways that may be biased towards particular viral segments. Phylogenetic methods have been used traditionally to identify reassortant viruses. In many studies up to now, the assumption has been that if two phylogenetic trees differ, it is because reassortment has caused them to be different. While phylogenetic incongruence may be caused by real differences in evolutionary history, it can also be the result of phylogenetic error. Therefore, we wish to develop a method for distinguishing between topological inconsistency that is due to confounding effects and topological inconsistency that is due to reassortment. Results The current work describes the implementation of two approaches for robustly identifying reassortment events. The algorithms rest on the idea of significance of difference between phylogenetic trees or phylogenetic tree sets, and subtree pruning and regrafting operations, which mimic the effect of reassortment on tree topologies. The first method is based on a maximum likelihood (ML) framework (MLreassort) and the second implements a Bayesian approach (Breassort) for reassortment detection. We focus on reassortment events that are found by both methods. We test both methods on a simulated dataset and on a small collection of real viral data isolated in Hong Kong in 1999. Conclusions The nature of segmented viral genomes present many challenges with respect to disease. The algorithms developed here can effectively identify reassortment events in small viral datasets and can be applied not only to influenza but also to other segmented viruses. Owing to computational demands of comparing tree topologies, further development in this area is necessary to allow their application to larger datasets.
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            Plant chemistry and natural enemy fitness: effects on herbivore and natural enemy interactions.

            Paul J Ode (2005)
            Tremendous strides have been made regarding our understanding of how host plant chemistry influences the interactions between herbivores and their natural enemies. While most work has focused on plant chemistry effects on host location and acceptance by natural enemies, an increasing number of studies examine negative effects. The tritrophic role of plant chemistry is central to several aspects of trophic phenomena including top-down versus bottom-up control of herbivores, enemy-free space and host choice, and theories of plant defense. Furthermore, tritrophic effects of plant chemistry are important in assessing the degree of compatibility between biological control and plant resistance approaches to pest control. Additional research is needed to understand the physiological effects of plant chemistry on parasitoids. Explicit tests are required to determine whether natural enemies can act as selective forces on plant defense. Finally, further studies of natural systems are crucial to understanding the evolution of multitrophic relationships.
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              Extraction and GC/MS analysis of the human blood plasma metabolome.

              Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of approximately 0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 microM. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.
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                Author and article information

                Contributors
                daniela.weber@slu.se
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                3 April 2020
                3 April 2020
                2020
                : 10
                : 5899
                Affiliations
                [1 ]ISNI 0000 0000 8578 2742, GRID grid.6341.0, Department of Plant Protection Biology, , Swedish University of Agricultural Sciences, ; Box 102, 23053 Alnarp, Sweden
                [2 ]ISNI 0000 0001 2097 1371, GRID grid.1374.1, Biodiversity Unit, , University of Turku, ; 20014 Turku, Finland
                [3 ]ISNI 0000 0001 1034 3451, GRID grid.12650.30, Department of Ecology and Environmental Science, , Umeå University, ; 90187 Umeå, Sweden
                Article
                62698
                10.1038/s41598-020-62698-1
                7125231
                32246069
                430f076b-e90e-4d68-aa87-9f4d6a898b56
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 October 2019
                : 16 March 2020
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                © The Author(s) 2020

                Uncategorized
                agroecology,ecosystem services,entomology
                Uncategorized
                agroecology, ecosystem services, entomology

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