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      Lasiodiplodia syzygii sp. nov. (Botryosphaeriaceae) causing post-harvest water-soaked brown lesions on Syzygium samarangense in Chiang Rai, Thailand

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          Abstract

          Background

          Syzygium samarangense (Wax apple) is an important tropical fruit tree with high economic and nutrient value and is widely planted in the tropics or subtropics of Asia. Post-harvest water-soaked brown lesions were observed on mature fruits of ornamental wax apples in Chiang Rai Province, Thailand. A fungus with morphological characters, similar to Lasiodiplodia , was consistently isolated from symptomatic fruits. Phylogenetic analyses, based on ITS, LSU, TEF1-a and tub2, revealed that our isolates were closely related to, but phylogenetically distinct from, Lasiodiplodia rubropurpurea .

          New information

          Morphological comparisons indicated that pycnidia and conidiogenous cells of our strains were significantly larger than L. rubropurpurea . Comparisons of base-pair differences in the four loci confirmed that the species from wax apple was distinct from L. rubropurpurea and a new species, L. syzygii sp. nov., is introduced to accommodate it. Pathogenicity tests confirmed the newly-introduced species as the pathogen of this post-harvest water-soaked brown lesion disease on wax apples.

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          Most cited references 41

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          IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

          Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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            MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.

            We announce the release of an advanced version of the Molecular Evolutionary Genetics Analysis (MEGA) software, which currently contains facilities for building sequence alignments, inferring phylogenetic histories, and conducting molecular evolutionary analysis. In version 6.0, MEGA now enables the inference of timetrees, as it implements the RelTime method for estimating divergence times for all branching points in a phylogeny. A new Timetree Wizard in MEGA6 facilitates this timetree inference by providing a graphical user interface (GUI) to specify the phylogeny and calibration constraints step-by-step. This version also contains enhanced algorithms to search for the optimal trees under evolutionary criteria and implements a more advanced memory management that can double the size of sequence data sets to which MEGA can be applied. Both GUI and command-line versions of MEGA6 can be downloaded from www.megasoftware.net free of charge.
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              MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space

              Since its introduction in 2001, MrBayes has grown in popularity as a software package for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) methods. With this note, we announce the release of version 3.2, a major upgrade to the latest official release presented in 2003. The new version provides convergence diagnostics and allows multiple analyses to be run in parallel with convergence progress monitored on the fly. The introduction of new proposals and automatic optimization of tuning parameters has improved convergence for many problems. The new version also sports significantly faster likelihood calculations through streaming single-instruction-multiple-data extensions (SSE) and support of the BEAGLE library, allowing likelihood calculations to be delegated to graphics processing units (GPUs) on compatible hardware. Speedup factors range from around 2 with SSE code to more than 50 with BEAGLE for codon problems. Checkpointing across all models allows long runs to be completed even when an analysis is prematurely terminated. New models include relaxed clocks, dating, model averaging across time-reversible substitution models, and support for hard, negative, and partial (backbone) tree constraints. Inference of species trees from gene trees is supported by full incorporation of the Bayesian estimation of species trees (BEST) algorithms. Marginal model likelihoods for Bayes factor tests can be estimated accurately across the entire model space using the stepping stone method. The new version provides more output options than previously, including samples of ancestral states, site rates, site d N /d S rations, branch rates, and node dates. A wide range of statistics on tree parameters can also be output for visualization in FigTree and compatible software.
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                Author and article information

                Contributors
                Journal
                Biodivers Data J
                Biodivers Data J
                1
                urn:lsid:arphahub.com:pub:F9B2E808-C883-5F47-B276-6D62129E4FF4
                urn:lsid:zoobank.org:pub:245B00E9-BFE5-4B4F-B76E-15C30BA74C02
                Biodiversity Data Journal
                Pensoft Publishers
                1314-2836
                1314-2828
                2021
                07 January 2021
                : 9
                Affiliations
                [1 ] Department of Plant Pathology, Agricultural College, Guizhou University, Guiyang, China Department of Plant Pathology, Agricultural College, Guizhou University Guiyang China
                [2 ] Guiyang plant protection and inspection station, Guiyang, China Guiyang plant protection and inspection station Guiyang China
                [3 ] Center of Excellence in Fungal Research, Mae Fah Luang University, Chiang Rai, Thailand Center of Excellence in Fungal Research, Mae Fah Luang University Chiang Rai Thailand
                [4 ] School of Science, Mae Fah Luang University, Chiang Rai, Thailand School of Science, Mae Fah Luang University Chiang Rai Thailand
                Author notes
                Corresponding author: Yong Wang ( yongwangbis@ 123456aliyun.com ).

                Academic editor: Renan Barbosa

                Article
                60604 15117
                10.3897/BDJ.9.e60604
                7809010
                Chao-Rong Meng, Qian Zhang, Zai-Fu Yang, Kun Geng, Xiang-Yu Zeng, K. W. Thilini Chethana, Yong Wang

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 3, Tables: 2, References: 36
                Categories
                Taxonomic Paper
                Fungi
                Biodiversity & Conservation
                Cenozoic
                Asia

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