Find research datasets worth reusing

Figure 16 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod

Figure 16. Results of the second analysis. Strict consensus of 14 equally most parsimonious trees (length = 146 evolutionary steps, consistency index = 0.6918, retention index = 0.7115) generated from analysis based on data set of Gonźalez Riga & Ortiz David (2014). Open circles at nodes indicate a nodedefined clade name. Arcs on branches indicate a stem-defined lineage name. Op. = Opisthocoelicaudiinae. Numbers at nodes indicate bootstrap and decay index (Bremer value), formatted as decay index/bootstrap value. A dash (-) indicates a decay index of 0 and/or bootstrap value <50%.

Figure 15 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod

Figure 15. Results of first phylogenetic analysis based on data set of Lacovara et al. (2014). Strict consensus of 40 equally most parsimonious trees (length = 958 evolutionary steps; consistency index = 0.4238, retention index = 0.7072) set upon a geological timescale (Gradstein et al. 2012). Thickened branch sections indicate approximate temporal range for each terminal taxon. Temporal ranges of taxa were taken from multiple sources (Borsuk-Bialynicka 1977; Upchurch et al. 2004; Weishampel et al. 2004; de Jesus Faria et al. 2015). Open circles at nodes indicate a node-defined clade name. Arcs on branches indicate a stem-defined lineage name. See Table 1 for name definitions. Numbers at nodes indicate bootstrap and decay index (Bremer value), formatted as decay index/bootstrap value. A dash (-) indicates a decay index of 0 and/or bootstrap value <50%.

Figure 11 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod

Figure 11. Right ischia of Alamosaurus sanjuanensis in medial view. A, USNM 10487; B, TMM 41541-1; C, BIBE 45909. Image of USNM 10487 taken from Gilmore (1922). Abbreviations: acet, acetabular border; il, iliac peduncle; isc, ischial contact edge; pu, pubic contact edge. Scale bars = 20 cm.

Figure 10. Alamosaurus sanjuanensis, first caudal vertebra. A—C, TMM 41541-1 in An articulated cervical series of Alamosaurus sanjuanensis Gilmore, 1922 (Dinosauria, Sauropoda) from Texas: new perspective on the relationships of North America's last giant sauropod

Figure 10. Alamosaurus sanjuanensis, first caudal vertebra. A—C, TMM 41541-1 first caudal vertebra in A, anterior, B, right lateral, and C, posterior views. D, E, USNM 15560, line drawings of first caudal vertebra in D, anterior, and E, right lateral views. F, TMM 41541-1, close-up of first caudal centrum in right lateroventral and slightly anterior view; arrows point to at least three foramina ventral to transverse process. D and E drawn from Gilmore (1946). Abbreviations: for, foramen; nc, neural canal; ns, neural spine; posl, postspinal lamina; prsl, prespinal lamina; poz, postzygapophysis; prz, prezygapophysis; sprl, spinoprezygapophyseal lamina; tp, transverse process. A—C, scale bar = 20 cm.

opencc-by-4.0Mar 2024View details →

Long-time series vSAG 37-F6

<p>Supplementary data to the paper "Time series data provide insights into the evolution and abundance of one of the most abundant viruses in the marine virosphere: the uncultured pelagiphages vSAG 37-F6"</p> <div>Includes the reads for the 1 and 7-years time series</div> <div> </div>

opencc-by-4.0Feb 2024View details →

High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023

<p>This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline. </p> <h2>1) Maize cover and yield seasonal composites</h2> <p>The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at: </p> <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> <p>The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>.</p> <p>The land cover classification images contain 3 bands/variables: <em>maizeProb</em>, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; <em>majorityClass</em>, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and <em>optimalClass</em>, the categorical land cover class adjusted to agree with national statistics for expected maize area.</p> <p>The land cover classes map to the raster values as follows: </p> <div> <div> <pre>{<br> 1: 'maize',<br> 2: 'nonmaize_annual',<br> 3: 'nonmaize_perennial',<br> 4: 'scrub_shrub_land',<br> 5: 'forest',<br> 6: 'flooded_vegetation',<br> 7: 'water',<br> 8: 'structure',<br> 9: 'bare'<br>}</pre> </div> </div> <p>The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B.</p> <h3>Maize yield</h3> <p>The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>.</p> <p>Each of the images in the yield composites has 3 bands/variables also: <em>maizeYield</em>, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; <em>maizeYield_majorityClass</em>, predicted maize yield masked to the majority class land classification; and <em>maizeYieldAdj_optimalClass</em>, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics. </p> <p>The dataset includes the same seasons as the classification product; see above for a description.</p> <h2>2) End-to-end machine learning pipeline</h2> <p>All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season). </p> <p>The scripts needed to perform the pipeline are located in the <code>scripts</code> folder. </p> <p>The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user. </p> <p>Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed. </p> <h3>Google Cloud Storage and Earth Engine set-up</h3> <p>Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the <em>Objects</em> section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>.</p> <h3>Pipeline set-up</h3> <p>In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction. </p> <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> <p>In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover.</p> <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> <p>In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation. </p> <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> <p>If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>). </p> <h2>3) Input data into machine learning pipeline</h2> <p>A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024.</p> <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named <em>districts_fc</em> for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named <em>aez_rwanda</em> for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named <em>impact_obs_lulc</em> for use in the pipeline.</li> </ul> <p>(The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.)</p> <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul> <p> </p>

Figure 9 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 9. Ontogenetic development of pedicle foramen of Palaeotreta zhujiahensis from the Shuijingtuo Formation of western Hubei. A, enlarged pedicle notch of Figure 8A; B, juvenile with unrestricted pedicle notch, showing raised propareas (arrows), ELI-AJH 8-2-3 AC-11; C, posterior view of Figure 8B; D, 'U'-shaped pedicle notch, note the growth of propareas at the posterior margin of metamorphic shell (arrow); E, F, 'U'-shaped pedicle foramen is soon to be enclosed, note the growth of propareas (arrow); G, enclosed pedicle foramen with short intertrough, ELI-AJH 8-2-D AD2-12; H, larger adult showing pedicle foramen outside the metamorphic shell, ELI-AJH 8-2-3 CD2-02; I, enlargement of propareas growing at the posterior margin of metamorphic shell and lateral sides of pedicle foramen of G; J, lateral view of I, note propareas growth (arrow); K, posterior view, showing pedicle foramen mostly located outside the metamorphic shell, box indicates the area shown in L, ELI-AJH 8-2-3 AC-22; L, enlarged view showing propareas growth (arrow); M, enlargement of propareas at the posterior margin of metamorphic shell, note pedicle foramen (arrow), ELI-AJH 8-2-1 AE-09; N, pitting structures on metamorphic shell, ELI-AJH 8-2-1 AE-09; O, P, enlarged secondary columnar layer, ELI-AJH 8-2-1 AE-09, ELI-AJH 8-2-1 CE-03.

Figure 10 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 10. Ontogenetic scheme of Palaeotreta and Eohadrotreta from Cambrian Series 2 of South China, demonstrating transitions of important characters during different ontogenetic stages (modified from Z.-L. Zhang et al. 2018a, Claybourn et al. 2020).

Figure 7 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 7. Bivariate plots of ventral valves of Palaeotreta shannanensis gen. et sp. nov. from the Shuijingtuo Formation showing ontogenetic stage T1 (red) and stage T2 (blue). A, plots of valve width – length ratio (W/L), length at maximum width – valve length ratio (Lm/L), and valve height – length ratio (H/L). B, plots of apical process length – valve length ratio (La/L), pedicle foramen length – valve length ratio (Lf/L).

Figure 8 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 8. Ontogenetic development of ventral valve of Palaeotreta zhujiahensis from the Shuijingtuo Formation of western Hubei. A–F, ventral valves demonstrating pedicle foramen forming stage (T1); A, oblique dorsal view of a very small conjoined specimen showing unrestricted pedicle notch, ELI-AJH 8-1-2-B AF12; B, lateral view of a small conjoined specimen, ELI-AJH 8-2-3 AD2-07; C, posterior view, box indicates the area shown in Figure 9E, ELI-AJH S05 AG07; D, interior view of C; E, oblique view of a larger juvenile, ELI-AJH 8-2-3 AC-27; F, posterior view of E, note 'U'-shaped pedicle notch, box indicates the area shown in Figure 9D; G, lateral view, indicating pedicle foramen-enclosing stage (T2), showing enclosed pedicle foramen outside of the metamorphic shell, ELI-AJH 8-2-D AD2-12; H–N, intertrough-increasing stage (T3); H, posterior view, ELI-AJH S05 AF-16; I–N, adult with short intertrough, ELI-AJH 8-2-1 CE-03; I, exterior view; J, interior view; K, oblique lateral view; L, lateral view; M, posterior view, note the posterior migration of enclosed pedicle foramen, outside the metamorphic shell; N, oblique anterior view.

Figure 6 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 6. Ontogenetic development of pedicle foramen of Palaeotreta shannanensis gen. et sp. nov. from the Shuijingtuo Formation of southern Shaanxi. A, B, juvenile with unrestricted pedicle notch, note raised propareas (arrows), ELI-XYB S4-3 AV-09; C, D, juvenile with unrestricted pedicle notch, ELI-XYB S4-3 AV-17; E–G, semicircular pedicle foramen soon to be enclosed, ELI-XYB S4-3 AV-05; H, raised propareas (arrows), ELI-XYB S4-3 AV-11; I–L, pedicle foramen, just enclosed with very short intertrough, XYB S4-3 AU-07; M, N, adult with enclosed pedicle foramen, showing the successive growth of propareas at the posterior margin of the metamorphic shell (arrow), ELI-XYB S4-3 AU-01; O, enclosed pedicle foramen is mostly outside the metamorphic shell, ELI-XYB S4-3 AV-07.

Figure 4 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 4. Ornamentation and ultrastructure of the earliest ontogeny of Palaeotreta shannanensis gen. et sp. nov. from the Shuijingtuo Formation of southern Shaanxi. A, enlarged ventral apex, note pronounced halo (arrow) and drape structures (tailed arrow), ELI-XYB S4-3 AU-08; B, oblique lateral view of A, showing the pedicle foramen (arrow) and protegulum (tailed arrow); C, posterior view of ventral apex, show enclosed pedicle foramen outside of metamorphic shell, ELI-XYB S4-3 AU-01; D, enlarged dorsal apex, note pronounced halo (arrow) and drape structures (tailed arrow), ELI-XYB S4-3 AV-15 E, obliquely lateral view of D, showing the protegulum (tailed arrow) and two pairs of larval setal sacs (arrows); F, evenly distributed pitting structures on metamorphic shell, ELI-XYB S4-3 AU-13; G, enlargement of F; H, growth lines and drape structures on post-metamorphic shell, ELI-XYB S4-3 AU-13.

Figure 5 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 5. Internal morphology and ultrastructure of Palaeotreta shannanensis gen. et sp. nov. from the Shuijingtuo Formation of southern Shaanxi. A, enlarged ventral posterior end, showing cardinal muscle scars (arrows) and paired vascula lateralia (tailed arrows), ELI-XYB S4- 3 AU-07; B, oblique lateral view of A; C, enlarged ventral posterior end, ELI-XYB S4-3 AV-07; D, lateral view of dorsal posterior end, noting cardinal muscle scars by arrows, XYB S4-3 AV-18; E, enlarged dorsal pseudointerarea and median buttress, ELI-XYB S4-3 AU-12; F, enlargement of the terminal of median buttress of E, note weakly developed median septum; G, fine pores on the exterior of columnar lamella by exfoliation of the primary layer, ELI-XYB S4-3 AU-09; H, I, one layer of the secondary columnar structures on valve margin, ELI-XYB S4-3 AV-09, XYB S4-3 AU-07; J, K, remaining base of columns after exfoliation of the covering lamella of H; L, one lamella of very short secondary columns, ELI-XYB S4-3 AU-08; M, re-crystallization of the columns, note hollow in the centre, ELI-XYB S4-3 AV-13.

Figure 3 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 3. Ontogenetic development of dorsal valve of Palaeotreta shannanensis gen. et sp. nov. from the Shuijingtuo Formation of southern Shaanxi. A–D, juvenile with rudiment median buttress, ELI-XYB S4-3 AV-19; E–G, larger valve with developed median buttress, ELI-XYB S4-3 AU-12; G, oblique lateral view showing weakly developed median septum (arrow); H–K, adult valve with weakly developed median septum, ELI-XYB S4-3 AV-18.

Figure 1 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 1. Palaeogeographic map and fossil localities in southern Shaanxi and western Hubei, South China (modified from Z.-F. Zhang et al. 2016), noting the Xiaoyangba and Aijiahe sections.

Figure 2 in Ontogeny and evolutionary significance of a new acrotretide brachiopod genus from Cambrian Series 2 of South China

Figure 2. Ontogenetic development of ventral valve of Palaeotreta shannanensis gen. et sp. nov. from the Shuijingtuo Formation of southern Shaanxi. A–H, ventral valves demonstrating pedicle foramen forming stage (T1); A–D, juvenile with unrestricted pedicle notch, ELI-XYB S4-3 AU-06; E–H, small valve with pedicle opening, ELI-XYB S4s-3 AV-04; I–P, ventral valves demonstrating pedicle foramen-enclosing stage (T2); I–L, adult with enclosed pedicle foramen, ELI-XYB S4-3 AU-01; L, interior view, noting vascula lateralia (tailed arrow); M–P, larger valve with very short intertrough, ELI-XYB S4-3 AU-08.

opencc-by-4.0Apr 2024View details →

Time Series Analysis with Python datasets

<p>Datasets used in the course <a href="https://github.com/FilippoMB/python-time-series-handbook">Time Series Analysis with Python</a>.</p>

opencc-by-4.0Apr 2024View details →

Prospective case series of Standardbred racehorses treated with intra-lesional ultrasound-guided injection of platelet-rich plasma for suspensory ligament branch injuries

<p><strong>Abstract</strong> </p> <p>Standardbred racehorses are frequently diagnosed with suspensory ligament branch injuries which results in loss of earnings and morbidity. This prospective study aims to measure outcomes for suspensory ligament branch desmitis (SLBD) in Standardbred racehorses treated with intra-lesional platelet rich plasma (PRP).</p> <p> </p> <p>All Standardbred racehorses presented at the Vets On Wheels clinic in Marsa between 01.10.2019 and 31.12.2020 with grade 2-3 SLBD diagnosed ultrasonographically were included. All core lesions were injected with PRP under ultrasound guidance following standardised blood collection and centrifugal processes. All horses underwent a standardised rehabilitation protocol. Racing data including number of starts and earning following treatment was retrieved from the Malta Racing Club database.</p> <p> </p> <p>Twelve Standardbred racehorses were included in the study with a mean age of 9 years (range; 6 – 12).  Forelimb lesions accounted for 36% of injuries. Seven horses (58%) returned to racing, although only two (17%) started more than two races. The average time elapsed between treatment and date of first start was 200 days.</p> <p> </p> <p>To the authors knowledge this is the first study to examine SLBD outcomes following PRP treatment in Standardbred racehorses. Older racing Standardbreds with grade 2/3 SLBD have a guarded prognosis for return to racing, as documented within racing Thoroughbred populations. Whether or not intra-lesional PRP alters the prognosis cannot be ascertained from this study.</p>

opennotspecifiedOct 2022View details →

Fig. 6 in A phylogenetic investigation of the taxonomically problematic Eucalyptus odorata complex (E. section Adnataria series Subbuxeales): evidence for extensive interspecific gene flow and reticulate evolution

Fig. 6. Isolation by distance plot of core E. odorata complex samples (E. odorata, E. cajuputea, E. wimmerensis, E. walshii, E. yarriambiack, E. filiformis, E. polybractea and E. viridis from south-eastern Queensland). Geographic distances are kilometres between collection coordinates and genetic distances are uncorrelated-P distances.