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For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the cooccurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM), a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k-means, hierarchical clustering and the incorporation of the SOM analysis into criteria based approaches to assess pest risk.
This study presents the development and mapping of simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers in chickpea. The mapping population is based on an inter-specific cross between domesticated and non-domesticated genotypes of chickpea (Cicer arietinum ICC 4958 × C. reticulatum PI 489777). This same population has been the focus of previous studies, permitting integration of new and legacy genetic markers into a single genetic map. We report a set of 311 novel SSR markers (designated ICCM—ICRISAT chickpea microsatellite), obtained from an SSR-enriched genomic library of ICC 4958. Screening of these SSR markers on a diverse panel of 48 chickpea accessions provided 147 polymorphic markers with 2–21 alleles and polymorphic information content value 0.04–0.92. Fifty-two of these markers were polymorphic between parental genotypes of the inter-specific population. We also analyzed 233 previously published (H-series) SSR markers that provided another set of 52 polymorphic markers. An additional 71 gene-based SNP markers were developed from transcript sequences that are highly conserved between chickpea and its near relative Medicago truncatula. By using these three approaches, 175 new marker loci along with 407 previously reported marker loci were integrated to yield an improved genetic map of chickpea. The integrated map contains 521 loci organized into eight linkage groups that span 2,602 cM, with an average inter-marker distance of 4.99 cM. Gene-based markers provide anchor points for comparing the genomes of Medicago and chickpea, and reveal extended synteny between these two species. The combined set of genetic markers and their integration into an improved genetic map should facilitate chickpea genetics and breeding, as well as translational studies between chickpea and Medicago.
The Facility for Antiproton and Ion Research (FAIR), under construction at Darmstadt will provide intense relativistic beams of exotic nuclei at its Superconducting-FRagment Separator. High-resolution in-beam γ-ray spectroscopy will be performed in the HISPEC experiment, using the European Advanced GAmma-ray Tracking Array (AGATA). The PreSPEC-AGATA campaign is the predecessor of HISPEC and runs from 2012 to 2014 at GSI Helmholtzzentrum für Schwerionenforschung GmbH. Up to19 AGATA modules were used at GSI's F Ragment Separator in 2012. We report on the status of the experiment including preliminary results from performance commissioning.
The polarization of Λ and Λ¯ hyperons along the beam direction has been measured relative to the second and third harmonic event planes in isobar Ru+Ru and Zr+Zr collisions at √sNN = 200 GeV. This is the first experimental evidence of the hyperon polarization by the triangular flow originating from the initial density fluctuations. The amplitudes of the sine modulation for the second and third harmonic results are comparable in magnitude, increase from central to peripheral collisions, and show a mild pT dependence. The azimuthal angle dependence of the polarization follows the vorticity pattern expected due to elliptic and triangular anisotropic flow, and qualitatively disagree with most hydrodynamic model calculations based on thermal vorticity and shear induced contributions. The model results based on one of existing implementations of the shear contribution lead to a correct azimuthal angle dependence, but predict centrality and pT dependence that still disagree with experimental measurements. Thus, our results provide stringent constraints on the thermal vorticity and shear-induced contributions to hyperon polarization. Comparison to previous measurements at RHIC and the LHC for the second-order harmonic results shows little dependence on the collision system size and collision energy.