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FIGURE 4 in Boletus recapitulatus (Boletaceae), a new species from India with peculiar mushroom-shaped cells
FIGURE 4. Boletus recapitulatus sp. nov. (DC 14-001) a. Basidiospores b. Pleurocystidia c. Cheilocystidia d. Basidia and basidiole e. Caulocystidia f & g. Hyphal elements on pileipellis. Bars: a–g = 10 μm.
FIGURE 1. Oreocharis tsaii. A. Habit. B. Leaf. C–F. Corolla shapes. G–J. Opened corolla showing stamens and staminode. K–L. Calyx. M. Pistil. N in Oreocharis tsaii, a new species of Gesneriaceae from southern Yunnan, China
FIGURE 1. Oreocharis tsaii. A. Habit. B. Leaf. C–F. Corolla shapes. G–J. Opened corolla showing stamens and staminode. K–L. Calyx. M. Pistil. N. Capsule. Photographed by Yun-Hong Tan.
FIGURE 1. Sapium sceleratum. A. Branch. B–C. Acropetiolar glands. D. Leaf margin. E–F. Leaf shapes and veins pattern. G. Inflorescence base. H. Staminate flower. I in Reinstatement of Sapium sceleratum (Euphorbiaceae), an endemic species from Northeast Brazil, and new circumscription of Sapium argutum
FIGURE 1. Sapium sceleratum. A. Branch. B–C. Acropetiolar glands. D. Leaf margin. E–F. Leaf shapes and veins pattern. G. Inflorescence base. H. Staminate flower. I. Pistillate flower showing calyx covering more than half of the length of the ovary. J. Pistillate sepal. A–C. A. M. Miranda 3273 (HST). D. A. M. Miranda 4214 (HST). E–H. Carvalho Sobrinho et al. 1840 (HVASF).
FIGURE 2. Sapium argutum. A. Branch. B–C. Acropetiolar glands. D. Leaf shape and veins pattern. E–F. Leaf margin. G. Inflorescence apex. H. Inflorescence base. I. Staminate flower. J. Pistillate flower. K. Pistillate sepal. L. Seed. A–L. W. Cordeiro 780 in Reinstatement of Sapium sceleratum (Euphorbiaceae), an endemic species from Northeast Brazil, and new circumscription of Sapium argutum
FIGURE 2. Sapium argutum. A. Branch. B–C. Acropetiolar glands. D. Leaf shape and veins pattern. E–F. Leaf margin. G. Inflorescence apex. H. Inflorescence base. I. Staminate flower. J. Pistillate flower. K. Pistillate sepal. L. Seed. A–L. W. Cordeiro 780 (PEUFR).
lateral pronotum fan-like on broad setae on) i setae (clove-shaped sericeous pronotum)) and b e ((head pronotum setae on setae on clove-shaped setae sericeous and clove-shaped sericeous and fan-like and with) h (sericeous pronotum setae) fan-like a (and australis head and) d sericeous Morbora (hirtula.) with setae a-c (Morbora dorsum fan-shaped species)) g d-f (= (Morbora fs scutellum schoutedeni; of setae vestiture on Morbora clove-shaped setae of) fan-like g-i = (cs micrographs: short scutellum) Abbreviations c (electron pronotum on setae. Scanning of clove-shaped scutellum: on 13 projections. setae Fig) f ( in Jewel Bugs of Australia (Insecta, Heteroptera, Scutelleridae)
lateral pronotum fan-like on broad setae on) i setae (clove-shaped sericeous pronotum)) and b e ((head pronotum setae on setae on clove-shaped setae sericeous and clove-shaped sericeous and fan-like and with) h (sericeous pronotum setae) fan-like a (and australis head and) d sericeous Morbora (hirtula.) with setae a-c (Morbora dorsum fan-shaped species)) g d-f (= (Morbora fs scutellum schoutedeni; of setae vestiture on Morbora clove-shaped setae of) fan-like g-i = (cs micrographs: short scutellum) Abbreviations c (electron pronotum on setae. Scanning of clove-shaped scutellum: on 13 projections. setae Fig) f (
FIGURE 1. Critoniopsis hermogenesii. A. Reproductive branch. B. Leaf shape. C in Critoniopsis hermogenesii (Vernonieae, Asteraceae), a new endemic species from Serra do Mar Mountain Range, São Paulo state, Brazil
FIGURE 1. Critoniopsis hermogenesii. A. Reproductive branch. B. Leaf shape. C. Types of inflorescences. D. Detail of capitulum. E. Corolla with papillae and trichomes at the apex of the lobes. F. Detail of corolla lobe. G. Anther. H. Style. I. Detail of the sweeping hairs. J. Cypsela. Drawing by Klei Sousa.
statistical analysis and data: How personality shapes gaze behavior without compromising subtle emotion recognition.
<pre>CODE:<br>script_gca_X.R: scripts used for the growth curve analysis fits<br>script_fits_brms_no_tb.r: script containing the fits (except those related to the crowth curve analysis).<br>inpact_script_contrasts.r: contrasts of every fits (call inpact_script_plot.r and inpact_script_save.r)<br>inpact_script_plot.r: plots<br>inpact_script_save: save contrasts to csv and xlsx files<br><br>DATA:<br>cluster_df.Rda: personality <br>neutral.Rda: neutral trials<br>sdtg.Rda: signal detection theory parameters<br>resp_emo.Rda : raw data to emotional trials<br>df_et4_propn.Rda: eye tracking data, first exposure phase of the emotional trials (0-1000ms)<br>df_et4_prop.Rda: eye tracking data, second exposure phase of the emotional trials (1000-2000ms)<br>df_n4_propn.Rda: eye tracking data, first exposure phase of the neutral trials (0-1000ms)<br>df_n4_prop.Rda: eye tracking data, second exposure phase of the neutral trials (1000-2000ms)<br><br>gca_e_contrast.Rda: contrasts of the growth curve analysis fit for the eye Area of Interest (AOI)<br>gca_n_contrast.Rda: contrasts of the growth curve analysis fit for the nose AOI<br>gca_m_contrast.Rda: contrasts of the growth curve analysis fit for the mouth AOI<br><br>X.rds: fits<br><br><br>STIMULI:<br>- videos of the neutral and emotional facial expressions<br>- backward masks</pre>
Data sets and code for Sullivan and Koski "The role of photosynthetic response to environmental variation in shaping an elevational cline in leaf variegation"
Open the record for dataset details and reuse information.
Data of "Influence of nanoparticle shape on hydrogen absorption in Palladium: A combined DFT and atomistic approach"
<p>xyz of structures, input and output files of DFT calculations, Bader charge files, cube files</p>
FIGURE 2 in A new species of Argostemma (Rubiaceae) with free stamens in star-shaped corolla from the Philippines
FIGURE 2. Argostemma separatum (drawn from the type specimen)—A. habit; B. adaxial surface of leaf. C. top view of open flower showing free stamens; D. open flower showing exserted stigma; E. free stamens; F. corolla lobe; G. calyx; H: style. Scale bars: A & B, 1 cm; C–H, 2mm. Illustrated by N. Diego.
FIGURE 1 in A new species of Argostemma (Rubiaceae) with free stamens in star-shaped corolla from the Philippines
FIGURE 1. Field photographs of Argostemma separatum. (A) Habit showing its lithophytic nature; (B) Flower showing free stamens in star-shaped corolla with red apex. Photos taken by D.R. Aranas.
Data supporting the analysis of lymphatic endothelial cell junctions and shape
<p><strong>Data in support of: </strong></p> <p><span><strong>Dynamic cytoskeletal regulation of cell shape supports<span> </span>resilience of lymphatic endothelium</strong></span></p> <p>Hans Schoofs<sup>1#</sup>, Nina Daubel<sup>1#</sup>, Sarah Schnabellehner<sup>1</sup>, Max Grönloh<sup>2</sup>, Sebastián Palacios Martínez<sup>3</sup>, Aleksi Halme<sup>4</sup>, Amanda M. Marks<sup>1</sup>, Marie Jeansson<sup>1</sup>, Sara Barcos<sup>5</sup>, Cord Brakebusch<sup>6</sup>, Rui Benedito<sup>7</sup>, Britta Engelhardt<sup>5</sup>, Dietmar Vestweber<sup>8</sup>, Konstantin Gängel<sup>1</sup>, Fabian Linsenmeier<sup>9</sup>, Sebastian Schürmann<sup>9</sup>, Pipsa Saharinen<sup>4,10</sup>, Jaap D. van Buul<sup>2,3,11</sup>, Oliver Friedrich<sup>9</sup>, Richard S. Smith<sup>12</sup>, Mateusz Majda<sup>13</sup>, and Taija Mäkinen<sup>1,4,10</sup>*</p> <p> </p> <p><sup>1</sup>Uppsala University, Department of Immunology, Genetics and Pathology, Dag Hammarskjölds väg 20, 751 85 Uppsala, Sweden.</p> <p><sup>2</sup>Department of Medical Biochemistry at the Amsterdam UMC, location AMC, The Netherlands.</p> <p><sup>3</sup>Department of Molecular Cytology, Leeuwenhoek Centre for Advanced Microscopy at Swammerdam Institute for Life Sciences at the University of Amsterdam, The Netherlands.</p> <p><sup>4</sup>Translational Cancer Medicine Program and Department of Biochemistry and Developmental Biology, University of Helsinki, Haartmaninkatu 8, 00014 Helsinki, Finland.</p> <p><sup>5</sup>Theodor Kocher Institute, University of Bern, Bern, Switzerland.</p> <p><sup>6</sup>Biotech Research and Innovation Center, University of Copenhagen, Ole Maaløes Vej 5, 2200 Denmark.</p> <p><sup>7</sup>Centro Nacional de Investigaciones Cardiovasculares, Melchor Fernández Almagro 3, E-28029 Madrid, Spain.</p> <p><sup>8</sup>Max Planck Institute for Molecular Biomedicine, Münster, Germany.</p> <p><sup>9</sup>Institute of Medical Biotechnology, Department of Chemical and Biological Engineering, Friedrich-Alexander-University, Erlangen-Nürnberg, Paul-Gordan-Str.3, 91052 Erlangen, Germany.</p> <p><sup>10</sup>Wihuri Research Institute, Haartmaninkatu 8, 00290 Helsinki, Finland.</p> <p><sup>11</sup>Amsterdam UMC, Sanquin Research and Landsteiner Laboratory, The Netherlands.</p> <p><sup>12</sup>John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK.</p> <p><sup>13</sup>Department of Plant Molecular Biology, University of Lausanne, CH-1015 Lausanne, Switzerland.</p> <p><sup>#</sup>These authors contributed equally.</p> <p>*Corresponding author: Taija Mäkinen, E-mail: <a href="mailto:taija.makinen@igp.uu.se">taija.makinen@igp.uu.se</a>, <a href="mailto:taija.makinen@helsinki.fi">taija.makinen@helsinki.fi</a></p> <p> </p> <p><strong>DATASET A: Annotated cell-cell junction types in lymphatic capillaries of wild type mouse ear skin at different ages <br></strong>__________________________________________________________________________________________________________</p> <p><strong>Contents</strong></p> <ul> <li>SOURCE DATA Fig1 FINAL. xlsx</li> <li>3w <ul> <li>animal 1</li> <li>animal 2</li> <li>animal 3</li> <li>animal 4</li> <li>animal 5</li> <li>sprouts</li> </ul> </li> <li>5w <ul> <li>animal 1</li> <li>animal 2</li> <li>animal 3</li> <li>animal 4</li> <li>animal 5</li> <li>diaphragm <ul> <li>Overview of diaphragm and high mag. of different capillary ends</li> </ul> </li> <li>trachea</li> </ul> </li> <li>25w <ul> <li>animal 1</li> <li>animal 2</li> <li>animal 3</li> <li>animal 4</li> <li>animal 5</li> <li>diaphragm</li> <li>trachea</li> </ul> </li> </ul> <p><strong>File legends<br></strong></p> <p>C1 images: inverted LYVE1 signal (.tif)<br>C2 images: inverted VE-cadherin signal (.tif)<br>MAX images: RGB merge of LYVE1 (cyan) and VE-cadherin (red) (.tif)<br>"NAME".roi: Regions of interest (ROI) of annotated junctions can be imported in ImageJ</p> <p><strong>Methods</strong></p> <p><em>Junctional classification:</em> <br>Analysis of junction morphology was done on blunt-ended initial lymphatic capillaries in the segment between the intial tip and the first valve. Junction types were quantified in Z-stack projection by numbering of individual lobes of LYVE1 and VE-cadherin-stained LECs and subsequent categorizing of lobe-associated junctions based on VE-cadherin signal.</p> <p>Four categories were defined:</p> <p>1) Button junction – a punctate VE-cadherin<sup>+</sup> deposit at the neck of LYVE1<sup>+</sup> lobe/overlap, with no detectable VE-cadherin at the borders of the overlap,</p> <p>2) Curvilinear junction – unsegmented(continous) or segmented (discontinuous) distribution of VE-cadherin within one border of LYVE1<sup>+</sup> lobe/cellular overlap,</p> <p>3) Double junction – unsegmented(continous) or segmented (discontinuous) distribution of VE-cadherin within both borders of LYVE1<sup>+</sup> lobe/cellular overlap, and</p> <p>4) LYVE1- curvilineair junction – unsegmented(continous) linear VE-cadherin distribution at cell-cell contacts in the absence of LYVE1.</p> <p>Wild-type C57BL/6J mice were used for analysis of junction types, and 4-5 blunt ended vessels per mouse from five mice per age group and condition were analysed; in total 1785 junctions were annoted</p> <p><em>Imaging:<br></em>Confocal images were obtained using a Leica Stellaris 5 confocal microscope equipped with 405 nm and white light lasers, 63x/1.3 HC PL APO CORR CS2 Glycerol immersion objective, and Leica LAS X software. Images were aquired at 1.51 digital zoom using a 2048x2048 resolution</p> <p><em>Tissue processing and staining: </em><strong> <br></strong>Tissues were fixed in 4% paraformaldehyde for 2 h at RT and permeabilized in 0.3% Triton X-100 in PBS (PBST) for 10 min. After blocking in PBST with 2% bovine serum albumin, 1% FBS for 2 h, tissues were incubated with primary antibodies in blocking buffer overnight, followed by PBST washing and incubation with fluorescent dye-conjugated secondary antibodies for 2 h. All incubation steps were carried out at RT. Prior to mounting in Mowiol, samples were repeatedly washed in PBST and water. Antibodies used: Goat anti-mouse VE-cadherin (R&D Systems, AF1002; 1:200), Rat anti-mouse LYVE1 (R&D Systems, MAB2125; 1:200)</p> <p> </p> <p><strong>DATASET B: Finite element method (FEM) simulations of cellular stresses<br>_______________________________________________________________</strong></p> <p>The FEM simulations were performed with MorphoMechanX using available models adapted from Sapala et al, <em>eLife</em> <strong>7</strong>, e32794 (2018). A regular cylindrical grid 45 µm wide and 200 µm long was created and outlines from the cells of a lymphatic vessel were projected onto it and smoothed. These cells were then extruded inward to make 3D volumetric cells with a depth of 2 µm and triangulated using a threshold area of 4 µm. The template was then used as the reference configuration for triangular 3 node membrane elements which were given a thickness of 0.1um. An isotropic St. Venant material model (linear, large deformation) was used with the Young's modulus set to 100 kPa to match a 10 kPa cell level Young's modulus estimated from the literature (ignoring the cell ends, the 2 x 0.1 µm membrane thickness occupied roughly 1/10<sup>th</sup> the cross-sectional area of the cell that were 2 µm deep). A uniform internal pressure was applied normal to the inside faces of the elements, which cancels out on the shared walls between cells. For simulations with a lower pressure inside the vessel, the inside faces were assigned a higher pressure. Stresses were visualized as the trace of the stress tensor.</p> <p><strong> </strong></p>
FIGURE 4. A–J. Pontederia reflexa. A. Habit. B. Different leaves showing the leaf-shape variation. C. Inflorescence. D in Two new species of Pontederia L. (Pontederiaceae Kunth) to South America
FIGURE 4. A–J. Pontederia reflexa. A. Habit. B. Different leaves showing the leaf-shape variation. C. Inflorescence. D. Trichome of the inflorescence rachis and perianth. E. Detail of a flower sectioned in the middle. F. Stamens. G. Trichome of the filament. H. Gynoecium. I. Trichome of the style. J. Dispersion unit. (D.J.L. Sousa 553).
Kinetically Trapped Nanocrystals with Symmetry-Preserving Shapes
<p>This .zip file contains the raw data for the manuscript "Kinetically Trapped Nanocrystals with Symmetry-Preserving Shapes". Folder and files are named according to main text and supporting information figures and videos. All files are compressed with tar.gz</p> <p>Data types:<br>* .pos files are to be opened with INJAVIS (https://engellab.de/injavis, https://zenodo.org/records/10125525)<br>* .txt files are the raw data for plots<br>* Input data of all simulations is found in Table S1 of the supporting information.</p>
From soil to sediment: Bedform migration shapes microbial communities from eroding bank soil during terrestrial-aquatic regime shift [dataset]
<p>This dataset contains raw data of metabolism measurements, microbial abundance, and nutrients that were assessed for the research paper entitled "From soil to sediment: Bedform migration shapes microbial communities from eroding bank soil during terrestrial-aquatic regime shift," which has been submitted to the Journal of Geophysics: Biogeosciences on October 19, 2024. Please note that raw sequencing data were already submitted to the NCBI library and are accessible via https://www.ncbi.nlm.nih.gov./sra/PRJNA1154565.</p> <p>The excel sheet (.xlsx) contains all relevant parameters that were used to detect the influence of environmental parameters on the microbial community structure, e.g. bacterial, fungal and algal abundance, nutrient concentrations, and final NCP and CR rates. The .zip archive contains all raw metabolism measurements during our experimental duration.</p>
A Cross-Continental Analysis of How Regional Cues Shape Developers' Stack Overflow Contributions – Replication Package
<p>Stack Overflow provides a wide range of knowledge for the software development community. Despite the importance of these platforms, several studies have shown that digital information tends to cluster geographically, which limits knowledge access that is otherwise necessary for innovation.</p> <p>The proposed study highlights the dynamics of users from different geographical backgrounds within Stack Overflow, which entails intra-country interactions, predominant topics of discourse, as well as their communication patterns. Finally, the study highlights that regional behavioural variations stem beyond cultural factors, encompassing technological advancement, entrepreneurial ventures, and workforce composition. </p> <p>This replication package is provided for those interested in further examining our research methodology.</p>
Important role of intra-transform spreading centers in shaping thermal structure along the Blanco transform fault
<p>The input files for the six models presented in the paper are included in the accompanying compressed file.</p>
Transfemoral hybrid socket brim shapes
<p>This dataset was used to develop a standardized hybrid transfemoral socket brim set. The dataset was analysed by measuring the dimensions of the brims, to seek for correlations. The latter was identified using statistical analysis. </p> <p>The brims are manufactured by Papenburg Orthopaedics, Ravenstein, The Netherlands.</p>
Data accompanying "Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars"
<p>This dataset contains the data used in the burned area classificaiton and statistical analysis in:</p> <p><em>Rietze et al. (<strong>in prep.</strong>) - Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars</em></p> <p>All code to preprocess, analyze and visualize this data can be found under <a href="https://github.com/nrietze/SiberiaFires" target="_blank" rel="noopener">https://github.com/nrietze/SiberiaFires</a>.</p> <p><strong>Folder structure:</strong></p> <p>This dataset contains three major components:</p> <ol> <li>The feature layers like training polygons used for the burned area classification and areas of interest.</li> <li>The burned area maps produced from 3 m PlanetScope imagery.</li> <li>The raster layers used in the statistical analysis, e.g., pre-processed digital elevation models and burned area products.</li> </ol> <pre><code>└───geodata ├───feature_layers │ ├───aoi_wv │ ├───burn_polygons │ │ └───planet │ └───training_polygons └───raster ├───arcticDEM ├───burned_area │ └───planet ├───landsat ├───predictors └───water_area └───planet</code></pre> <p><strong>Detailed description of content:</strong></p> <p> </p> <p><em><strong>Feature layers</strong></em></p> <table> <tbody> <tr> <td>planet_masks.shp</td> <td>Manually delineated polygons to mask out undetected clouds in the Berelech and Lapcha sites.</td> </tr> <tr> <td>aoi_wv/aois_analysis.geojson</td> <td>Square areas of interest for data cropping and selection.</td> </tr> <tr> <td>burn_polygons/planet/rough_burn_perimeter_{<em>fire scar name</em>}.shp</td> <td>6 shapefiles (1 per fire scar) that contain the fire perimeter generated in ArcGIS.</td> </tr> <tr> <td>training_polygons/training_polygons_burn_area.shp</td> <td>Training polygons for all fire scars used for the burned area classification.</td> </tr> </tbody> </table> <p> </p> <p><strong><em>Raster data</em><br></strong></p> <table> <tbody> <tr> <td>arcticDEM/aoi_{<em>fire scar name</em>}_dem_v3_utm.tif</td> <td>Elevation data (<a href="https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics/v3.0/2m.json">ArcticDEM v3</a>) reprojected to UTM 55N and cropped to the square areas of interest of each fire scar. </td> </tr> <tr> <td>burned_area/ba_descals_landsat_2020_utm_shifted.tif</td> <td>Landsat-based burned area from <a href="https://doi.org/10.1126/science.abn9768" target="_blank" rel="noopener">Descals et al. (2022)</a>, contains data from "Tile 17" only. Dara was reprojected to UTM 55N align with the Landsat-8 Collection-2 Level-2 grid. (burned class (2019) = 29, burned class (2020) = 30, unburned = 0)</td> </tr> <tr> <td>burned_area/N75E145_burn_class_UTM_55N.tif</td> <td>Landsat-based burned area from <a href="https://vapd.gitlab.io/post/gabam/" target="_blank" rel="noopener">Wei et al. (2022)</a>. Dara was reprojected to UTM 55N align with the Landsat-8 Collection-2 Level-2 grid. (burned class = 255, unburned = 0)</td> </tr> <tr> <td>burned_area/planet/{<em>fire scar name</em>}_burned_area_top5TD.tif</td> <td>Binary PlanetScope-based burned area (this study) for each fire scar based on random forest classifiers using the top 5 predictors ranked by transformed divergence (TD). (burned class = 2, unburned = 1)</td> </tr> <tr> <td>landsat/LC08_L2SP_115010_20200608_20200824_02_T1_{<em>spectral or quality band</em>}.TIF</td> <td>Landsat-8 Collection-2 Level-2 pre-fire image from 8 June 2020. B4 = RED, B5 = NIR, ST_B10 = LST</td> </tr> <tr> <td>landsat/LC08_L2SP_116010_20200615_20200824_02_T1_{<em>spectral or quality band</em>}.TIF</td> <td>Landsat-8 Collection-2 Level-2 pre-fire image from 15 June 2020. B4 = RED, B5 = NIR, ST_B10 = LST</td> </tr> <tr> <td>predictors/{<em>fire scar name</em>}_predictors_30m.tif</td> <td>Raster maps of model predictors & the response <em>burned_fraction</em> for the ZOIB model (exported in "ZOIB_model.R" before running the model). Raster bands are named.</td> </tr> <tr> <td>water_area/{<em>fire scar name</em>}_Landsat_mask.tif</td> <td>Landsat-8 binary (water = 2, clear pixels = 1) water mask for each fire scar based on the Quality assessment rasters.</td> </tr> <tr> <td>water_area/planet/{<em>fire scar name</em>}__water_area_top5TD.tif</td> <td>[deprecated] 3 m resolution water areas classified from PlanetScope imagery. Not used for the analysis.</td> </tr> </tbody> </table> <p> </p> <p><strong>Using this data:</strong></p> <p>Clone the Github repository before downloading this data and insert the contents of this dataset into the empty "data" folder from the Github repo. </p> <p>Important: Please move the entire "geodata" folder into the "data" folder from the code repo.</p> <p>If you use this data, please cite as follows:</p> <p>Rietze, N., Heim, R. J., Troeva, E., Schaepman-Strub, G. & Assmann, J. J. (in prep.). Data accompanying "Pre-fire Vegetation Conditions and Topography Shape Burn Mosaics of Siberian Tundra Fire Scars" [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12650945" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12650945</a></p> <p><strong>Abstract (from manuscript):</strong></p> <p>The fire season of 2020 in Siberia set a precedent for extreme wildfires in the Arctic tundra. Large fires burned in the carbon-rich permafrost landscape, releasing vast amounts of carbon, and changing land surface processes by burning vegetation and organic soils. However, little is known about the mosaics of burned and unburned patches formed by tundra fires and the underlying processes that generate them. In this study, we investigated six fire scars in the northeastern Siberian tundra using high-resolution PlanetScope imagery (3 m) to map burned fraction within the scars. We then used Bayesian mixed models to identify which biotic and abiotic predictors influenced the burned fraction. We observed high spatial variation in burned fraction across all tundra landforms common to the region. Current medium-resolution fire products could not capture this heterogeneity, thereby underestimating the burned area of fire scars by a factor of 1.1 to 4.4. The heterogeneity of the burn mosaic indicates a mix of burned and unburned patches, with median unburned patch sizes being smaller than 180 to 324 m². Pre-fire land surface temperature, vegetation heterogeneity and topography predicted burn fraction in our analysis, matching factors previously shown to influence large-scale fire occurrence in the Arctic. Future studies need to consider the fine-scale heterogeneity within tundra landscapes to improve our understanding and predictions of fire spread, carbon emissions, post-fire recovery and ecosystem functioning.</p> <p><strong>Acknowledgements (from manuscript):</strong></p> <p>N.R. was supported through the TRISHNA Science and Electronics Contribution (T-SEC), ESA PRODEX Trishna T-SEC project (PEA C4000133711). Field work and vegetation sample processing were conducted in the scope of State Assignment of the Ministry of Science and Higher Education of the Russian Federation (Project AAAA-A21-121012190038-0), using the equipment of the Centre for collective use of Federal Research Centre "Yakut Scientific Centre" (grant no. 13.TsKP.21.0016). We would like to thank Planet Labs fo free access to PlanetScope imagery. We would like to thank Tim Gyger for helpful discussions regarding our statistical analysis. The authors declare no competing interests.</p>
Data from: Heterochronic shifts and conserved embryonic shape underlie crocodylian craniofacial disparity and convergence
The distinctive anatomy of the crocodylian skull is intimately linked with dietary ecology, resulting in repeated convergence on blunt- and slender-snouted ecomorphs. These evolutionary shifts depend upon modifications of the developmental processes which direct growth and morphogenesis. Here we examine the evolution of cranial ontogenetic trajectories to shed light on the mechanisms underlying convergent snout evolution. We use geometric morphometrics to quantify skeletogenesis in an evolutionary context and reconstruct ancestral patterns of ontogenetic allometry to understand the developmental drivers of craniofacial diversity within Crocodylia. Our analyses uncovered a conserved embryonic region of morphospace (CER) shared by all non-gavialid crocodylians regardless of their eventual adult ecomorph. This observation suggests the presence of conserved developmental processes during early development (before Ferguson stage 20) across most of Crocodylia. Ancestral state reconstruction of ontogenetic trajectories revealed heterochrony, developmental constraint, and developmental systems drift have all played essential roles in the evolution of ecomorphs. Based on these observations, we conclude that two separate, but interconnected, developmental programs controlling craniofacial morphogenesis and growth enabled the evolutionary plasticity of skull shape in crocodylians.
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.