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3,682 results for “ROOTS”
Figure 4 in Analysis of microbial diversity in the root of Astragalus mongholicus
Figure 4. Species richness at the class level.
Fig. 6 in The origin and diversification of the Entorrhizales: deep evolutionary roots but recent speciation with a phylogenetic and phenotypic split between associates of the Cyperaceae and Juncaceae
Fig. 6 Spores of Entorrhiza fuirenae (holotype) seen by light microscopy. Scale bar = 10 μm
Evaluation of orthodontically induced external root resorption following orthodontic treatment using Cone Beam Computed Tomography (CBCT): a systematic review and meta-analysis
<p>Datasets for all analyses performed in the paper.</p>
CDS of Urochloa fusca transcriptome - whole seedling (except roots)
<p>The CDS data was obtained from transcriptome of above whole seedling (except roots) of 12 days old <em>Urochloa fusca</em> seedlings.</p>
Convergent evolution of NFP-facilitated root nodule symbiosis
<p># NFP</p> <p>Scripts and data associated with the manuscript "Convergent evolution of NFP-facilitated root nodule symbiosis."</p> <p>## 00_sequences<br>This directory contains NFP/LYR homologs and annotations.</p> <p>## 02_alignments<br>This directory contains NFP alignments</p> <p>## 03_trees<br>This directory contains gene trees and species tree guides for the analyses.</p> <p>## 04_reconciliation<br>This directory contains notung files and figures for the gene tree reconciliation analysis</p> <p>## 05_synteny<br>This directory contains files for the synteny analyses. The base directory contains circos plots and a subdirectory `Synteny.2.2020` containing scripts and summary output as well as the following:<br>### Results_Apr01.zip<br>Summary of synteny results<br>### csvs.zip<br>An archive containing all csvs in the analysis.<br>### fastas.zip<br>synteny analysis - chr 5 v 8.zip<br>### tsvs.zip<br>An archive containing all csvs in the analysis.<br>### blasts.zip<br>An archive containing blast results.<br>### coords.zip<br>An archive containing genome coordinate files.<br>### gffs.zip<br>An archive containing GFF annotations.<br>### gff3s.zip<br>An archive containing GFF annotations.<br>### imgs.zip<br>An archive containing figure files used for preparing the manuscript.<br> <br>## figs<br>This directory contains figure files used for preparing the manuscript.</p> <p>## Scripts_used_CF<br>This directory contains submission and shell scripts used to conduct the analyses.</p>
Salt marsh litter quality and decomposition under sea-level rise scenarios: from leaves to fine absorptive roots
Open the record for dataset details and reuse information.
Miniscrew-to-Tooth Root Distance and PTV Data
<p>This dataset was collected as part of an experimental study aimed at diagnosing miniscrew–root contact by assessing tooth mobility before and after miniscrew implantation. Using cadaveric pig mandibles, miniscrews were placed near the premolars, and tooth mobility was measured before and after implantation using the Periotest system. The Periotest Value difference (PTV-Diff) was calculated and correlated with the distance between the miniscrews and the tooth roots, measured via computed tomography (CT) images. The study investigates the efficacy of PTV-Diff in diagnosing root contact compared to maximum insertion torque (MIT)</p>
Generalised Root Segmentation Example Using 3D-Unet
Open the record for dataset details and reuse information.
Data and R code used in Hennecke et al. "Plant species richness and the root economics space drive soil fungal communities"
<p>To investigate how plant diversity and root traits relate to soil fungal communities, in 2021 we collected trait data from plots in the Jena Experiment (https://the-jena-experiment.de; funded by the DFG FOR 5000) and characterized fungal communities by sequencing, respiration and lipid fatty acid quantification. </p>
Data from: Host genotype and age shape the leaf and root microbiomes of a wild perennial plant
Bacteria living on and in leaves and roots influence many aspects of plant health, so the extent of a plant's genetic control over its microbiota is of great interest to crop breeders and evolutionary biologists. Laboratory-based studies, because they poorly simulate true environmental heterogeneity, may misestimate or totally miss the influence of certain host genes on the microbiome. Here we report a large-scale field experiment to disentangle the effects of genotype, environment, age and year of harvest on bacterial communities associated with leaves and roots of Boechera stricta (Brassicaceae), a perennial wild mustard. Host genetic control of the microbiome is evident in leaves but not roots, and varies substantially among sites. Microbiome composition also shifts as plants age. Furthermore, a large proportion of leaf bacterial groups are shared with roots, suggesting inoculation from soil. Our results demonstrate how genotype-by-environment interactions contribute to the complexity of microbiome assembly in natural environments.
Collection of exudates from wild type mycorrhizal and non mycorrhizal roots
<p>The aim of the experiment was to define a protocol for root exudates collection from tomato plants (<em>Solanum lycoipersicum </em>cv. M82) colonized or not with <em>Funneliformis mosseae</em>, a very common mycorrhizal fungus in nature. Mycorrhizal plants were grown on sandy soil under well-watered conditions under low nitrogen and phosphorus level alongside their not-inoculated control, non mycorrhizal, plants. Exudates were collected by washing the medium with 1 volume of sterile distilled water. Collections have been performed on mature mycorrhizas (50 days from inoculum) collecting root exudates every 4 days for 4 times. After the last collection, plants were sampled and some plant traits measured. Root exudate samples will be analysed by UNITO-DISAFA in terms of total organic carbon, organic acids, amino acids, enzymes (phosphatase and phytase) and SL content.</p>
Root responses to neighbors depend on neighbor identity and resource distribution
<p>In a complex soil environment, competitive and environmental factors will interact with individual traits to influence a plant's root growth patterns and ability to compete for resources. Here, we examine how root growth of a focal plant, <i>Plantago lanceolata</i> L., responds to resource heterogeneity and to presence of two neighbor species, <i>Centaurea jacea</i> L.and <i>Poa pratensis</i> L. A full factorial experiment tested the effects of nutrient heterogeneity, neighbors, and their interaction on root responses of <i>Plantago</i>. Roots in shared quadrants of a pot were harvested and quantified by qPCR for plants grown alone or with a neighbor, in patchy or even soil. The effects of experimental treatments on <i>Plantago</i> root mass distribution were tested with two-way ANOVA. When soil resources were evenly distributed, <i>Plantago</i> individuals increased root allocation to soil shared with a <i>Centaurea</i> neighbor but not a <i>Poa</i> neighbor. When soil resources were patchy, <i>Plantago</i> responded more strongly to <i>Poa</i> than to <i>Centuarea</i>, and placed more roots in the high-resource patch. These results demonstrate that plants can respond differently to neighbors depending on species and that integrating multiple cues results in non-additive effects on root behavior.</p>
Images, data, and statistical analysis scripts for review article on cover crop roots
<p>Images, data, and statistical analysis scripts for review article on cover crop roots.</p> <blockquote> <p><strong>Optimization of root traits to provide enhanced ecosystem services in agricultural systems: a focus on cover crops</strong> - [<a href="https://doi.org/10.1111/pce.14247">https://doi.org/10.1111/pce.14247</a>]</p> </blockquote> <ul> <li>Research site, planting, and growth <ul> <li>10/2020 - 04/26/2021 cover crop field trial. DDPSC FRS at Planthaven Farm, O'Fallon, MO 63366 (latitude 38.848240°, longitude -90.686640°). </li> <li>The field was tilled before sowing of cover crops. Seed for each cover crop were spread in using a push seed spreader and were lightly irrigated.</li> <li>Alfalfa (<em>Medicago sativa</em>), dundale pea (<em>Pisum sativum</em>), milkvetch (<em>Astragalus canadensis</em>, <em>Astragalus bisulcatus</em>), crimson clover (<em>Trifolium incarnatum</em>), hairy vetch (<em>Vicia villosa</em>), mustard (<em>Brassica junce</em>a var Mighty Mustard, var Kodiak), barley (<em>Hordeum vulgare</em>), wheat (<em>Triticum aestivum</em>, winter, spring), winter rye (<em>Secale cereale</em>), and triticale (× T<em>riticosecale</em> Wittmack).</li> </ul> </li> <li> <p>Field harvest measurements</p> <ul> <li> <p>Four canopy images were taken across each cover crop row using a Canon 5DS R camera. Images were taken from above each plot at 5ft height manually. Green color was thresholded from the canopy images in batch using OpenCV python script and the percent green cover calculated (Jupiter notebook).</p> </li> <li> <p>Five soil monoliths were excavated using a "shovelomics" approach with an average monolith size of 25.4cm x 25.4cm x 20 cm. The remaining four soil monoliths were destructively analyzed.</p> </li> <li> <p>One soil monolith was imaged using a Canon 50D DLSR camera in a photogrammetry shed. All photogrammetric analysis was conducted using Pix4D mapper software (Pix4D S.A. Prilly, Switzerland), and point cloud cleaning was conducted in CloudCompare V2. 10.2.</p> </li> <li> <p>Cover crop shoots from the remaining soil monoliths were cut and placed into a paper bag for dry biomass determination (60oC for 5 days). A cover crop shoot count was conducted for each monolith with each tiller considered as a shoot for the grasses (barley, wheat, triticale). After cover crop shoot harvesting, a photo was then taken of each soil monolith with remaining weed biomass. A weed score was assigned to each image by one trained researcher with a score 1 low weeds to 5 high weed presence.</p> </li> <li> <p>Soil monoliths were the soaked briefly in water and then the soil washed using a hose keeping the roots. Roots were then scanned on an Epson Expression 12000XL Photo Scanner with transparency unit. Images labeled with "_part" were samples with too many roots for scanning and so were separately weighed. Dry root biomass was taken for the scanned and unscanned roots separately. Root length was determined from images using software RhizoVision Explorer (https://doi.org/10.5281/zenodo.4095629), total root length was estimated using scanned root length and scanned dry biomass with unscanned root biomass.</p> </li> <li> <p>Along each cover crop plot a 10ft trench was dug using a Yanmar Excavator Vi020-6 perpendicular to the row with each trench fully bisecting the plot. Trench was one bucket wide (19 inches) and approximately 36 inches deep in the middle of the row. The five deepest roots that could be observed in the trench wall was measured manually with a tape measure for each cover crop. A garden trowel and shovel were used to excavate and confirm roots in trench wall.</p> </li> <li> <p>Data was analyzed using R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R). PCA analysis was conducted using the “FactoMineR” package (Husson <em>et al</em>. 2019) to explore the relationships between the traits within the dataset and clustered by family.</p> </li> </ul> </li> </ul> <p>Individual ZIP file contents:</p> <ul> <li><code><strong>2021PlantHavenCovercrop_CanopyImages.zip</strong></code> – Raw canopy images, processed percent green cover images, and Jupiter notebook python script (2021PlantHavenCovercrop_ImageBatchColorThreshold.ipynb).</li> <li><code><strong>2021PlantHavenCovercrop_RootFlatbedImages.zip</strong></code> – Raw flatbed root scans of cover crops and processed images using RhizoVision Explorer.</li> <li><code><strong>2021PlantHavenCovercrop_SoilMonolithWeedImages.zip</strong></code> – Images of soil monoliths after cover crop shoot biomass was removed.</li> <li><code><strong>2021PlantHavenCovercrop_dataprocessing.zip</strong></code> – R Statistics script and raw data used for data processing and figure generation (2021PlantHavenCovercrop_dataprocessing.R).</li> <li><code><strong>2021PlantHavenCovercrop_ShootPhotogrammetry.zip</strong></code> – 3D models of cover crop shoots from excavated soil monoliths. The .bin files can be opened using CloudCompare app.</li> </ul> <p> </p> <p> </p>
Large seeds provide an intrinsic growth advantage that depends on leaf traits and root allocation
<p>Seed mass and growth rate are important dimensions of plant ecological diversity, but their relationship remains unresolved. Negative relationships between relative growth rate (RGR) and seed mass are well established. However, RGR is size-dependent, so small-seeded species might achieve fast growth simply because they are initially small.</p> <p>Using a dataset of unprecedented size, sampling 382 grass species, we investigated seed mass and growth rate using both RGR and SGR (RGR at a specific size), accounting for diversity in phylogeny, ecology (e.g. life history, photosynthetic pathway) and environment (mean annual temperature and precipitation).</p> <p>RGR and SGR showed contrasting relationships with seed mass, such that large-seeded species had lower RGR but higher SGR than small-seeded species. However, the relationship between SGR and seed mass depended on leaf dry matter content (LDMC), and was only positive in high-LDMC species. When compared at a common size, the fast growth of large-seeded and low-LDMC species was associated with greater biomass allocation to roots in the hot, high-light environment used for our experiment. Photosynthetic pathway and life history contributed to variation in SGR, with C4 annuals having higher SGRs than C3 perennials regardless of seed size.</p> <p>Large seeds therefore afford an intrinsic growth advantage in species with resource-conserving leaf traits, and may provide a competitive edge in resource-poor environments. This work advances understanding of how seed mass and growth rate coevolve with other ecological factors.</p>
ACORBA: Automated workflow to measure Arabidopsis thaliana root tip angle dynamic
<p>Raw data linked to the research article "ACORBA: Automated workflow to measure Arabidopsis thaliana root tip angle dynamic"</p>
Genetic disruption of synthesis pathways of Arabidopsis secondary metabolites dramatically affects root-associated nematode populations directly and via modulation of microbial communities
<p>Dataset of nematode, fungal and bacterial sequence reads of Arabidopsis roots. Dataset of fungal and bacterial sequence reads of Arabidopsis microbial suspension. DNA concentration of Arabidopsis root microbial suspension. Meloidogyne incognita J2 invasion into tomato roots. qPCR dataset of Meloidogyne hapla infection pressure into Arabidopsis roots. </p>
Data from: Plant-type dominates fine-root C:N:P stoichiometry across China: a meta-analysis
<p>Aim: Fine roots play an important role in biogeochemical cycling in terrestrial ecosystems. However, our understanding of large scale biogeographic patterns and drivers of fine-root C:N:P stoichiometry is extremely limited.</p> <p>Location: China.</p> <p>Methods: We compiled data for fine-root carbon (C), nitrogen (N) and phosphorus (P) concentrations at 165 sites across China to explore large-scale biogeographic patterns and drivers of fine-root C:N:P stoichiometry.</p> <p>Results: The geometric means of fine-root C, N, and P concentrations were 448.81 mg g-1, 10.73 mg g-1, and 0.9 mg g-1, respectively, whereas C:N, C:P and N:P ratios were 41.84, 508.32 and 11.73, respectively. The fine-root elemental concentrations and their ratios varied widely among plant groups and biomes, and showed clear latitudinal and longitudinal trends, as a consequence of differences in climate, soil and plant-type. However, plant-type was the largest contributor to the total variance in fine-root C, N, and P and their ratios compared to climate factors or soil features.</p> <p>Main conclusions: The data reveal the existence of broad biogeographic patterns of fine-root C:N:P stoichiometry in China. These results advance our knowledge about the biogeochemical cycling of fine roots.</p>
Persistence of plant-mediated microbial soil legacy effects in soil and inside roots
<p>Plant-soil feedbacks are shaped by microbial legacies that plants leave in the soil. We tested the persistence of these legacies after subsequent colonization by the same or other plant species. Soil fungal legacies were detectable for months, but the current plant effect on fungi amplified in time. Contrary, in bacterial communities, legacies faded away rapidly and bacteria communities were influenced strongly by the current plant. However, both fungal and bacterial legacies were conserved inside the roots of the current plant species and their composition significantly correlated with plant growth. Hence, microbial soil legacies present at the time of plant establishment play a vital role in shaping plant growth even when these legacies have faded away in the soil due the growth of the current plant species. We conclude that soil microbiome legacies are reversible and versatile, but that they can create plant-soil feedbacks via altering the endophytic community acquired during early ontogeny.</p>
Tree species richness differentially affects the chemical composition of leaves, roots and root exudates in four subtropical tree species - Sampling Raw Data
<p>Sampling Raw Data for the manuscript "<strong>Tree species richness differentially affects the chemical composition of leaves, roots and root exudates in four subtropical tree species </strong>" </p> <p>R Code for producing the sunburst plots from the data obtained by classyFire</p> <p> </p>
Text-fig. 14. Longitudinal scraping marks on radius 98-595-B, root traces visible on the shaft. in Consumption Of Canid Meat At The Gravettian Předmostí Site, The Czech Republic
Text-fig. 14. Longitudinal scraping marks on radius 98-595-B, root traces visible on the shaft.
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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.
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DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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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.
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