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942 results for “Scenarios”
Figure 4 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 4. Plumage distribution and geographic variation in the Synallaxis rutilans group. Dark grey circles, specimens with grey-patterned plumage; pale grey circles, specimens with olive-patterned plumage; black circles, specimens with rufous-patterned plumage. Concerning intermediate individuals, any specimen with rufous present on the upperparts was classified in the 'specimens with rufous-patterned plumage' group. *Illustrations of birds with the typical plumage of: S. caquetensis (on the left); S. dissors (centre above); S. omissa (right). Area of endemism (AE) and distribution of species of the Synallaxis rutilans group: purple, Guiana EA, S. dissors; red, Napo EA, S. caquetensis; orange, Xingu EA, S. rutilans; yellow, Belém EA, S. omissa; and green, Inambari EA, blue, Rondônia EA and turquoise, Tapajós EA, S. amazonica. *Illustrations from del Hoyo J, Elliott A, Sargatal J, Christie DA, de Juana E, eds. 2017. Handbook of the birds of the world alive. Barcelona: Lynx Edicions (retrieved on 10.11.2017 from http://www.hbw.com).
Figure 1 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 1. Map showing the distribution of sequenced individuals, phylogenetic time tree and plumage analyses for the Synallaxis rutilans group. The areas of endemism recognized by Silva et al. (2005) are highlighted on the map, and distribution points are numbered in accordance with individuals in the tree. The phylogenetic time tree is based on 1539 bp of concatenated ND2 and COI genes. Posterior probability values and the 95% HPD are indicated at each node. Thr, throat; Rec, rectrices; For, forehead; Sup, supercilium; Fac, face; Win, wing-coverts; Rem, remiges; 1st, first colour (main colour); 2nd, second colour (variation ±); 36 (e.g.), colours in Smithe's catalogue; *, specimen damaged or immature; **, specimen analysed without Smithe's catalogue; MPEG A, spirit collection specimen; dark grey circles in map and patches in table, specimens with grey plumage pattern; light grey circles and patches, specimens with olive plumage pattern; black circles and patches, specimens with rufous plumage pattern; purple star, type locality of S. r. dissors; red star, type locality of S. r. caquetensis; red star with white spot, type locality of S. r. confinis; blue star, type locality of S. r. amazonica; blue star with a white spot, type locality of S. r. tertia; orange star, type locality of S. r. rutilans; yellow star, type locality of S. r. omissa.
Figure 5 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 5. Specimens of the Synallaxis rutilans group showing the grey (left-hand bird in each image) and olive patterns in juvenile plumage, from left to right, ventral, lateral and dorsal views: MZUSP 44653, Capim, Pará, Brazil; and MZUSP 93965, Boa Vista, Roraima, Brazil.
Figure 2 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 2. Scatterplots illustrating the results of the PCA for the morphometric data pertaining to populations of the Synallaxis rutilans group as clades resulting from the cladistic analysis performed using mtDNA.
Figure 3 in Taxonomic challenges posed by discordant evolutionary scenarios supported by molecular and morphological data in the Amazonian Synallaxis rutilans group (Aves: Furnariidae)
Figure 3. Specimens of the Synallaxis rutilans group illustrating the patterns grey, olive, and rufous in plumage from left to right: ventral, lateral and dorsal views: MPEG 38618, Alto Turiaçu, Maranhão, Brazil; MPEG 59487, Barcelos, Amazonas, Brazil; and MPEG 56645, Juruti, Pará, Brazil.
Data from: Is there a benefit for anesthesiologists of adding difficult airway scenarios for learning fiberoptic intubation skills using virtual reality training? A randomized controlled study
<p><strong><span>Introduction</span></strong><span><strong>:</strong> Fiberoptic intubation for a difficult airway requires significant experience. Traditionally only normal airways were available for high fidelity bronchoscopy simulators. It is not clear if training on difficult airways offers an advantage over training on normal airways. This study investigates the added value of difficult airway scenarios during virtual reality fiberoptic intubation training.</span></p> <p><span><strong>Methods:</strong> </span><span>A prospective multicentric randomized study was conducted 2019 to 2020, among 86 inexperienced anesthesia residents, fellows and staff. Two groups were compared: Group N (control, n=43) first trained on a normal airway and Group D (n=43) first trained on a normal, followed by three difficult airways. All were then tested by comparing their Global Rating Scores (GRS) on 5 scenarios (1 normal and 4 difficult airways).</span></p> <p><span><strong>Results:</strong> </span><span>The final evaluation GRS score for the normal airway testing scenario was significantly higher for group N than group D: median score 76% (IQR 56.5 - 90) versus 58% (IQR 51.5 - 69, p = 0.0039), but there was no difference in GRS scores for the difficult intubation testing scenarios. </span></p> <p><span><strong>Conclusions:</strong> </span><span>A single exposure to each of 3 different difficult airway scenarios did not lead to better fiberoptic intubation skills on previously unseen difficult airways, when compared to multiple exposures to a normal airway scenario. This finding may be due to the learning curve of approximately 5-10 exposures to a specific airway scenario required to reach proficiency. </span></p>
Smart Energy Europe scenarios
<p>Scenarios for Smart Energy Europe, PRIMES 1.5 TECH and 2050 Baseline.</p> <p>Includes scenario files and distribution time series. Requires EnergyPLAN to run the models.</p>
Responses of Surface Evaporative Fluxes in Montane Cloud Forests to the Climate Change Scenario
<p>CL_surfobs_raw.mat and LHC_nodew_new_raw.mat are the analyzed CLM simulation output with atmospheric observations in Chi-Lan and Lien-Hua-Chih as input forcings.</p> <p>CLatm_LHC_prec*.mat are the analyzed CLM simulation output in sensitivity tests for the rainfall pattern.</p> <p>CL_*_transform_raw.mat are the analyzed CLM simulation output in CTL simulation and climate change sensitivity tests. </p> <p>Fig_*.m are matlab code files to reproduce figures in the article.</p> <p>Fig_11_ttest.m and Fig_11_LE_p_value.mat are the t-test for the decrease of latent heat flux in the diurnal cycle under climate change scenario and the resulting p-value.</p> <p>Fig_quantile.m and Fig_4_6_7_8_9_10_11_quantile.mat are to obtain first and third quantile of the changes of canopy water and surface heat fluxes in the diurnal cycle under climate change scenarios.</p>
Dataset - modelled CO2 emissions from tropical peat-draining rivers and coastal waters based on enhanced weathering scenarios.
<p>Dataset related to the manuscript "Destabilization of carbon in tropical peatlands by enhanced weathering" (DOI: <a href="https://doi.org/10.1038/s43247-022-00544-0">10.1038/s43247-022-00544-0</a>).</p> <p>Model runs for enhanced leaching of dissolved inorganic carbon (DIC) and of dissolved organic carbon (DOC) were conducted.</p> <p>Results include in-river carbon dioxide (CO2), DIC, DOC, oxygen (O2) and pH as well as CO2 emissions from rivers and from coastal waters.</p> <p>Main results are in "River_And_Coastal_Response_To_Enhanced_Weathering.xlsx".<br> Results for uncertainty study are in "River_And_Coastal_Response_To_Enhanced_Weathering_Uncertainty_Scenarios.xlsx".</p>
Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation
<p>Documentation to reproduce in silico analyses related to the manuscript<br> <strong>Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure</strong></p> <p>by</p> <p>Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih</p> <p>Published in<br> Nature Communications (doi: 10.1038/s41467-022-33464-w)</p> <p>All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426). </p> <p>For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366).</p> <p><br> <strong>Simulation results:</strong></p> <p>All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: <em>verticesDict.pkl</em> and <em>edgesDict.pkl</em>containing all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder <em>Baseline</em> contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder <em>Dilated</em> contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders <em>dc_x.x</em> contain the simulation results for the different diameter changes. Note that, folder <em>dc_0.0</em> contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: <em>toDilate</em> and <em>base_capillary</em>). </p> <p> </p> <p><strong>Reproducing figure 8:</strong></p> <p>Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0).<br> Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure.<br> Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below). </p> <p><br> Output: All created Figures are saved in the folder <em>Figures</em>. The associated source data is available in Excel format in the folder <em>SourceData</em>.</p> <p> </p> <p><strong>Edge attributes:</strong></p> <p>diameter: vessel diameter [µm]<br> mainAV: 1 if ascending venule main branch, 0 otherwise<br> connectivity: vertex tuple to define location of edge<br> flow: flow rate [µm<sup>3</sup>/ms]<br> mainDA: 1 if descending arteriole main branch, 0 otherwise<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> htt: tube hematocrit [-]<br> toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise<br> base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise</p> <p> </p> <p><strong>Vertex attributes:</strong></p> <p>index: vertex index<br> pressure: pressure [mmHg]<br> nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary<br> coords: vertex coordinates x,y,z [µm]<br> pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes</p> <p> </p> <p><strong>Obtaining simulation results:</strong><br> General:</p> <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> <p>Specific for current application:</p> <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> <p>Demo:</p> <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (<em>G_averaged.pkl</em>)</li> </ul>
Dataset for Assessing the mycotoxin-related health impact of shifting from meat-based diets to soy-based meat analogues in a model scenario based on Italian consumption data
<p>Dataset used for <strong>Assessing the mycotoxin-related health impact of shifting from meat-based diets to soy-based meat analogues in a model scenario based on Italian consumption data.</strong></p>
FIGURE 20 in DNA barcodes reveal different speciation scenarios in the four North American Anthocharis Boisduval, Rambur, [Duménil] & Graslin, [1833] (Lepidoptera: Pieridae: Pierinae: Anthocharidini) species groups
FIGURE 20. Haplotype tree for North American Anthocharis. Sara group (Green—A. julia, Blue—A. sara, Red—A. thoosa), lanceolata group (Brown—nominotypical lanceolata, Orange—A. desertolimbus), cethura group (Red—A. cethura morrisoni, Orange—A. cethura pima and A. cethura catalina), midea group (Green—A. limonea and A. midea), Black—A. scolymus [outgroup].
FIGURE 27 in DNA barcodes reveal different speciation scenarios in the four North American Anthocharis Boisduval, Rambur, [Duménil] & Graslin, [1833] (Lepidoptera: Pieridae: Pierinae: Anthocharidini) species groups
FIGURE 27. Distribution of sampled individuals in the midea group. Anthocharis midea—upside down green triangles, Anthocharis limonea—pale blue triangles, erect green triangle—Anthocharis limonea phenotype with Anthocharis midea haplotype.
FIGURE 23 in DNA barcodes reveal different speciation scenarios in the four North American Anthocharis Boisduval, Rambur, [Duménil] & Graslin, [1833] (Lepidoptera: Pieridae: Pierinae: Anthocharidini) species groups
FIGURE 23. Distribution of Anthocharis sara group. Small red-filled circles—type localities of various names. Green-filled circles—Anthocharis julia, Blue-filled triangles—Anthocharis sara, Red-filled squares—Anthocharis thoosa.
FIGURE 21 in DNA barcodes reveal different speciation scenarios in the four North American Anthocharis Boisduval, Rambur, [Duménil] & Graslin, [1833] (Lepidoptera: Pieridae: Pierinae: Anthocharidini) species groups
FIGURE 21. Distribution of Anthocharis lanceolata group, six-pointed stars—nominotypical lanceolata, yellow-filled circle—southern Sierra Nevada lanceolata and subspecies australis, orange-filled circles—desertolimbus and San Pedro Martir population.
FIGURES 17–19 in DNA barcodes reveal different speciation scenarios in the four North American Anthocharis Boisduval, Rambur, [Duménil] & Graslin, [1833] (Lepidoptera: Pieridae: Pierinae: Anthocharidini) species groups
FIGURES 17–19. Fifth instar larval phenotypes of Anthocharis sara group species—17. A. sara, 18. A. thoosa, and 19. A. julia.
FIGURES 1–16 in DNA barcodes reveal different speciation scenarios in the four North American Anthocharis Boisduval, Rambur, [Duménil] & Graslin, [1833] (Lepidoptera: Pieridae: Pierinae: Anthocharidini) species groups
FIGURES 1–16. Adults of North American Anthocharis species. Male D, V, Female D, V—1–2 A. lanceolata, 3–4 A. desertolimbus, 5–6 A. sara, 7–8 A. julia, 9–10 A. thoosa, 11–12 A. cethura, 13–14 A. midea, 15–16 A. limonea.
Fig. 3. Scenario 9 in Inferring Ancestry and Divergence Events in a Forest Pest Using Low-Density Single-Nucleotide Polymorphisms
Fig. 3. Scenario 9 was identified as the 'best' from 11 competing phylogeographic scenarios. This scenario represents an east-to-west colonization route with stable population sizes in which cluster 4 is derived from clusters 1 and 3 through admixture. Terminal branch labels are consistent with the five STRUCTURE clusters identified (Fig. 1). T = time expressed as number of generations assuming one generation per year; R = inferred migration rate.
Purkinje cell responses during diverse Granule cell plasticity scenarios
<p>Purkinje cell responses during diverse Granule cell plasticity scenarios</p>
Supplementary material 1 from: Jarnevich CS, Young NE, Sheffels TR, Carter J, Sytsma MD, Talbert C (2017) Evaluating simplistic methods to understand current distributions and forecast distribution changes under climate change scenarios: an example with coypu (Myocastor coypus). NeoBiota 32: 107-125. https://doi.org/10.3897/neobiota.32.8884
Supplementary figures and table : Explanation note: Supporting information including global location data used to create models (Supplementary figure 1), global distribution of tropical environments (Supplementary figure 2), and Global circulation model climate data used for forecasts of Myocastor coypus distributions (Supplementary table 1).
ScienceDex guides
Understand access before you commit
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.