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102 results for “plant disease”
The conflict between hosts and non-hosts changes the severity of diseases in degraded grassland plant communities
<p>load: disease severity of plant community</p> <p>richnes: species richness</p> <p>Nonhost: Non-hosts richness</p> <p>coverage: relative coverage</p> <p>RHost: relative hosts richness</p> <p>PD: Faith's phylogenetic distance</p> <p>beta: beta diversity of plant community</p> <p>SLA: CWM SLA</p> <p>LN: CWM leaf N content</p> <p>LP:CWM leaf P content</p> <p>NP: CWM leaf N:P</p> <p>group: degree of grassland degradation</p> <p>Shannon: Shannon index of plant community</p> <p>Simpson: Simpsion index of plant community</p> <p>Pielou: Pielou index of plant community</p> <p>marglef: Marglef index of plant community</p>
DiaMOS Plant Dataset: A Dataset for Diagnosis and Monitoring Plant Disease
<p>DiaMOS Plant, is a dataset for diagnosis and monitoring plant disease, collected in the field, consisting of 3505 images, depicting 4 leaf diseases with 4 level of severity and 4 fruit stages.</p> <p> </p> <p>Cite as:</p> <p>Fenu, G.; Malloci, F.M. DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease. Agronomy 2021, 11, 2107. https://doi.org/10.3390/agronomy11112107</p>
Temperature and plants disease simulation to 2038
<p>In this datasets we combined temperatures data broken down by NUTS Level 3 from 1989 to 2018 (link to data: <span>https://doi.org/10.5281/zenodo.5789430</span>) with disease observed in laboratory from scientists of the University of Torin (<span>link to data: https://doi.org/10.5281/zenodo.6323408). </span></p> <p><span>We computed the </span>prediction of<span> temperature using an LSTM algorithm to year 2038 and we associated the result with plant disease evolution observed in the laboratory. </span></p> <p><span>The main purpose of this project is to better understand possible scenario related to </span>increasing of <span>temperatures and plant disease spreading. </span></p>
Durable resistance or efficient disease control? Adult Plant Resistance (APR) at the heart of the dilemma [Dataset]
<p>Dataset of simulation results used in this study (https://doi.org/10.24072/pcjournal.271):</p> <p><strong>Durable resistance or efficient disease control? Adult Plant Resistance (APR) at the heart of the dilemma.</strong></p> <p>published in Peer Community Journal.</p> <p> </p> <p>All simulations are computed using the R package landsepi<br> Package webpage: https://csiro-inra.pages.biosp.inrae.fr/landsepi/<br> To download the package: https://cran.r-project.org/web/packages/landsepi/index.html</p> <p>Raw data used for this study are stored in three folders corresponding to<br> the three numerical experiments (APR_1, APR_2 and APR_3, respectively).</p> <p>Every folder includes:<br> - an R script for the generation of a simulation plan<br> - 10 folders containing simulation results for 10 stochastic replicates<br> - an R script for the analysis of the results and generation of graphics</p> <p>Every folder containing the simulation results includes:<br> - a text file "myDesign.txt" containing the simulation plan and outputs (1 line per simulation)<br> - a text file "parameters.txt" containing a summary of all parameter values<br> - a text file "param_disp_patho.txt" containing the dispersal matrix of the pathogen<br> - a text file "param_landscape.txt" containing the landscape structure<br> (i.e. for every polygon [lines] and every year [columns] the index of the cultivated host)</p>
A Phase III Trial to Assess the Safety and Efficacy of Plant Cell Expressed GCD in Patients With Gaucher Disease
ClinicalTrials.gov study NCT00376168. IPD Sharing: Not stated. Countries: 8. Publications: 3.
Quantitative interactions: the disease outcome of Botrytis cinerea across the plant kingdom
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Data from: Whole plant disease severity is associated with reduced polyphenolic concentrations in lesioned but not green tissue in eelgrass
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Data from: Disease where you dine: plant species and floral traits associated with pathogen transmission in bumble bees
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Contrasting effects of sheep and cattle grazing on foliar fungal diseases by changing plant community characteristics
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Data from: Survival relative to new and ancestral host plants, phytoplasma infection and genetic constitution in host races of a polyphagous insect disease vector
Dissemination of vectorborne diseases depends strongly on the vector's host range and the pathogen's reservoir range. Because vectors interact with pathogens, the direction and strength of a vector's host shift is vital for understanding epidemiology and is embedded in the framework of ecological specialization. This study investigates survival in host-race evolution of a polyphagous insect disease vector, Hyalesthes obsoletus, whether survival is related to the direction of the host shift (from field bindweed to stinging nettle), the interaction with plant-specific strains of obligate vectored pathogens/symbionts (stolbur phytoplasma), and whether survival is related to genetic differentiation between the host races. We used a twice repeated, identical nested experimental design to study survival of the vector on alternative hosts and relative to infection status. Survival was tested with Kaplan–Meier analyses, while genetic differentiation between vector populations was quantified with microsatellite allele frequencies. We found significant direct effects of host plant (reduced survival on wrong hosts) and sex (males survive longer than females) in both host races and relative effects of host (nettle animals more affected than bindweed animals) and sex (males more affected than females). Survival of bindweed animals was significantly higher on symptomatic than nonsymptomatic field bindweed, but in the second experiment only. Infection potentially had a positive effect on survival in nettle animals but due to low infection rates the results remain suggestive. Genetic differentiation was not related to survival. Greater negative plant-transfer effect but no negative effect of stolbur in the derived host race suggests preadaptation to the new pathogen/symbiont strain before strong diversifying selection during the specialization process. Physiological maladaptation or failure to accept the ancestral plant will have similar consequences, namely positive assortative mating within host races and a reduction in the likelihood of oviposition on the alternative plant and thus the acquisition of alternative stolbur strains.
Data from: Museum specimen data reveal emergence of a plant disease may be linked to increases in the insect vector population
The emergence rate of new plant diseases is increasing due to novel introductions, climate change, and changes in vector populations, posing risks to agricultural sustainability. Assessing and managing future disease risks depends on understanding the causes of contemporary and historical emergence events. Since the mid-1990s, potato growers in the western United States, Mexico, and Central America have experienced severe yield loss from Zebra Chip disease and have responded by increasing insecticide use to suppress populations of the insect vector, the potato psyllid, Bactericera cockerelli (Hemiptera: Triozidae). Despite the severe nature of Zebra Chip outbreaks, the causes of emergence remain unknown. We tested the hypotheses that 1) B. cockerelli occupancy has increased over the last century in California and 2) such increases are related to climate change, specifically warmer winters. We compiled a dataset of 87,000 museum specimen occurrence records across the order Hemiptera collected between 1900 and 2014. We then analyzed changes in B. cockerelli distribution using a hierarchical occupancy model using changes in background species lists to correct for collecting effort. We found evidence that B. cockerelli occupancy has increased over the last century. However, these changes appear to be unrelated to climate changes, at least at the scale of our analysis. To the extent that species occupancy is related to abundance, our analysis provides the first quantitative support for the hypothesis that B. cockerelli population abundance has increased, but further work is needed to link B. cockerelli population dynamics to Zebra Chip epidemics. Finally, we demonstrate how this historical macro-ecological approach provides a general framework for comparative risk assessment of future pest and insect vector outbreaks.
Wheat Enhanced Disease Resistance EMS-Mutants Include Lesion-mimics With Adult Plant Resistance to Stripe Rust
<h3>The datasets for '<strong>Wheat Enhanced Disease Resistance EMS-Mutants Include Lesion-mimics With Adult Plant Resistance to Stripe Rust</strong>'.</h3> <p> </p> <p><strong>Main datasets</strong></p> <ul> <li>Kr620_GATK.zip</li> <li>Kr620_QTLseq.zip</li> </ul> <p><strong>Kr620_GATK.zip</strong> includes vcf files generated by mapping wild type Kronos and resistant or susceptible Kr620 into the Kronos genome. <strong>Kr620_QTLseq.zip </strong>includes the bulk segregation analysis outputs using QTL-seq. </p>
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH. in Deep learning brings speed, accuracy to the life sciences.
In 2017, Plantix, a free smartphone app that helps identify plant damage, was introduced to the Indian state of Andhra Pradesh, with an extension partner. Plantix was created by Progressive Environmental and Agricultural Technologies (PEAT), a German startup. Two PEAT cofounders, Charlotte Schuman (second from the right) and Alex Kennepohl (center, with eyeglasses), confer about the smartphone app with students from Angrau University. Farmers and gardeners can transmit their plant images to Plantix, which uses deep learning and computer vision to help identify diseases and pests. The smartphone app offers symptom descriptions, treatment recommendations, and potential preventive actions. Photographs: Courtesy of PEAT GmbH.
Coffee Plant Leaf Disease
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Developing epidemiological preparedness for a probable plant disease invasion: modelling citrus huánglóngbìng in the European Union
<p>Video 1: Spread of the vector in a single simulation in Region A (Valencia). Corresponds to Fig S10 in supplementary material. Maps show the measure of vector density within each cell and light grey shows initial exposure.</p> <p>Video 2: Spread of the pathogen in a single simulation in Region A (Valencia). Corresponds to Fig 3 in main text. Both vector and pathogen are introduced simultaneously at t=0 into a single 1km x 1km cell. Maps showing the density of infected citrus host units (E+C+I) within each cell at different times after introduction.</p> <p>Video 3: Spread of the pathogen in a single simulation in Region A (Valencia) using baseline parameters for detection and control. Corresponds to Fig 6 in main text. Maps show densities of infected citrus (E+C+I) in each 1km x 1km cell</p> <p>Video 4: Spread of the vector in a single simulation in Region B (Andalusia). Corresponds to Fig S13 in supplementary material. Maps show the measure of vector density within each cell and light grey shows initial exposure.</p> <p>Video 5: Spread of the pathogen in a single simulation in Region B (Andalusia). Corresponds to Fig S14 in supplementary material. Both vector and pathogen are introduced simultaneously at t=0 into a single 1km x 1km cell. Maps showing the density of infected citrus host units (E+C+I) within each cell at different times after introduction.</p> <p>Video 6: Spread of the pathogen in a single simulation in Region B (Andalusia) using baseline parameters for detection and control. Corresponds to Fig S17 in supplementary material. Maps show densities of infected citrus (E+C+I) in each 1km x 1km cell</p>
Plant and Disease Association in Forested Areas in Jhargram District of West Bengal, India
<p>Forests play the most important role among ecosystem service providers. Forest ecosystem services are considered fundamental to the daily life of human beings. Ecological life-support systems address the control of essential products and services such as human health, life, and livelihoods by forest ecosystem services. Many products including forest wood, honey, perfume, and medicinal plants are common timber and non-timber products of our daily life. Forest ecosystem services also play a vital role in maintaining the balance of nature, climate change, soil quality, nutrient cycle, etc. Based on empirical observations, it has been found that the people living in forested areas of the district have extensive knowledge of medicinal plants and their therapeutic potential. They also use these plant resources to treat sick people. Moreover, the traditional healers of the region use this knowledge in their day-to-day practices, which promotes ecological sustainability by protecting medicinal resources from commercial exploitation. Although some archives for medicinal knowledge of the tribal communities of the area have been established, there is a significant gap in exercising the practical importance of such traditional healthcare practices followed by the indigenous people.</p>
Knowledge base for rice plant disease diagnosis
<p>This dataset describes symptoms, disease, and their relationships in rice plant disease.</p>
FIGURE 8 in Cytospora species associated with canker disease of three anti-desertification plants in northwestern China
FIGURE 8. Pattern of overlapping Cytospora species among Elaeagnus angustifolia, Hippophae rhamnoides and Salix psammophila.
FIGURE 2 in Cytospora species associated with canker disease of three anti-desertification plants in northwestern China
FIGURE 2. Phylogram of ITS regions based on MP, ML and Bayesian analysis. Values above the branches indicate maximum parsimony bootstrap (MP BP ≥ 50 %) and maximum likelihood bootstrap (ML BP ≥ 50 %). Values below branches represent posterior probabilities (BI PP ≥ 0.90) from Bayesian inference. Scale bar = 20 nucleotide substitutions. The new sequences resulting from the current study are in blue. Ex-type strains are in bold. The strains designated as epitype are shown in asterisk.
FIGURE 5 in Cytospora species associated with canker disease of three anti-desertification plants in northwestern China
FIGURE 5. Morphology of Cytospora elaeagni from Elaeagnus angustifolia (BJFC-S642). a, d: Habit of conidiomata on a twig. b, c: Longitudinal sections through conidiomata. e, f: Transverse sections through conidiomata. g: Conidiophores. h: Conidia. i: Colonies on PDA at 3 days (left) and 30 days (right). Scale bars: a=1 mm; b–f=0.5 mm; g=20 μm; h=5 μm.
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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.