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264 results for “grapevine”
Increases in vein length compensate for leaf area lost to lobing in grapevine
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Phenotypic data from: from buds to shoots: insights into grapevine development from the Witch’s Broom bud sport
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Data from: Latent developmental and evolutionary shapes embedded within the grapevine leaf
<ul> <li>Across plants, leaves exhibit profound diversity in shape. As a single leaf expands, its shape is in constant flux. Plants may also produce leaves with different shapes at successive nodes. In addition, leaf shape varies among individuals, populations and species as a result of evolutionary processes and environmental influences.</li> <li>Because leaf shape can vary in many different ways, theoretically, the effects of distinct developmental and evolutionary processes are separable, even within the shape of a single leaf. Here, we measured the shapes of > 3200 leaves representing > 270 vines from wild relatives of domesticated grape (<i>Vitis </i>spp.) to determine whether leaf shapes attributable to genetics and development are separable from each other.</li> <li>We isolated latent shapes (multivariate signatures that vary independently from each other) embedded within the overall shape of leaves. These latent shapes can predict developmental stages independent from species identity and vice versa. Shapes predictive of development were then used to stage leaves from 1200 varieties of domesticated grape (<i>Vitis vinifera </i>), revealing that changes in timing underlie leaf shape diversity.</li> <li>Our results indicate that distinct latent shapes combine to produce a composite morphology in leaves, and that developmental and evolutionary contributions to shape vary independently from each other.</li> </ul>
Seed Morphology in Key Spanish Grapevine Cultivars
<p>Photographs were taken with a camera Nikon D80 of 10,2 megapixels. The seeds were oriented with their chalaza downwards, such as to expose the ribs upwards (ventral orientation) and straight in the middle to provide each seed image a maximum of symmetry. Composed images containing 30 seeds per accession were prepared with Corel Photo Paint</p>
Data from: Demographic and ecogeographic factors limit wild grapevine spread at the southern edge of its distribution range - wild grapevine sampling locations, Maxent input files, morphological and microsatellite data
<p><span>This dataset contains raw data described in the paper: "Rahimi O., Ohana-Levi N., Brauner H., Inbar N., Hübner S. and Drori E. (2021) "Demographic and ecogeographic factors limit wild grapevine spread at the southern edge of its distribution range", accepted for publication in "Ecology and Evolution".</span></p> <p><span>The spatial distribution of plants is constrained by demographic and eco-geographic factors that determine the range and abundance of the species. In this study, we performed genetic and morphological analyzes based on SSR and OIV datasets. In addition, according to the spatial distribution model performed by Maxent software we found that distance to water sources, Normalized difference vegetation index, and precipitation are the main environmental factors constraining <i>V.v. sylvestris</i> distribution at its southern distribution range. All raw data used for this study can be found in this deposit which contains a table with grapevine locations, Maxent input files, morphological and microsatellite data. </span></p>
2-class Grapevine Pest Dataset of Scaphoideus titanus and Orientus ishidae on yellow Sticky traps for Insect Detection
<p>This dataset consists of 615 images of <em>Scaphoideus titanus</em> (ST) and <em>Orientus ishidae</em> (OI) from yellow sticky traps (YST). Among these, 150 photos, which lack target insects, have been repurposed as background images. Insect annotations comprise 1329 ST and 1506 for OI, ensuring an almost class-balanced dataset. The images were acquired through four distinct methods:</p> <ul> <li>Photos from the field;</li> <li>Images of stored YST (T = 5±1°C) and reared insects within a controlled greenhouse environment;</li> <li>Digital scans of YST collected during regular monitoring activities in the fields;</li> <li>Photos from a smart trap prototype installed in our experimental vineyard.</li> </ul> Structure of the dataset, showing the number of images from each data source and the corresponding class annotations. <table><tbody> <tr> <td>Image source</td> <td>Number of images</td> <td>ST annotations</td> <td>OI annotations</td> <td>Number of background images</td> </tr> <tr> <td> <p>Field</p> </td> <td>18</td> <td>3</td> <td>101</td> <td>8</td> </tr> <tr> <td>Laboratory</td> <td>157</td> <td>473</td> <td>863</td> <td>8</td> </tr> <tr> <td>scanned</td> <td>390</td> <td>853</td> <td>542</td> <td>84</td> </tr> <tr> <td>smart-trap</td> <td>50</td> <td>0</td> <td>0</td> <td>50</td> </tr> </tbody> </table> <p>We provide the yellow sticky trap images already cropped in the pre-processing stage, the corresponding enhanced datasets focusing on <em>brightness & contrast</em>, <em>sharpness</em>, and a combination of both. Finally the annotations exported in YOLO format.</p> <h3>Dataset structure</h3> <ol> <li><em>crop/</em></li> <li><em>bright/</em></li> <li><em>sharp/</em></li> <li><em>bright_and_sharp/</em> </li> <li><em>labels/</em></li> </ol> <p>At the time of publication, this dataset is the largest publicly available resource in the control of FD vectors. Detailed documentation, along with model benchmarking and performance results is given in an accompanying journal paper: (paper under submission).</p> <h3>Deployment</h3> <p>You can use this dataset as starting point to train your own insect detection models. Open source Python scripts to deploy the trained models can be found in our <a href="https://github.com/checolag/insect-detection-scripts/tree/main">Github</a> repository.</p>
Grapevine flavescence dorée ̶ Pest Report and Datasheet to support ranking of EU candidate priority pests
<p>These two files are part of the outputs produced under the mandate <a href="http://registerofquestions.efsa.europa.eu/roqFrontend/wicket/page?1-1.ILinkListener-contentPane-listContainer-pageable-21-mandateNumberLnk">M-2017-0056</a> of the European Commission requesting EFSA for technical assistance in the field of quarantine pests qualifying as priority pests as by Article 6(2) of the Regulation (EU) 2016/2031 <em>on protective measures against pests of plants</em>.</p> <p>Under the mandate EFSA produced: i) 1 methodology report (DOI available at the field "Related/alternate identifiers"), ii) 28 datasheets, one for each of the 28 candidate pests, and iii) 28 pest reports supporting the information provided in the datasheets.</p> <p>EFSA wishes to acknowledge the contribution of Xavier Foissac and Cristina Marzachí to the EKE and the review conducted by Jean Claude Gregoire.</p>
Supplementary tables S1-S5. Comparison of two different host plant genera responding to grapevine leafroll-associated virus 3 infection
<p>Grapevine leafroll-associated virus 3 (GLRaV-3) is one of the most important viruses of grapevine but, despite this, there remain several gaps in our understanding of its biology. Because of its narrow host range -limited to <em>Vitis</em> species - and because the virus is restricted to the phloem, most GLRaV-3 research has concentrated on epidemiology and the development of detection assays. The recent discovery that GLRaV-3 can infect <em>Nicotiana</em> <em>benthamiana, </em>a plant model organism, makes new opportunities available for research in this field. We used RNA-seq to compare both <em>V. vinifera </em>and <em>N. benthamiana</em> host responses to GLRaV-3 infection. This is the first analysis of gene expression profiles beyond <em>Vitis </em>to mealybug-transmitted GLRaV-3.</p>
Testing of different methods to induce lime stress responses in grapevine rootstocks - supplementary figures
<p>Defined experiments are necessary to clarify the response to nutrient deficiency. The study tested different options to induce lime stress in pot experiments to select the most appropriate method with grapevine roostocks.We tested soil substrates, an hydroponic culture and an inert sand substrate by adding KHCO3. Grapevine rootstocks Teleki 5C, Couderc 3309 and Fercal were used. The figures represent supplementary material of the obtained phenotype with the different rootstocks and testing conditions.</p>
Trinity assembly of Powdery mildew of Grapevine lesion samples from Italy PMG
<p>De novo trinity assembly powdery mildew of grapevine from NGS data (RNAseq)</p>
Trinity assembly of Downy mildew of Grapevine lesion samples DMG-G
<p>De Novo trinity assembly RNAseq NGS-Data</p>
Trinity assembly of Downy mildew of Grapevine lesion samples DMG-A
<p>Tirnity Assembly of NGS reads </p>
FIGURE 1 in Phylogenetic Relationship Among Wild and Cultivated Grapevine in Sicily: A Hotspot in the Middle of the Mediterranean Basin
FIGURE 1 | Map of Sicily indicating the collection sites of wild grapevine.
A tale of three vines: current and future threats to wild Eurasian grapevine by vineyards and invasive rootstocks
<p>This dataset has been used for the paper <em>A tale of three vines: current and future threats to wild Eurasian grapevine by vineyards and invasive rootstocks</em> by Petitpierre et al.</p> <p>It contains the species distribution of five <em>Vitis</em> species in North America and Europe (<em>Vitis acerifolia</em>, <em>Vitis aestivalis</em>, <em>Vitis rupestris</em>, <em>Vitis riparia </em>and <em>Vitis berlandieri</em>), so as the distribution of the Eurasian wild grapevine (<em>Vitis vinifera</em> ssp. <em>sylvestris</em>) and cultivated grapevine in Europe (<em>Vitis vinifera</em> ssp. <em>vinifera</em>). 40 bioclimatic variables are associated to these distributions. These variables were derived from the Climond dataset (Kriticos et al., 2012).</p> <p>Species distribution data were gathered using several databases and sources. The list of the different sources is cited in methods section of the related manuscript. The distribution of each <em>Vitis</em> taxa was rasterized at a resolution of 0.167° (coordinate reference system WGS84; EPSG:4326). X and Y coordinates correspond to the center of each cell. Environmental variables were extracted from the CliMond database (Kriticos et al., 2012) for each site. It consists of a set of 40 bioclimatic variables at a resolution of 0.167°, grouped into 4 categories: temperature, precipitation, moisture, and solar radiation. The description of these 40 variables can be found in the original CliMond publication (Kriticos et al., 2012).</p> <p> </p> <p>Each file contains the distribution of one Vitis taxa.</p> <p>v_ace.csv contains the distribution of Vitis acerifolia in North America.</p> <p>v_aes.csv contains the distribution of Vitis aestivalis in North America.</p> <p>v_ber.csv contains the distribution of Vitis berlandieri in North America.</p> <p>v_rip.csv contains the distribution of Vitis riparia in North America.</p> <p>v_rup.csv contains the distribution of Vitis rupestris in North America.</p> <p>v_root.csv contains the distribution of the American vitis taxa (Vitis acerifolia, Vitis aestivalis, Vitis berlandieri, Vitis riparia and Vitis rupestris) in Europe.</p> <p>v_vin.csv contains the distribution of the vineyards (i.e. Vitis vinifera ssp. vinifera, the cultivated grapevine) in Europe.</p> <p>v_syl.csv contains the distribution of the vineyards (i.e. Vitis vinifera ssp. sylvestris, the wild European grapevine) in Europe.</p> <p><strong>Reference</strong><br> Kriticos, D.J., Webber, B.L., Leriche, A., Ota, N., Macadam, I., Bathols, J., Scott, J.K., 2012. CliMond: Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 3, 53–64. https://doi.org/10.1111/j.2041-210X.2011.00134.x</p> <p>GBIF data for Vitis vinifera ssp. vinifera. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.hacmjx<br> GBIF data for Vitis ssp. sylvestris. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.qu7txv<br> GBIF data for Vitis berlandieri. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.w6dt7r<br> GBIF data for Vitis riparia. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.aqh55u<br> GBIF data for Vitis acerifolia. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.csd7wc<br> GBIF data for Vitis aestivalis. GBIF.org (11 November 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.f5jfzf<br> GBIF data for Vitis rupestris. GBIF.org (02 September 2020) GBIF Occurrence Download https://doi.org/10.15468/dl.gqjm7w</p> <p> </p>
Grapevine cv. tempranillo grafted onto 110R and SO4 rootstocks —gas exchange parameters
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Data from: Demographic and ecogeographic factors limit wild grapevine spread at the southern edge of its distribution range - wild grapevine sampling locations, Maxent input files, morphological and microsatellite data
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Cold hardiness, deacclimation, and budbreak phenology in grapevine
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Genomic structure and ex situ conservation of the North American grapevine <em>Vitis labrusca</em>
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Pierce's disease vector transmission-preference experiment on PdR1 resistant grapevines
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Data from: Latent developmental and evolutionary shapes embedded within the grapevine leaf
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International Brain Laboratory public data
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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.