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3,682 results for “ROOTS”
Library of simulated root images
<p>This depository contains the images and associated RSML files used in the paper entitled "Using a structural root system model for an in-depth assessment of root image analysis pipeline" from the same authors.</p> <p> </p> <p>It contains: </p> <p>- 10.000 simulated root images;</p> <p>- 10.000 corresponding RSML files;</p> <p>- .csv files containing the ground-truth data for each modelled root system;</p> <p>- .csv files containing the image descriptors extracted using RIA-J;</p> <p>- .tps files containing the shape descriptors data.</p> <p> </p> <p>The codes used with this data is available here: https://zenodo.org/record/62063</p>
Root images of rice plant
<p>These root images of rice plant were used for<em> </em>analysising root morphological indexs like length, diameter, surface area and volume by the image anaiysis software like <em>WinRhizo</em>. These grayscale images of roots were obtained using an EPSON1680 scanner.</p>
Library of simulated root images, with different noise levels
<p>This depository contains the images and associated RSML files used in the paper entitled "Using a structural root system model to evaluate and improve the accuracy of root image analysis pipelines" from the same authors.</p> <p> </p> <p>It contains: </p> <p>- 30.000 simulated root images of 10.000 different root systems, with different noise levels (0=null, 1=medium, 3=high);</p> <p>- 10.000 corresponding RSML files;</p> <p>- .csv files containing the ground-truth data for each modelled root system (500-data.csv);</p> <p>- .csv files containing the image descriptors extracted using RIA-J (500-descriptors.csv);</p> <p> </p> <p>The codes used to generate and analyse this dataset is available here: http://doi.org/10.5281/zenodo.208499</p> <p>Note about the metrics computed from the RSML files (contained in 500-data.csv): the root systems were simulated in a constrained 2D space (rhizotron-like). Therefore, the RSML_reader plugin computed the lengths of the different roots in 2D only (despite the fact that a Z coordinate is present in the the RSML files). </p> <p> </p> <p><strong>CORRECTION</strong>: There is a scale issue in the 500-descriptors.csv file. "area" and "convexhull" columns values should be divided by 10 </p> <p> </p> <p> </p>
Data and results for manuscript "Multi-frequency electrical impedance tomography as a non-invasive tool to characterize and monitor crop root systems "
<p>Root systems are essential in nutrient uptake and translocation, but are difficult to characterize non-invasively with existing methods. We propose electrical impedance tomography (EIT) as a new tool for the imaging and monitoring of crop root systems. In a laboratory experiment we demonstrate the capability of the method to capture physiological responses of root systems with high spatial and temporal resolution. We conclude that EIT is a promising functional imaging technique for crop roots.</p> <p>This package contains measured raw EIT data, electrical imaging results, spectral results from the Debye decomposition, and the Python scripts used to generate the plots in the manuscript.</p>
Unraveling clonal trait space: Beyond aboveground and fine-root traits
<p>Plant trait variation is constrained by mechanical and energetic tradeoffs as attested by the global spectrum of plant form and function and the fine-root economics space for above- and belowground traits. However, traits that are key for fitness maintenance in some plant groups, such as clonal and bud bank traits, have not yet been integrated within the frameworks provided by the aboveground and the fine-root economics space.</p> <p>By using an extensive dataset encompassing aboveground, fine-root, clonal, and bud bank traits of 2000 species of Central European herbs, we asked whether clonal and bud bank traits correspond to the placement of species in the aboveground or fine-root trait spaces.</p> <p>Perennial clonal and non-clonal herbs show indistinct positioning within the aboveground and fine-root trait spaces. This extends and reinforces previous fragmentary evidence of weak correlations between clonal and bud bank traits and aboveground trait dimensions. Additionally, we identify for the first time a limited correlation between clonal and fine-root traits as well. This disconnection suggests that clonal traits operate independently from other trait spectra. For this reason, we introduce the concept of a "clonal trait space" for clonal herbs. The first dimension of this space is defined by bud bank size and the persistence of clonal connection, reflecting a gradient of species specialisation for on-spot persistence and tolerance to disturbance (persistence dimension). The second dimension, defined by multiplication rate and lateral spread, reflects a specialisation axis for clonal multiplication and horizontal size dimension (clonal multiplication dimension). Clonal trait dimensions add non-redundant information to the aboveground or fine-roots trait space.</p> <p><strong>Synthesis:</strong> We champion the integration of the persistence and clonal multiplication dimensions from the "clonal trait space" into the frameworks provided by the aboveground trait and the fine-root economics spaces, thereby enhancing our comprehension of the multifaceted trait strategies exhibited by plants.</p>
Clockor2: Inferring global and local strict molecular clocks using root-to-tip regression
<p>Molecular sequence data from rapidly evolving organisms are often sampled at different points in time. Sampling times can then be used for molecular clock calibration. The root-to-tip (RTT) regression is an essential tool to assess the degree to which the data behave in a clock-like fashion. Here, we introduce Clockor2, a client-side web application for conducting RTT regression. Clockor2 uniquely allows users to quickly fit local and global molecular clocks, thus handling the increasing complexity of genomic datasets that sample beyond the assumption homogeneous host populations. Clockor2 is efficient, handling trees of up to the order of 10^4 tips, with significant speed increases compared to other RTT regression applications. Although clockor2 is written as a web application, all data processing happens on the client-side, meaning that data never leaves the user's computer. Clockor2 is freely available at https://clockor2.github.io/</p>
Data from: The effect of drainage on the fine root biomass, production, and turnover in hemiboreal old-growth forests on organic soils
<p>Information on the capacity of organic soils to capture and store carbon in old-growth forests in the hemiboreal forest zone is scarce and fragmented. However, fine root data can provide valuable insights into soil carbon fluxes. Thus, the aim of the current study was to provide estimates of the fine root biomass (FRB), fine root production (FRP), and fine root turnover (FRT) rate by tree species and other functional groups in old-growth (stand age 131–179 years) forests on mesotrophic organic soils dominated by Scots pine (Pinus sylvestris L.), with (drained mesotrophic organic soil) and without (undrained mesotrophic organic soil) the effects of forest drainage. The sequential soil coring method was used to estimate the FRB and FRP. The total FRB (sum of the FRB of all functional groups) was significantly higher in the undrained sites (6.8±0.3 t ha 1) than in the drained sites (3.97±0.1 t ha 1). The FRB of Scots pine in the undrained forest was significantly higher (1.7±0.1 t ha 1) than in the drained forest (0.5±0.1 t ha 1), supporting an extensive foraging strategy. The significantly higher mean FRB of Norway spruce (Picea abies [L.] Karst.) (1.4±0.1 t ha 1) in the drained sites than the undrained sites (0.7±0.2 t ha-1) can be explained by there being a higher proportion of spruce in the stand compositions, thus a higher standing volume (cubic meters per hectare) of this species and an increased FRB. The FRB of dwarf shrubs (2.43±0.2 t ha-1) formed the largest part of the total FRB in the undrained sites and the second largest (1.16±0.1 t ha-1), following Norway spruce, in the drained sites. The total FRP was similar between the undrained (2.05±0.31 t ha-1 yr-1) and drained (1.82±0.26 t ha-1 yr-1) stands. However, considerable variability in the FRP was observed between different sites of the same forest site type. The FRT rate of Scots pine was twice as high in the drained sites than the undrained sites, suggesting faster nutrient and carbon input into the drained soil compared to the undrained soil. Estimates of FRB, FRP, and FRT rate for different functional groups can be used in carbon-cycle modeling and in further calculations to estimate the carbon budget (balance) in forests on organic soils.</p>
Figure 1 in Detection of Male Mediterranean Fruit Flies (Diptera: Tephritidae): Performance of Trimedlure Relative to Capilure and Enriched Ginger Root Oil
Figure 1. Capture of C. capitata males in TML- versus CPL-baited Jackson traps for 3 replicates in an Oahu coffee field. Abscissa represents period of lure ageing, where 0 weeks represents fresh lures. Bar heights indicate mean of 20 traps per lure type; error bars represent + 1 SE. Symbols above bars show results of the Tukey HSD test comparing the 2 lures for each ageing category, where an asterisk indicates P <0.001 and ns indicates no significant difference.
Shifts in evolutionary lability underlie independent gains and losses of root-nodule symbiosis in a single clade of plants
<p><span>Supplementary Data 4 from the manuscript "Shifts in evolutionary lability underlie independent gains and losses of root-nodule symbiosis in a single clade of plants". </span></p> <p><span>A concatenated alignment of all samples and all loci. This file was used for branch length estimation and not directly for phylogeny reconstruction (though an extraction of each subtree taxonomic samples subset from this alignment would be the alignment used to produce the scaffolded subtrees that were described in the paper). Sequences are labeled by well and can be mapped back to taxon for sub-setting and further use using Supplementary Data 1. </span></p>
Fig. 1 in Description of a New Mangrove Root Dwelling Species of Teleotanais (Crustacea: Peracarida: Tanaidacea) from India, with a Key to Teleotanaidae
Fig. 1. Teleotanais indiaensis sp. nov. A, holotype, habitus, lateral view; B, paratype, habitus, dorsal view; C, antennule; D, antenna; E, labrum, lateral view. Scale bars: 1.0 mm for A–B, 0.25 mm for C–D, 0.1 mm for E.
Fig. 2 in Description of a New Mangrove Root Dwelling Species of Teleotanais (Crustacea: Peracarida: Tanaidacea) from India, with a Key to Teleotanaidae
Fig. 2. Teleotanais indiaensis sp. nov. paratype. A, left mandible; B, right mandible; C, labium; D, maxillule, endite; E, same, palp; F, maxilliped; G, epignath; H, pleopod; I, uropod. Scale bars 0.1 mm.
STIR ROOT Consistency Test Data
<p>Data generated by `ROOT_STIR_consistency` for the `test_view_offset_root` test. This data is pre-generated GATE data of point sources, measured by a GATE geometry (similar to the GE Discovery 690), that is to be used to test the allignment between STIR and GATE crystal positions.</p>
Supplementary files for Transcriptomic alterations in roots of two contrasting Coffea arabica cultivars after hexanoic acid priming
<p>Supplementar files for Transcriptomic alterations in roots of two contrasting Coffea arabica cultivars after hexanoic acid priming.</p> <p>Supplementary Table 1. Differentially expressed genes (DEGs) identified in Obatã and Catuaí cultivars<br> Supplementary Table 2. Gene ontology (GO) enrichment analysis of differentially expressed genes (DEGs) identified in Obatã and Catuaí cultivars<br> Supplementary Table 3. MapMan pathway analysis of differentially genes expressed (DEGs) identified in Obatã and Catuaí cultivars<br> Supplementary Table 4. FPKM values for Catuaí cultivar genes and Roots DEGs<br> Supplementary Table 5. FPKM values for Obatã cultivar genes and Roots DEGs</p>
Manhattan and QQ plots of GWAS on salt stress responses in root system architecture parameters of wild tomato (S. pimpinellifolium)
<p>The population of +/2 200 accessions of wild tomato was screened with the protocol described <a href="https://www.protocols.io/view/studying-root-system-architecture-changes-in-tomat-2mqgc5w">here</a> with the only exception that the plants were transferred 4 days after germination (rather than 3 - described in the protocol). The images were analyzed using the <a href="https://smartroot.github.io/">SmartRoot</a> for days 0, 1, 2, 3, and 4 after transfer to treatment plates (0 or 100 mM NaCl, 1/4 MS, 0.5% sucrose, 0.1% MES, 1% Dashin agar). The data analysis was performed as described <a href="https://rpubs.com/mjulkowska/BIGpimp_RSA_salt">here</a>, while the pareto front calculations were done according to Chandrasekhar & Julkowska paper (<a href="https://www.biorxiv.org/content/10.1101/2021.08.12.456185v1">preprint here</a>). The GWAS was performed using the ASReml script similar to <a href="https://onlinelibrary.wiley.com/doi/10.1111/tpj.15310">Awlia et al. (2021)</a>. The raw GWAS outputs can be found <a href="https://zenodo.org/badge/DOI/10.5281/zenodo.5856310.svg">here</a>. This dataset represents Manhattan plots and QQ plots made out of the data. </p>
Conflict over the eukaryote root resides in strong outliers, mosaics and missing data sensitivity of site-specific (CAT) mixture models
Abstract Phylogenetic reconstruction using concatenated loci ("phylogenomics" or "supermatrix phylogeny") is a powerful tool for solving evolutionary splits that are poorly resolved in single gene/protein trees (SGTs). However, recent phylogenomic attempts to resolve the eukaryote root have yielded conflicting results, along with claims of various artefacts hidden in the data. We have investigated these conflicts using two new methods for assessing phylogenetic conflict. ConJak uses whole marker (gene or protein) jackknifing to assess deviation from a central mean for each individual sequence, while ConWin uses a sliding window to screen for incongruent protein fragments (mosaics). Both methods allow selective masking of individual sequences or sequence fragments in order to minimize missing data, an important consideration for resolving deep splits with limited data. Analyses focused on a set of 76 eukaryotic proteins of bacterial-ancestry previously used in various combinations to assess the branching order among the three major divisions of eukaryotes: Amorphea (mainly animals, fungi and Amoebozoa), Diaphoretickes (most other well-known eukaryotes and nearly all algae) and Excavata, represented here by Discoba (Jakobida, Heterolobosea, and Euglenozoa). ConJak analyses found strong outliers to be concentrated in under-sampled lineages, while ConWin analyses of Discoba, the most under-sampled of the major lineages, detected potentially incongruent fragments scattered throughout. Phylogenetic analyses of the full data using an LG-gamma model support a Discoba sister scenario (neozoan-excavate root), which rises to 99-100% bootstrap support with data masked according to either protocol. However, analyses with two site-specific (CAT) mixture models yielded widely inconsistent results and a striking sensitivity to missing data. The neozoan-excavate root places Amorphea and Diaphoretickes as more closely related to each other than either is to Discoba, a fundamental relationship that should remain unaffected by additional taxa.
Limited effect of thermal pruning on wild blueberry crop and its root-associated microbiota - Agricultural dataset
<p>These datasets contain all the agricultural data (soil chemistry, blueberry performance, weeds and diseases...) used in our study.</p>
Spatial structure within root systems moderates stability of Arbuscular Mycorrhizal mutualism and plant-soil feedbacks
<p class="RealLife">The persistence of mutualisms is paradoxical, as there are fitness incentives for exploitation. This is particularly true for plant-microbe mutualisms like arbuscular mycorrhizae (AM), which are promiscuously, horizontally-transmitted. Preferential allocation by hosts to the best mutualist can stabilize horizontal mutualisms, however preferential allocation is imperfect, with its fidelity likely depending upon the spatial structure of symbionts in plant roots. In this study, we tested AM mutualisms' dependence on two dimensions of spatial structure: the initial dispersion of fungi and the ease of fungal dispersal, through three complementary experiments. We found that fitness of the beneficial AM fungus increased when fungi were initially separate, while initial spatial mixing benefited the fitness of the non-beneficial fungus. These effects were strongest when dispersal was limited, and hosts could discriminate. Additionally, we found that spatial structure moderated changes in AM fungal composition produced differential feedbacks on plant growth. Our results identify symbiont spatial structure within plant roots as an important modifier of plant preferential allocation and the dynamics of mycorrhizal mutualisms, with cascading effects on plant communities.</p>
Supplementary Materials - videos - for 'The root to the Galápagos mantle plume on the core-mantle boundary' by Cottaar et al.
<p>Movie S1.</p> <p>Animation of interaction between the wavefront and the ultra-low velocity zone for Event A using a modified version of the wavefront tracker by (Hauser <em>et al.</em>, 2008). Only horizontal propagation is considered. Event location - blue star, seismometer locations – blue triangles. Velocities show the shear wave velocity deviations at 2800 km depth from the tomographic SEMUCB-WM1 (French and Romanowicz, 2014) and the ultra-low velocity zone. </p> <p> </p> <p>Movie S2.</p> <p>Animation of interaction between the wavefront and the ultra-low velocity zone for Event B using a modified version of the wavefront tracker by (Hauser <em>et al.</em>, 2008). Only horizontal propagation is considered. Event location - blue star, seismometer locations – blue triangles. Velocities show the shear wave velocity deviations at 2800 km depth from the tomographic SEMUCB-WM1 (French and Romanowicz, 2014) and the ultra-low velocity zone. </p> <p> </p> <p>Movie S3.</p> <p>Animation of interaction between the wavefront and the ultra-low velocity zone for Event C using a modified version of the wavefront tracker by (Hauser <em>et al.</em>, 2008). Only horizontal propagation is considered. Event location - blue star, seismometer locations – blue triangles. Velocities show the shear wave velocity deviations at 2800 km depth from the tomographic SEMUCB-WM1 (French and Romanowicz, 2014) and the ultra-low velocity zone. </p> <p> </p> <p>Movie S4.</p> <p>Animation of interaction between the wavefront and the ultra-low velocity zone for Event D using a modified version of the wavefront tracker by (Hauser <em>et al.</em>, 2008). Only horizontal propagation is considered. Event location - blue star, seismometer locations – blue triangles. Velocities show the shear wave velocity deviations at 2800 km depth from the tomographic SEMUCB-WM1 (French and Romanowicz, 2014) and the ultra-low velocity zone. </p>
Phylogenomic Analyses of 2,786 Genes in 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts and All Other Lineages
<p><strong>Abstract</strong></p> <p>Advances in phylogenetic methods and high-throughput sequencing have allowed the reconstruction of deep phylogenetic relationships in the evolutionary history of eukaryotes. Yet, the root of the eukaryotic tree of life remains elusive. The most ‘popular’ (i.e. in textbooks and reviews) hypothesis for the root is between Unikonta (Opisthokonta + Amoebozoa) and Bikonta (all other eukaryotes), which emerged from analyses of a single gene fusion and a limited sampling of eukaryotic lineages. Subsequent highly-cited studies based on concatenation of genes supported this hypothesis with some variations or proposed a root within the Excavata. However, concatenation of genes neither considers phylogenetically-informative events (i.e. gene duplications and losses), nor provides an estimate of the root. A more recent study using gene tree-species tree reconciliation methods suggested the root lies between Opisthokonta and all other eukaryotes, but only including 59 taxa and 20 genes. Here we apply a gene tree – species tree reconciliation approach to a gene-rich and taxon-rich dataset (i.e. 2,786 gene families from two sets of ~158 diverse eukaryotic lineages) to assess the root, and we iterate each analysis 100 times to quantify tree space uncertainty. Our results estimate a root between Fungi and all other eukaryotes, or between Opisthokonta and all other eukaryotes, and reject alternative popular roots from the literature. Based on further analysis of genome size we propose Opisthokonta + others as the most likely root. Finding the root of the eukaryotic tree of life is critical for the field of comparative biology as it allows us to understand the timing and mode of evolution of characters across the evolutionary history of eukaryotes.</p> <p>Methods<br> Here we provide the alignments, gene trees, inputs, and outputs from our project entitled "Phylogenomic Analyses of 2,786 Genes in 158 Lineages Support a Root of The Eukaryotic Tree of Life Between Opisthokonts and All Other Lineages". Sequences and alignments were produced using the phylogenomic pipeline PhyloToL, which contains a taxon- and gene-rich database (including eukaryotes, archaea, and bacteria). These data were then used for 1) assessing the root of the eukaryotes and 2) for comparison with other previously published hypotheses. In both cases, we used the species tree - gene tree reconciliation tool iGTP. We also did a comparison of hypotheses using the likelihood-based tool SpeciesRax</p> <p><strong>iGTP</strong></p> <p>Input data</p> <p>These data are divided into four datasets based on taxa selection. For dataset SEL+, taxa were selected based on their taxonomy; for RAN+, taxa were selected randomly among the major eukaryotic clades Opisthokonta, Amoebozoa, Archaeplastida, Excavata, SAR, and some orphan lineages. Datasets SEL- and RAN- are the same as SEL+ and RAN+, but exclude microsporidians in order to account for and avoid long branch attraction due to microsporidians fast-evolutionary rates. We chose the gene families that contain at least 25 taxa representing at least four of the five major eukaryotic clades. Additionally, at least 2 of the major clades had to contain at least 2 minor clades (e.g. Glaucophytes and Rhodophyta are minor clades in the major clade Archaeplastida). In a pilot analysis, we produced an alignment and a phylogenetic tree for each gene family using the default settings of a previous version of PhyloToL (GUIDANCE V1.3.1 sequence cutoff = 0.3 and column cutoff = 0.4; RAxML quick tree with model PROTGAMMALG and no bootstraps). Then, we kept the gene families that are exclusive of eukaryotes or the ones in which eukaryotes were monophyletic. From a total of 3,002 gene families that met our criteria, 2786 passed the initial steps of PhyloToL when including only the data from the dataset SEL+. These 2,786 gene families were used for further analyses with all datasets.</p> <p>MSAs were produced with PhyloToL (GUIDANCE V2.02 sequence cutoff = 0.3, column cutoff = 0.4, number of iterations = 5; Sela, et al. 2015). The default parameters of PhyloToL include up to five iterations of GUIDANCE V2.02 with 10 bootstraps and MAFFT V7 with algorithm E-INS-i for less than 200 sequences or “auto” option if more than 200 sequences, and maxiterate = 1000. Instead, here we run up to five iterations of GUIDANCE with 20 bootstraps and the simple MAFFT algorithm FFT-NS-2. Then, we perform an additional GUIDANCE run with 100 bootstraps and the default MAFFT parameters for PhyloToL.</p> <p>Gene trees were inferred with RAxML v.8.2.4 with 10 ML searches for best-ML tree (option "-# 10"), using the rapid hill-climbing algorithm (option "-f d") and no bootstrap replicates. The protein evolution model used was evaluated during the gene tree inference (option "-m PROTCATAUTO") by testing all models available in RAxML (e.g. JTT, LG, WAG, etc) with optimization of substitution rates and of site-specific evolutionary rates which were categorized into four distinct rate categories for greater computational efficiency.</p> <p>We ran 100 repetitions of iGTP analyses per dataset. But, given the complexity of the datasets and the heuristic nature of some key steps of the iGTP algorithm (e.g. gene tree rooting and initial starting species tree generation), in a preliminary analysis, we faced two systematic challenges with iGTP as the inferred species tree was affected by: 1) the order of the leaves in the input unrooted gene tree Newick strings (i.e. the input trees were treated as rooted even though we specified that they were not); and 2) the input gene order in the 100 replicates. Therefore, we randomly shuffled the order of the leaves in the unrooted gene trees (keeping the same topology), and randomly shuffled the order of the input gene trees in each of the 100 replicates per dataset. Here we provide the 100 input files generated for those iGTP analyses. </p> <p>Output data</p> <p>Here we also share the data generated after two analyses: 1) root assessment and 2) hypothesis testing. For the former, we allowed iGTP to calculate the more parsimonious root given our input files. For the latter, we allowed iGTP to calculate the reconciliation cost of the gene trees given the input files and constraints in the species trees to reflect previously published root hypotheses. The constraints are explained in the README file.</p> <p>SpeciesRax</p> <p>Since we removed LGT and contamination from our dataset using a series of filters, we applied the model UndatedDL instead of UndatedDTL, which implies that we only took into consideration duplications and losses and ignored the transferences. Then, the command used for SpeciesRax was...</p> <p>./generax --families forGeneRax/famFile --species-tree forGeneRax/spsTreeAn.newick --strategy SKIP --rec-model UndatedDL --per-family-rates --prefix An_DS1 --si-strategy EVAL </p> <p>input </p> <p>Here, we are sharing all the necessary files to run SpeciesRax, including the mapping file (famFile), the species trees (spsTrees; the best iGTP constrained species trees per hypothesis), and their underlying gene trees (trees_r)</p> <p>output</p> <p>Output folder from SpeciesRax, which includes log files, events (duplications, losses) counts, and statistics (i.e., reconciliation likelihood values)</p> <p>Note: <br> * As in the iGTP analyses, for the SpeciesRax files, the words Op, Fu, Di, Un, An, refer to the five root hypotheses compared: Opisthokonta-others, Fungi-others, Discoba-others, Unikonta-Bikonta, and (Ancyromonadida + Metamonada)-others, respectively. <br> * For the SpeciesRax analyses, the Un word refers to Ut (Cavalier-Smith 2003) <br> </p>
Volumetric imaging of Arabidopsis thaliana root cells
<p><em>Arabidopsis thaliana</em> seeds were surface sterilized, germinated and grown in Murashigue and Skoog medium at pH 5.7 and supplemented with vitamins (0.1 mg l-1 pyridoxine, 0.1 mg l-1 nicotinic acid), 0.8% agar, and 1% sucrose. Plants were grown at 21°C, 16/8-hour light/dark periods at 105 µmol/m<sup>2</sup>s<sup>2</sup> light intensity.</p> <p>Using a Zeiss Axiovert 200M microscope and a C-APO 63X, 1.2NA objective (Oberkochen, Germany), confocal volumetric imaging of the plant material was performed, with a pixel size of 404 nm and Z step size of 500 nm. Excitation of the sample was provided by a 488 nm laser, and a filter cube with 525/45 nm and 630/92 nm bandpass filters was used for yellow and red emission light collection, respectively.</p> <p>An inverted Olympus FV1000-IX81 confocal microscope equipped with a LUMFLN×60, 1.3NA S objective was used for root cell nuclear imaging. Sample excitation was achieved with a 543 nm laser and a BA560-660 filter was used for emission light collection. Pixel size was 41 nm, with a Z step size of 100 nm.</p> <p>A custom-built selective plane illumination microscopy (SPIM) system was used for imaging of primary root cells expressing p35s:H2B-R-RF. Sample excitation was achieved with a 561 nm laser using stroboscopic illumination. Emission was filtered via a multi-bandpass emission filter (Semrock, FF01-446/523/600/677-25 BrightLine). Volumetric imaging was done by mounting the sample on a four dimensional (XYZ, and Y rotation) motorized stage (Picard Industries). A sCMOS sensor (Hamamatsu, ORCA-Flash4.0 V2) was used for signal recording. The OpenSPIM plugin of µmanager (v.1.4 for windows) was used for control of acquisition parameters, sample translation and stroboscopic illumination. Collected images had a pixel size of 0.325µm, Z step size of 100 nm and a Y rotation step of 1.8°.</p> <p>Experimental procedures were approved by the Bioethics Committee of the Biotechnology Institute of the National Autonomous University of Mexico.</p>
ScienceDex guides
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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.