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249 results for “plant pathogen”
FIGURE 2 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 2. Curvularia australis (IMI 261917). A) Conidiophores on Sporobolus caroli leaf B) Conidiophores and conidia C–F) Conidia. (Scale bars A = 10 μm B = 20 μm C–F = 5μm).
FIGURE 1. Phylogram generated for Curvularia from a in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 1. Phylogram generated for Curvularia from a maximum likelihood analysis based on the combined ITS, GPDH and TEF alignment. Maximum likelihood bootstrap values are shown above the branches. The thickened branches correspond to Bayesian posterior probability values more than 0.7. All ex-type cultures are printed in bold.
FIGURE 5 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 5. Curvularia hawaiiensis (IMI 213864) A). Ascomata covered with conidiophores B, C) Cross section of ascomata D–H) Asci I, J) Conidiophores and conidia K–M) Conidia. (Scale bars A = 100 μm B, C = 50 μm D, F, G = 20 μm E, I, J = 10 μm K–M = 5 μm).
FIGURE 4 in A taxonomic and phylogenetic re-appraisal of the genus Curvularia (Pleosporaceae): human and plant pathogens
FIGURE 4. Curvularia cymbopogonis (IMI 130402). A) Ascomata produced in culture B) Cross section of ascomata C) Peridium wall with asci D) Ascus and pseudoparaphyses E–G) Asci H) Ascospore I) Conidia and conidiophores J–L) Conidia. (Scale bars A = 1000 μm B = 100 μm C–I = 10 μm J–L = 10 μm).
Two novel Trichoderma species and their antagonistic activity against sclerotia-producing plant pathogens
<p>Alignments used to reconstruct the phylogenetic trees in the manuscript.</p>
Metabolomics of Tomato Plants Inoculated with Beneficial Bacteria and Infected with Pathogen Alternaria solani
<p>Image foles of Metabolomics data generated on tomato inoculated with bacteria and infected with pathogen using LC-ESI-MS/MS.</p>
Data from: Investigating the production of sexual resting structures in a plant pathogen reveals unexpected self-fertility and genotype-by-environment effects
The sexual stage of pathogens governs recombination patterns and often also provides means of surviving the off-season. Despite its importance for evolutionary potential and between-season epidemiology, sexual systems have not been carefully investigated for many important pathogens, and what generates variation in successful sexual reproduction of pathogens remains unexplored. We surveyed the sexually produced resting structures (chasmothecia) across 86 natural populations of fungal pathogen Podosphaera plantaginis (Ascomycota) naturally infecting Plantago lanceolata in the Åland archipelago, southwest of Finland. For this pathosystem, these resting structures are a key life-history stage, as more than half of the local pathogen populations go extinct every winter. We uncovered substantial variation in the level of chasmothecia produced among populations, ranging from complete absence to presence on all infected leaves. We found that chasmothecia developed within clonal isolates (single strain cultures). Additionally, these clonal isolates all contained both MAT1-1-1 and MAT1-2-1 genes that characterize mating-types in Ascomycetes. Hence, contrary to expectations, we conclude that this species is capable of haploid selfing. In controlled inoculations we discovered that pathogen genotypes varied in their tendency to produce chasmothecia. Production of chasmothecia was also affected by ambient temperature (E), and by the interaction between temperature and pathogen genotype (G × E). These G, E and G × E effects found both at a European scale, as well as within Åland, may partly explain the high variability observed among populations in chasmothecia levels. Consequently, they may be key drivers of the evolutionary potential and epidemiology of this highly dynamic pathosystem.
Figure 1 in Leaf-mining beetles carry plant pathogenic fungi amongst hosts
Figure 1. (A) A pandanus tree (Pandanus boninensis, height: 1.5–2.0 m), a species endemic to the Ogasawara Islands. (B) A leaf mined by Phylloplatypus pandani. (C) Phylloplatypus pandani using the entrance hole of a mine. (D) Phylloplatypus pandani within a mine. Scale bars represent 1.0 mm.
Data from: Nitrogen addition and warming modulate the pathogen impact on plant biomass by shifting intraspecific functional traits and reducing species richness
<p><span>1. </span><span>Foliar fungal pathogens can substantially reduce plant biomass. This effect can be modulated by environment conditions, such as soil nitrogen availability and air temperature. The ongoing global changes are altering these variables and thus interact with pathogens to influence plant biomass, but experimental test of their interactions is scarce. </span></p> <p><span>2. </span><span>We conducted a 4-year field experiment in a Tibetan alpine meadow to examine the interactive effects of nitrogen addition, warming and foliar pathogens (via fungicide application) on plant biomass. We also measured plant functional traits, species richness and abundance to test the possible mechanisms underlying these interactions. </span></p> <p><span>3. </span><span>Our results showed that foliar fungal pathogens reduced plant community biomass under nitrogen addition, which in turn weakened the positive nitrogen effect on community biomass. Mechanistically, nitrogen addition shifted the plant communities towards fast-growing traits; this happened predominantly because of changes in within-species trait values, including an increase in specific leaf area and height. These trait changes resulted in greater suppression of plant biomass by pathogens, likely because of the trade-offs associated with the allocation of resources to plant growth and defense. Moreover, the reduction in species richness amplified the pathogen effect under nitrogen addition due to the increased density and susceptibility of the most dominant species (i.e. Kobresia capillifolia). Furthermore, warming did not interact with pathogens and nitrogen addition to influence plant community biomass, but their three-way interaction modified the biomass of K. capillifolia. Specifically, warming enhanced the positive effect of nitrogen addition on the biomass of K. capillifolia in the fungicide, low infection plots, while it weakened the nitrogen effect in the no fungicide, high infection plots.</span></p> <p><span>4. </span><span>Synthesis:</span> <span>Our results demonstrate how pathogens interact with nitrogen addition and warming to influence the biomass of dominant species and the whole plant community. Our study highlights the importance of considering foliar fungal pathogens when assessing ecosystem responses to multiple global change factors.</span></p>
Nitrogen enrichment and foliar fungal pathogens affect the mechanisms of multispecies plant coexistence
<p>This is the data and code repository for the manuscript entitled "Nitrogen enrichment and foliar fungal pathogens affect the mechanisms of multispecies plant coexistence".</p> <p>DATA:</p> <p><strong>data.txt</strong></p> <p>Dataset collected in the PaNDiv experiment, a large field experiment in Münchenbuchsee (near Bern) which investigates the mechanisms by which nitrogen enrichment affects ecosystem functioning. The dataset is composed by the following elements:</p> <ul> <li><em>year </em>--- 2017 or 2018</li> <li><em>block </em>--- experimental block in the PaNDiv experiment (1, 2, 3 or 4)</li> <li><em>plot </em>--- experimental plot in the PaNDiv experiment (from 1 to 336)</li> <li><em>nitrogen </em>--- addition of nitrogen to the soil (0 = no, 1 = yes)</li> <li><em>fungicide </em>--- application of fungicide to the vegetation (0 = no, 1 = yes)</li> <li><em>treatment</em> --- control, nitrogen addition, fungicide application, and their combined effect</li> <li><em>number</em> --- replicate number of the focal species; numbers are repeated because it restarts with each target focal-neighbour species combination (not shown)</li> <li><em>focal_sp</em> --- 8 possible species: <ul> <li>tar_off = <em>Taraxacum officinale</em></li> <li>cre_bie = <em>Crepis biennis</em></li> <li>rum_ace = <em>Rumex acetosa</em></li> <li>dac_glo = <em>Dactylis glomerata</em></li> <li>ant_odo = <em>Anthoxanthum odoratum</em></li> <li>cen_jac = <em>Centaurea jacea</em></li> <li>sal_pra = <em>Salvia pratensis</em></li> <li>pla_med = <em>Plantago media</em></li> </ul> </li> <li><em>biomass_i </em>--- initial biomass of the focal plant (start of the growing season; February/March)</li> <li><em>biomass_f</em> --- final biomass of the focal plant (end of the growing season; June)</li> <li><em>tar_off</em> --- visually estimated cover for this species as a neighbour</li> <li><em>cre_bie</em> --- visually estimated cover for this species as a neighbour</li> <li><em>rum_ace</em> --- visually estimated cover for this species as a neighbour</li> <li><em>dac_glo</em> --- visually estimated cover for this species as a neighbour</li> <li><em>ant_odo</em> --- visually estimated cover for this species as a neighbour</li> <li><em>cen_jac</em> --- visually estimated cover for this species as a neighbour</li> <li><em>sal_pra</em> --- visually estimated cover for this species as a neighbour</li> <li><em>pla_med</em> --- visually estimated cover for this species as a neighbour</li> <li><em>herbs </em>--- visually estimated cover for non-target herb species in the PaNDiv experiment</li> <li><em>grasses </em>--- visually estimated cover for non-target grass species in the PaNDiv experiment</li> <li><em>legumes </em>--- visually estimated cover for non-target legume species in the PaNDiv experiment</li> </ul> <p> </p> <p>CODE:</p> <p><strong>001-optimx.R</strong></p> <p>Code that uses maximum likelihood to fit population models to the data. Produces several datasets with model coefficients and AIC values.</p> <p> </p> <p><strong>002-model_sel_coefs.R</strong></p> <p>Code to select the coefficients based on the best model and add the size effect of the focal plants when needed. Creates all interaction matrices and intrinsic growth rate vectors.</p> <p> </p> <p><strong>003-final_matrices.R</strong></p> <p>Code to adjust the matrices for coexistence computing.</p> <p> </p> <p><strong>004-coexistence.R</strong></p> <p>Computes structural coexistence outputs. Provides a clean dataset with structural niche differences, structural fitness differences, and other multispecies coexistence metrics.</p>
Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves
<p>Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves</p>
Characterizing the Plant-Pathogen Interactions of Harringtonia lauricola with Susceptible Lauraceae
<p>Supporting datasets.</p>
Data from: The genetic structure of the plant pathogenic fungus Melampsora larici-populina on its wild host is extensively impacted by host domestication
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Raw data: The timing and asymmetry of plant-pathogen-insect interactions
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Data from: Disentangling the genetic origins of a plant pathogen during disease spread using an original molecular epidemiology approach
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Data from: The role of habitat filtering in the leaf economics spectrum and plant susceptibility to pathogen infection
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Data from: Understanding the recent colonization history of a plant pathogenic fungus using population genetic tools and Approximate Bayesian Computation
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Data from: Recent range expansion and agricultural landscape heterogeneity have only minimal effect on the spatial genetic structure of the plant pathogenic fungus Mycosphaerella fijiensis
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Data from: The effects of rainforest fragment area on the strength of plant-pathogen interactions
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Data from: The chestnut blight fungus world tour: successive introduction events from diverse origins in an invasive plant fungal pathogen.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.