45 research outputs found

    Burkholderia insecticola sp. nov., a gut symbiotic bacterium of the bean bug Riptortus pedestris

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    A Gram-negative, aerobic, rod-shaped, non-spore-forming, motile bacterium, designated strain RPE64(T), was isolated from the gut symbiotic organ of the bean bug Riptortus pedestris, collected in Tsukuba, Japan, in 2007. 16S rRNA gene sequencing showed that this strain belongs to the Burkholderia glathei Glade, exhibiting the highest sequence similarity to Burkholderia peredens LMG 29314(T) (100%), Burkholderia turbans LMG 29316(T) (99.52%) and Burkholderia ptereochthonis LMG 29326(T) (99.04 %). Phylogenomic analyses based on 107 single-copy core genes and Genome BLAST Distance Phylogeny confirmed B. peredens LMG 29314(T), B. ptereochthonis LMG 29326(T) and several uncultivated, endophytic Burkholderia species as its nearest phylogenetic neighbours. Digital DNA-DNA hybridization experiments unambiguously demonstrated that strain RPE64(T) represents a novel species in this lineage. The G+C content of its genome was 63.2 mol%. The isoprenoid quinone was ubiquinone 8 and the predominant fatty acid components were C-16:0, C-18:1 omega 7c and C-17:0 cyclo. The absence of nitrate reduction and the capacity to grow at pH 8 clearly differentiated strain RPE64(T) from related Burkholderia species. Based on these genotypic and phenotypic characteristics, strain RPE64(T) is classified as representing a novel species of the genus Burkholderia, for which the name Burkholderia insecticola sp. nov. is proposed. The type strain is RPE64(T) (=NCIMB 15023(T)=JCM 31142(T))

    Genomic Comparison of Insect Gut Symbionts from Divergent Burkholderia Subclades

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    Stink bugs of the superfamilies Coreoidea and Lygaeoidea establish gut symbioses with environmentally acquired bacteria of the genus Burkholderia sensu lato. In the genus Burkholderia, the stink bug-associated strains form a monophyletic clade, named stink bug-associated beneficial and environmental (SBE) clade (or Caballeronia). Recently, we revealed that members of the family Largidae of the superfamily Pyrrhocoroidea are associated with Burkholderia but not specifically with the SBE Burkholderia; largid bugs harbor symbionts that belong to a clade of plant-associated group of Burkholderia, called plant-associated beneficial and environmental (PBE) clade (or Paraburkholderia). To understand the genomic features of Burkholderia symbionts of stink bugs, we isolated two symbiotic Burkholderia strains from a bordered plant bug Physopellta gutta (Pyrrhocoroidea: Largidae) and determined their complete genomes. The genome sizes of the insect-associated PBE (iPBE) are 9.5 Mb and 11.2 Mb, both of which are larger than the genomes of the SBE Burkholderia symbionts. A whole-genome comparison between two iPBE symbionts and three SBE symbionts highlighted that all previously reported symbiosis factors are shared and that 282 genes are specifically conserved in the five stink bug symbionts, over one-third of which have unknown function. Among the symbiont-specific genes, about 40 genes formed a cluster in all five symbionts; this suggests a “symbiotic island” in the genome of stink bug-associated Burkholderia

    Dynamic Programming for Creating Cooperative Behavior of Two Soccer Robots -Part 1: Computation of State-Action Map

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    Abstract-To solve decision making problems of multi-agent systems, researchers have devised complicated methods, which are expected to solve curse of dimensionality. In this paper, we go to the opposite extreme. We generate cooperative behavior of two soccer robots with a simple dynamic programming (DP), which was proposed in '50s. Through the example, the ability of DP on a recent computer is measured and evaluated both qualitatively and quantitatively. We then show that the simple structure of DP is useful in obtaining behavior of robots in a convincing way. In the implementation of DP, space that is spanned by eight variables for decision making is divided into 610 million states. DP solves the optimal actions of two robots in every division and creates a look-up table, which is called a state-action map. The ability of this state-action map is measured by simulation and the result is discussed

    Estimating species sensitivity distributions for microplastics by quantitively considering particle characteristics using a recently created ecotoxicity database

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    Abstract Estimation of a species sensitivity distribution (SSD) by fitting a statistical distribution to ecotoxicity data is a promising approach to deriving “safe” concentrations for microplastics. However, most existing SSDs do not quantitatively consider the diverse characteristics of microplastics, such as particle size and shape. To address this issue, based on 38 mass-based chronic no observed effect concentrations (NOECs) obtained from a recently created database, we estimated SSDs that quantitatively consider the influences of three types of microplastic characteristics (particle length, shape, and polymer type) and habitat of the test species (freshwater vs. marine) by using Bayesian modeling. We selected the best SSD model among all possible models using the widely applicable information criterion. The best SSD model included particle length (range: 0.05–280 μm) and a binary dummy variable corresponding to the fiber shape. Lower chronic NOECs were associated with decreasing particle size and with toxicity tests that included fibers in this model. Combined with the fact that the null model (i.e., an SSD model with no predictor variable) was ranked 27th among the 64 candidate SSD models, our results support the need to incorporate particle characteristics such as length and shape (e.g., fiber) into estimations of SSDs for microplastics. The medians of the hazardous concentration of 5% of species (HC5) for microplastic spheres and fragments, estimated by the posterior distributions of individual parameters in the best SSD model, ranged from 0.02 to 2 µg/L, depending on the particle length (0.1–100 μm). For microplastic fibers, the HC5 values were estimated to be approximately 100 times lower than those for microplastic spheres and fragments with the same particle length. However, the 95% Bayesian credible intervals for HC5 estimates for fibers were considerable, expanded by up to five orders of magnitude. Despite many remaining challenges, the Bayesian SSD modeling utilized in this study provides unique opportunities to simultaneously investigate the influences of multiple microplastic characteristics on the NOECs of multiple species, which would otherwise be difficult to discern

    Comparison of Drive Counts and Mark-Resight As Methods of Population Size Estimation of Highly Dense Sika Deer (Cervus nippon) Populations.

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    Assessing temporal changes in abundance indices is an important issue in the management of large herbivore populations. The drive counts method has been frequently used as a deer abundance index in mountainous regions. However, despite an inherent risk for observation errors in drive counts, which increase with deer density, evaluations of the utility of drive counts at a high deer density remain scarce. We compared the drive counts and mark-resight (MR) methods in the evaluation of a highly dense sika deer population (MR estimates ranged between 11 and 53 individuals/km2) on Nakanoshima Island, Hokkaido, Japan, between 1999 and 2006. This deer population experienced two large reductions in density; approximately 200 animals in total were taken from the population through a large-scale population removal and a separate winter mass mortality event. Although the drive counts tracked temporal changes in deer abundance on the island, they overestimated the counts for all years in comparison to the MR method. Increased overestimation in drive count estimates after the winter mass mortality event may be due to a double count derived from increased deer movement and recovery of body condition secondary to the mitigation of density-dependent food limitations. Drive counts are unreliable because they are affected by unfavorable factors such as bad weather, and they are cost-prohibitive to repeat, which precludes the calculation of confidence intervals. Therefore, the use of drive counts to infer the deer abundance needs to be reconsidered
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