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      Genetic transformation of ‘Hamlin’ and ‘Valencia’ sweet orange plants expressing the cry11A gene of Bacillus thuringiensis as an additional tool for the management of Diaphorina citri (Hemiptera: Liviidae)

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          Abstract

          <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d6380711e134">The Asian citrus psyllid (ACP) Diaphorina citri Kuwayama (Hemiptera: Liviidae) is the vector of Candidatus Liberibacter spp., the bacteria associated with huanglongbing (HLB), the most devastating disease of citrus worldwide. HLB management has heavily relied on insecticide applications to control the ACP, although there are efforts towards more sustainable alternatives. In previous work, our group assessed the potential bioactivity of different strains of Bacillus thuringiensis (Eubacteriales: Bacillaceae) (Bt) containing cry/cyt genes as feasible tools to control ACP nymphs. Here, we report an attempt to use the cry11A gene from Bt to produce transgenic sweet orange plants using two promoters. For the genetic transformation, 'Hamlin' and 'Valencia' sweet orange seedlings were used as sources of explants. Transgenic plants were detected by polymerase chain reaction (PCR) with specific primers, and the transgene copy number was confirmed by Southern blot analyses. Transcript expression levels were determined by qPCR. Mortality assays of D. citri nymphs were carried out in a greenhouse, and the effect of the events tested ranged from 22% to 43% at the end of the five-day exposure period. To our knowledge, this is the first manuscript reporting the production of citrus plants expressing the Bt cry11A gene for the management of D. citri nymphs. </p>

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            A Revised Medium for Rapid Growth and Bio Assays with Tobacco Tissue Cultures

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              Simultaneous inference in general parametric models.

              Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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                Author and article information

                Contributors
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                Journal
                Journal of Biotechnology
                Journal of Biotechnology
                Elsevier BV
                01681656
                May 2023
                May 2023
                : 368
                : 60-70
                Article
                10.1016/j.jbiotec.2023.04.007
                37088156
                e22840de-b880-48e8-a953-a6acff8d83b9
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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