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22,710 results for “Plant”
Disentangling the effects of jasmonate and tissue loss on the sex allocation of an annual plant
<p>In this study, we explored norms of reaction in sex expression and sex allocation to herbivory in an experiment designed to uncouple its direct (through tissue loss) and indirect effects (due to defensive jasmonate signalling) in hermaphroditic XX females of the wind-pollinated Mercurialis annua. To uncouple the direct and indirect effects of herbivory on the sex expression and to test the role of jasmonate on conditional sex allocation, we conducted a two-factorial experiment manipulating tissue loss (25% chronic defoliation) and plant anti-herbivore defences via the jasmonate pathway (external application of jasmonate), and measured sexual expression in plants with both a male and a female function. The herbivory treatment applied were:</p> <p>For the control treatment (C), leaves were sprayed with a sham solution containing only water and polysorbate until all leaves were wet (see Supplementary Materials for detailed solution formulae). The herbivory treatment (H) consisted of cutting off half of every second leaf on the plant with scissors and spraying plants with a sham solution until all leaves were wet (defoliation resulted in a 25% reduction of total leaf area over the course of the whole plant’s lifetime). In the jasmonate treatment (JA) plants were sprayed with a solution of methyl-jasmonate and polysorbate until all leaves were wet (polysorbate 20 was used to fix the methyl-jasmonate on the sprayed leaves). Finally, the jasmonate and herbivory treatment (JAH) consisted of cutting off half of every other leaf on the plant with scissors and spraying plants with the methyl-jasmonate solution until all leaves were wet. These treatments were applied repeatedly as plants continued to grow, i.e., they represent chronic stress or manipulation. The first round of treatment was applied one week after repotting the plants (25th of November 2019) and then every two weeks over the next 12 weeks (the last treatment was applied on the 2<sup>nd</sup> of February 2020). On the first round of treatment, when most plants had fewer than six leaves each, we cut off only half a leaf (~10% of the leaf area removed) for plants under the herbivory treatments to avoid seedlings death.</p> <p>Plant sampling consisted of cutting all above-ground plant material of 34 plants per enclosure (<em>N</em> = 272) and recording total height. Plants were then cut in half, lengthwise, creating two distinct segments: top and bottom. The top segment was carefully examined and we counted the number of fruits (immature and mature) and harvested all male flowers using tweezers. Male flowers were stored in paper envelopes, dried and weighed. After phenotyping, plant segments were dried and weighed to obtain plant dry biomass (top + bottom). To estimate seed production, the seeds were isolated from the dried plant materials, stored in paper envelopes and weighed. All materials were dried in an oven at 50°C for at least 14 days and weighed using a digital scale.</p> <p>Variables names and meaning:</p> <p>PlantID: Individual identifier for each plant<br> nb_seeds_estimate.TOP: Number of seeds form the top section of the plant <br> Biomass.BOTTOM: Dry biomass of the bottom plant section (grams) <br> Total_biomass: Dry biomass of the whole aboveground plant materials, except for the male flowers <br> Biomass.TOP: Dry biomass of the bottom plant section (grams) <br> seed_mass_total: Dry biomass of the seeds of the whole plant (top+bottom sections) (grams)<br> seed_mass.BOTTOM: Dry biomass of the seeds from the bottom section (grams)<br> seed_nb_total: Number of seeds from the whole plant (top+bottom sections) <br> Lenght_section.TOP: Length of the top section (cm) <br> Fruit_number.TOP: Number of fruits present on the top sectioon at the time of harvest<br> nb_seeds_estimate.BOTTOM: Number of seeds from the bottom section<br> Height: Plant height (top+bottom sections) (cm) at the time of harvest<br> Fruit_number.BOTTOM: Number of fruits present on the bottom section at the time of harvest <br> DPT: Days-post-treatment = the period elapsed between the last treatment application and the plant sampling date. For logistical reasons, our sampling was spread over 14 days by a team of six assistants.<br> Lenght_section.BOTTOM: Length of the bottom section (cm)<br> Treatment: Herbivory treatments: C=Control; H= 25% chronic tissue loss, JA=exogenous jasmonate application; JAH=tissue loss + jasmonate.<br> Box: Enclosure in which plants were kept. This was a blocking factor with 2 boxes per treatment, each one with 30-32 plants. <br> Date: sampling date <br> seed_mass.TOP: Dry biomass of the seeds on the bottom plant sections (grams)<br> Observer: Identifier for each of the six researchers who sampled plants. We recorder observer identity and included it in our statistical analyses to account for possible biases among assistants.<br> male_fl_mass.TOP: Dry biomass of the male flowers sampled from the top plant section (grams). <br> nb_fl_estimate.TOP: Number of male flowers present on the top plant section at the time of harvest <br> male_fl_mass.BOTTOM: Dry biomass of the male flowers sampled from the bottom plant section (grams). <br> nb_fl_estimate.BOTTOM: Number of male flowers present on the bottom plant section at the time of harvest </p> <p> </p>
Activity of antioxidant enzymes and lipid peroxidation of soybean plants treated with five Diaporthe species
<p>Absorbance data from spectrophotometric measurements of catalase, reduced glutathion, lipid peroxidation and superoxide-dismutase of soybean cv. Sava plants infected with five <em>Diaporthe</em> species (i.e. <em>D. aspalathi</em>, <em>D. caulivora</em>, <em>D. eres</em>, <em>D. gulyae</em>, <em>D. longicolla</em>).</p> <p>Supplementary data to the publication Petrovic et al. (2023) The biochemical response of soybean cultivars infected by <em>Diaporthe</em> species complex. Plants 12, 2896. https://doi.org/10.3390/plants12162896</p>
Datasets and Pipeline V1.0 from An Atlas of Plant Transposable Elements
<p>In this repository, we deposited support data for the article "An Atlas of Plant Transposable Elements", available at <a href="http://apte.cp.utfpr.edu.br/">http://apte.cp.utfpr.edu.br/</a>.</p> <p>Here, we included:</p> <p><strong>1.) Supplementary material data:</strong><br> A) SuppMat_1.xlsx: The genome assembly reference access from Ensembl Plants species used.<br> B) SuppMat_2.docx: A brief transposable elements annotation steps are used in this work.</p> <p><strong>2.) Code and software: </strong>all script code create, third-party software, how we are used it, are detailed using Arabidopsis thaliana genome as an example in the GitHub: <a href="https://github.com/alerpaschoal/apte_pipeline">https://github.com/alerpaschoal/apte_pipeline</a> under the MIT license (please see details in licence.txt file). For the third part-software, consult their terms.</p> <p>To report bugs, to ask for help, and to give any feedback, please contact Alexandre R. Paschoal (paschoal@utfpr.edu.br) or Douglas S. Domingues (douglas.domingues@unesp.br).</p>
Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model
<p>This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.</p>
Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system
<p>This dataset contains the raw and processed images from a low-cost high-throughput plant phenotyping (HTP) system, as well as the raw and processed images that were manually acquired for comparison. The HTP images were automatically and wirelessly acquired for entire benches of plants with a system composed of a Raspberry Pi and eight GoPro cameras. The entire file system of each GoPro camera was copied directly into a subfolder of finalGoProImages (numbered by camera). The raw HTP images were processed by correcting for lens distortion, computing the "greenness index" for each individual pixel, and filtering out extreme high and low values. These processed HTP images were then saved in the "greenness" subfolder of finalGoProImages. The manually acquired images in the finalDSLR folder each represent an individual plant from one of five time points during the same greenhouse experiment. The raw manually acquired images were processed in the same manner as the raw HTP images by computing the greenness index for each individual pixel and filtering out extreme high and low values. The two tab-delimited text files include the number of green pixels and mean greenness index for each HTP (greennessGoProTable2.txt) and manually acquired (greennessDSLRTable2.txt) image.</p>
Data from: The legacy of the extinct Neotropical megafauna on plants and biomes
<p>The main dataset consists of ecoregion-level data on five plant functional traits (wood density, leaf size, stem spines, leaf spines and latex production), as well as ecoregion-level data on extinct megafauna historical patterns, fire, climate, soil, hurricanes and geografical variables (first spreadsheet) for the Neotropical biogeographic realm (Table 1). It also includes species-level plant functional trait data, and the abundance (presence-absence for leaf size) of these species, and the occurrences extinct megafauna and extant mammal herbivore species per Neotropical ecoregion, as well as diet data compiled for megafauna species. The species-level functional trait data was compiled from the literature and the names of the species in these data was used to search for occurrence data for these species in the Global Biodiversity Information Facility (Data available from GBIF using the following doi: WD: 10.15468/dl.3vua3x; Stem spines: 10.15468/dl.ar5ddj; Latex: 10.15468/dl.m8dzjd; Leaf spines: 10.15468/dl.vv8gw4; Leaf size: 10.15468/dl.k98nxc). During the process, species level were corrected and updated using tools from the "rgbif" package for R. We then croped only the Neotropical region, and calculate ecoregion level trait means for continuous traits (Wood Density and Leaf Size) and maximum por binary traits (Stem and Leaf Spines, Latex), using the ecoregion shapefile provided in https://storage.googleapis.com/teow2016/Ecoregions2017.zip. We obtained data on historical distribution of megafauna species and extant mammal species from the MegaPast2Future/PHYLACINE_1.2 dataset, and obtained diet information from literature sources. Climate data was obtained from WorldClim 2.1 (10 minute spatial resolution) and was based on climate data from 1970 and 2000. Soil data were obtained from SoilGrids (5 km of spatial resolution), and consisted of mean values for two depths, 0.05 and 2 m. We obtained the number (a proxy for frequency) and intensity of wildfires per ecoregion area using the MODIS active fire location product (MCD14ML). We only considered fires with detection confidence of 95% or higher occurring from November 2000 to December 2019 (both included). To ensure that only wildfires were considered, we associated each fire pixel with a land cover type (300 m of spatial resolution) from for a buffer area of 1000 m surrounding the fire pixel centroid. We excluded all of the fires occurring in areas in which more than 10% of the surrounding land cover pixels corresponded to agricultural, urban and water classes. We calculated the number of wildfires per ecoregion area by dividing the fire count of each Ecoregion by the ecoregion area, and multiplying the resulting value by the proportion of vegetated land cover pixels (same classes used to exclude fires in anthropogenic areas and water bodies above). Fire intensity was estimated as the average fire radiative power across all detected wildfires in the ecoregion. We also classified ecoregions into insular (1), when most of the ecoregion area was located in islands, vs. continental (0), otherwise. We also compiled data on hurricane activity, as woody density was suggested to confer resistance against this disturbance. We used data from 1990 to 2019 from the HURDAT2 dataset, containing six-hourly information about the location of all of the known tropical and subtropical cyclones (0.1° latitude/longitude). We used the sum of hurricane occurrences per ecoregions divided by ecoregion area as an indicator of hurricane activity.</p> <p>Three .txt files containing the custom codes developed for building the Ecoregion-level dataset (predictors and traits) and for data analyses used in the article are also included.</p>
Needs of plant health laboratories and applicability of the horizontal proficiency testing approach based on the questionnaire answers
<p>Data collected in the framework of work package 5 of the Valitest project. They correspond to the needs and expectation concerning proficiency assessment expressed by plant health laboratories during a survey conducted online in 2019.</p>
Nicotiana benthamiana as a model organism for plant biology study
<p><em>Nicotiana benthamiana</em> is an amenable model organism for plant biology study. Several functional genomics tools, including viral vectors, RNAi, ethylmethanesulfonate (EMS) mutagenesis, CRISPR-mediated genome editing, and agroinfiltration, are available in the <em>N. benthamiana</em> experimental system. These tools can be applied to research in genomics, biochemistry, metabolomics, cell biology and pathology as well as other topics in plant biology.</p> <p>*This is an updated graphical abstract for commnetary article "Dude, where is my mutant? <em>Nicotiana benthamiana</em> meets forward genetics" (Derevnina et al., 2019, New Phytologist 221(2):607-610).</p>
VirHunter: a deep learning-based method for detection of novel RNA viruses in plant sequencing data
<p>This storage contains 2 archives: toy datasets to test the training of the VirHunter and weights of the fully trained VirHunter models for 3 host species (peach, grapevine, sugar beet) and for fragment sizes 500 and 1000. .</p> <p>The toy dataset consists of 3 archived files: 'viruses.fasta', 'host.fasta', 'bacteria.fasta'.</p> <p>'viruses.fasta' contains 10000 randomly selected plant viruses from the virus dataset described in the paper.</p> <p>'host.fasta' consists of peach chromosome 2.</p> <p>'bacteria.fasta' consists of 10 bacterial genomes selected randomly: GCF_000284415, GCF_000590555, GCF_001548055, GCF_002795265, GCF_003330825, GCF_003957805, GCF_005845345, GCF_009176625, GCF_010748935, GCF_014681765</p> <p> </p>
The role of plants and fibres in modelling monumental terracruda sculptures of the Silk Roads - Dataset
<p>Phytoliths and fibre datasets associated with the publication:</p> <p>Title: The role of plants and fibres in modelling monumental <em>terracruda</em> sculptures of the Silk Roads: archaeobotanical analyses from the Buddhists sites of Tepe-Narenj and Qol-e-tut (Kabul, Afghanistan)</p> <p>Authors: Mònica López-Prat, Carla Lancelotti, Gema Campo-Francés, Sudipa Ray Bandyopadhyay, Begoña Carrascosa, Noor Agha Noori, Alessandra Pecci, José Simón-Cortés & Domenico Miriello</p> <p>Journal: Heritage Science</p>
Phenology and production of pollen, nectar and sugar in 1612 plant species from various environments
<p>This dataset is related to the data paper published in Ecology: Filipiak et al, in press, <em>Phenology and production of pollen, nectar and sugar in 1612 plant species from various environments</em>; doi: to be provided when available. This paper must be always properly cited when using the database. Please use any scientific full citation format. <br> <br> <strong>Abstract</strong><br> To predict the quantity and quality of the food available for pollinators in various landscapes over time, it is necessary to collect detailed data on pollen, nectar, and sugar production per unit area and the flowering phenology of plants. Similar data are needed to estimate the contribution of plants to the functioning of food webs via the flow of energy and nutrients through the soil-plant-nectar/pollen-consumer pathway. Current knowledge on this topic is fragmented. This is the first database to compile data on the various food resources produced by 1612 different plant species, belonging to 755 genera and 133 families, including crops and wild plants, annuals and perennials, animal- and wind-pollinated plants, and weeds and trees growing in different ecosystems under various environmental conditions. The dataset consists of 103 parameters related to the traits of plant species, as well as to geographical and environmental factors, allowing for precise calculations of nectar, pollen and energy provided by plants and available to consumers in the considered flora or ecosystem on a daily basis throughout the year. These parameters, gathered by us and extracted from the available literature, describe pollen, nectar and sugar production (where applicable, in mass, volume and concentration units), honey yield, the timing and duration of flowering, flower longevity, numbers of plants and flowers per unit area, related weather conditions (temperature and precipitation), geographical location, landscape, and syntaxonomy. The data were obtained from various, mostly European, pedoclimatic zones, and the majority of the data were available for plant species and communities present in Central Europe, especially in Poland, where research on floral resources has a long tradition. These data are representative of the whole continent and may be used as a reference for plant communities occurring on continents other than Europe since the database allows the consideration of differences in the production of resources by a single plant species growing in different communities. This dataset provides a unique opportunity to test hypotheses related to the functioning of food webs, nutrient cycling, plant ecology, and pollinator ecology and conservation.</p>
Scanning the horizon for invasive plant threats using a data-driven approach
<p>This repository holds the data and code for the manuscript "Scanning the horizon for invasive plant threats using a data-driven approach". </p> <p><strong>Contents</strong></p> <ul> <li>code: descriptions below</li> <li>data: descriptions below</li> <li>intermediate-data: datasets produced by processing original data (see code) or produced through horizon scan process (descriptions below)</li> <li>fl-plants-horizon-scan.Rproj: RStudio project for running R scripts</li> </ul> <p> </p> <table> <thead> <tr> <th scope="col">code</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>gcw_processing.R</td> <td>R script to format data downloaded from the Global Compendium of Weeds</td> </tr> <tr> <td>native_introduced_ranges.R</td> <td>R script to create map of native and introduced ranges of taxa on the final list</td> </tr> <tr> <td>random_draws_plant_families.R</td> <td>R script to evaluate over- and underrepresentation of plant families in initial and final list</td> </tr> <tr> <td>review_process_comparison.R</td> <td>R script to evaluate differences in scores before and after peer-review and consensus-building</td> </tr> <tr> <td>scores_certainty_pathways.R</td> <td>R script to create figures of scores, certainty, and pathways for final list</td> </tr> <tr> <td>risk_scores_analys.R</td> <td>R script to evaluate final risk scores</td> </tr> <tr> <td>pathways_process.R</td> <td>R script to process pathways to introduction data</td> </tr> <tr> <td>taxa_list_processing.R</td> <td>R script to create list used for rapid risk assessments from an initial list</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th scope="col">data</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>cab_list_full.csv</td> <td>list of potential invasive species to Florida generated by CABI Horizon Scan Tool on November 15, 2019</td> </tr> <tr> <td>GCW_full_list_020420.csv</td> <td>Global Compendium of Weeds downloaded on February 4, 2020</td> </tr> <tr> <td>PlantAtlasDataExport-20191211-194219.csv</td> <td>Atlas of Florida plants downloaded December 11, 2019</td> </tr> <tr> <td>Taxon_x_List_GloNAF_vanKleunenetal2018Ecology_121119.csv</td> <td>GloNAF 1.2 database downloaded December 11, 2019</td> </tr> <tr> <td>the-plant-list</td> <td>The Plant List Database downloaded August 3, 2021</td> </tr> </tbody> </table> <p> </p> <table> <thead> <tr> <th scope="col">intermediate-data</th> <th scope="col">description</th> </tr> </thead> <tbody> <tr> <td>federal_noxious_weed_list.csv</td> <td>manually formatted version of the USDA Federal Noxious Weed List downloaded March 16, 2020</td> </tr> <tr> <td>first_round_assessments_050120.csv</td> <td>rapid risk assessments for horizon scan pre-peer-review</td> </tr> <tr> <td>fl_prohibited_plants.csv</td> <td>manually compiled list of prohibited plants in Florida based on the Florida Noxious Weed List, Florida Prohibited Plants list, and Florida Invasive Species Council (all downloaded March 9, 2020)</td> </tr> <tr> <td>horizon_scan_plants_full_reviews_080321.csv</td> <td>rapid risk assessments for horizon scan post-peer-review and consensus-building</td> </tr> </tbody> </table> <p> </p>
Scanned images of monocultures and mixtures of six grassland plant species roots, and of simulated fine roots
<p>Soil core samples were taken from a multi-species grassland experiment with field plots of monocultures and mixtures of six grassland plant species: <em>Lolium perenne</em> L. (PRG),<em> Phleum pratense</em> L. (TIM), <em>Trifolium pratense</em> L. (RC), <em>Trifolium repens</em> L. (WC), <em>Cichorium intybus </em>L. (CHIC), and <em>Plantago lanceolata </em>L.. The multi-species plots had a two species mixture with <em>Trifolium repens </em>L. and<em> Lolium perenne</em> L. (PRGWC), and a 6 species mixture with all species mentioned above. The cores were separated into soil depths of 0-10 cm, 10-15 cm and 15-20 cm and the roots separated from the soil.</p> <p>A ground-truth image set was created to simulate fine roots using fishing line. The fishing line used was a clear copolymer monofilament (Greys<sup>TM</sup> Greylon Tippet Material 3 lb), measured using a scanning electron microscope (Hitachi SU8200) to be 0.14 mm in diameter. The fishing line was used in its clear colour or coloured black using a permanent marker to simulate unstained and stained fine roots respectively. The fishing line was cut into lengths of 30 cm or 5 cm. </p> <p>Roots and fishing line were scanned using an Epson Perfection V800 flatbed scanner at 600 dpi. </p> <p>The Roots ZIP file contains a folder for the scanned root images and the Line zip file contains a folder with the scanned fishing line. The excel spreadsheet describes the naming convention for the images.</p> <p>Further details about the root sampling and image acquisition can be found in the publication that analyses these images: <a href="https://doi.org/10.1002/ppj2.20034">https://doi.org/10.1002/ppj2.20034</a></p>
Analysis scripts for the evaluation of a low-cost high-throughput plant phenotyping system
<p>Data analyses to complement "Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system" (DOI: 10.5281/zenodo.5725224). "README_SetupAndAnalyses.pdf" contains instructions for setting up the high-throughput phenotyping (HTP) system and analyzing the resulting image datasets. The analyses are split into two parts. First, the automatically acquired HTP and manually acquired (DSLR) images are processed using the Python script labeled "finalGreennessAnalyses.py". The csv file labeled "labelTable.csv" is used to rename the DSLR images in terms of the date acquired and experimental conditions and must be included for the Python script to process the DSLR images. The output of the Python script includes "greennessGoProTable.txt" containing tab-delimited data regarding foliar size and greenness for each HTP image and "greennessDSLRTable.txt" containing tab-delimited data regarding foliar size and greenness for each DSLR image. The second step of the analyses includes inferential statistics (e.g., correlations and linear mixed effects modeling) and is based on the R script labeled "ghGoProAndDSLR_toPublish2.R". The csv file labeled "parAllBenches.csv" includes average solar daily light integral (solar DLI) data that were used as part of the linear mixed effects models in R.</p>
Drone orthomosaics for 'Livestock impacts on an iconic Namib Desert plant are mediated by abiotic conditions'
<p>Drone orthomosaics used in the analyses and figures of: Livestock impacts on an iconic Namib Desert plant are mediated by abiotic conditions, accepted in Oecologia.</p> <p>These drone orthomosaics are a data supplement to the code and data repository here: https://github.com/jtkerb/Nara_Paper_Repo</p>
1-km forest tree height, cover, plant area index, and foliage height diversity for the CONUS
<p>Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at 1-km resolution for 2013-2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses.</p>
Evaluation of surface properties that influence the self-cleaning action of hydrophobic plant leaves
<p>It is well established that many leaf surfaces display self-cleaning properties. However, an understanding of how the surface properties interact is still not achieved. Twelve different leaf types were selected for analysis due to their water repellency and self-cleaning properties.</p>
A database of plant-pollinator networks
<p>This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases.</p> <p>Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.</p>
Root symbionts alter the volatile profile of herbivore-infested tomato plants and aid the attraction of a predator
<p>Beneficial root microbes are among the most frequently used biocontrol agents in cropping systems, since they have been shown to promote plant growth and crop yield. Moreover, they are able to enhance protection against pathogens and insect herbivores by activating plant resistance mechanisms. Plant defense responses against herbivorous insects include the induction of metabolic pathways involved in the synthesis of defense-related metabolites. These metabolites include volatile organic compounds (VOCs), which attract natural enemies of the herbivores as a form of indirect resistance. Considering that beneficial root microbes may affect direct herbivore resistance, we hypothesized that also indirect resistance may be affected. We tested this hypothesis in a study system composed of tomato, the arbuscular mycorrhizal fungus <em>Rhizophagus irregularis</em>, the growth-promoting fungus <em>Trichoderma harzianum</em>, the generalist chewing herbivore <em>Spodoptera exigua </em>and the omnivorous predator<em> Macrolophus pygmaeus</em>. Using a Y-tube olfactometer we found that <em>M. pygmaeus</em> preferred plants with <em>S. exigua </em>herbivory, but microbe-inoculated plants more than non-inoculated ones. We used a targeted GC-MS approach to assess the impact of beneficial microbes on the emission of volatiles twenty-four hours after herbivory to explain the choice of <em>M. pygmaeus</em>. We observed that the volatile composition of the herbivore-infested plants differed from that of the non-infested plants, which was driven by the higher emission of green leaf volatile compounds, methyl salicylate, and several monoterpenes and sesquiterpenes. Inoculation with microbes had only a marginal effect on the emission of some terpenoids in our experiment. Gene expression analysis showed that the marker genes involved in the jasmonic and salicylic acid pathways were differentially expressed in the microbe-inoculated plants after herbivory. Our results pinpoint the role of root symbionts in determining plant-microbe-insect interactions up to the third trophic level, and elucidates their potential to be used in plant protection.</p>
Alignment used in "A phylogenomically informed five-order system for the closest relatives of land plants"
<p>Alignment that served as the basis for the phylogenomic analyses presented in "A phylogenomically informed five-order system for the closest relatives of land plants" — preprint on bioRxiv doi: https://doi.org/10.1101/2022.07.06.499032</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.