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13,064 results for “predictions”
Figure 1 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 1. Graphic representation of the method for calculating mechanical advantage of each muscle group using the skull of Ta. wellnhoferi. This image shows only its temporal musculature, taken as an example, in (A) right lateral view and (B) palatal view. Green line: inlever. Red line: outlever. Yellow lines: lines of action in each represented plane. Pink area: two-dimensional representations of muscle extension in lateral view (A) and the palatal plane (B). The latter is equivalent to the CSA.
Figure 20 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 20. Absolute bite force and bite force quotients of the analysed pterodactyloids. A, comparison of the absolute values of anterior bite force, given in Newtons (N). B, comparison of bite force quotients. Skulls not to scale.
Figure 3 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 3. Upper temporal regions of Thalassodromeus sethi holotype (DGM 1476-R) in (A) dorsal and (B) right lateral view; and (C) of Tapejara wellnhoferi (SMNK 1137) in right lateral view. All scale bars = 20 mm. Abbreviations: f, frontal; fpcr, frontoparietal crest; or, orbit; utf, upper temporal fenestra.
Figure 13 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 13. Left mandibular ramus of Pteranodon sp. (A) YPM 2487, (B) LACM 51130 and (C) YPM 2401, in medial view. Arrowheads indicate muscle scars. Scale bars = 10 mm. Abbreviations: adfo, adductor fossa; an, angular; glfo, glenoid fossa; rapr, retroarticular process.
Figure 17 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 17. Left mandibular rami in lateral view. A, Thalassodromeus sethi holotype (DGM 1476-R). B, Anhanguera araripensis holotype (BSPG 1982 89). C, Ludodactylus sibbicki holotype (SMNK PAL 3828). Arrowhead indicates muscle scars. Scale bars = 20 mm. Abbreviations: sanfo, surangular fossa.
Figure 2 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 2. Jaw muscle reconstruction of the analysed taxa, with skulls in lateral and palatal views. These reconstructions are based on the present work and were used as the basis for calculating CSA, α, β, inlever and outlever. Pink represents the temporal musculature; green, M. adductor mandibulae posterior; and blue, the pterygoideus musculature. A, B, Caupedactylus ybaka (based on Kellner, 2013). C, D, Tapejara wellnhoferi (based on Kellner, 1989; 2004). E, F, Dsungaripterus weii (based on Young, 1984). G, H, Thalassodromeus sethi (based on Kellner & Campos, 2002; Pêgas et al., 2018). I, J, Tupuxuara leonardii (based on Kellner, 2004; Pinheiro & Schultz, 2012). K, L, Tropeognathus mesembrinus (based on Wellnhofer, 1987). M, N, Anhanguera blittersdorffi (based on Kellner & Tomida, 2000). O, P, Pteranodon longiceps (based on Bennett, 2001). Q, R, Nyctosaurus gracilis (based on Williston, 1902; Bennett, 2003). All scale bars = 200 mm.
Figure 14 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 14. Left mandibular ramus of (A) Pteranodon sp. YPM 1179, (B) Pteranodon longiceps YPM 2594 and (C) YPM 2594 in detail in medial view. Arrowheads indicate muscle scars. Scale bars = 10 mm. Abbreviations: adfo, adductor fossa; glfo, glenoid fossa; rapr, retroarticular process.
Figure 11 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 11. Mandibular rami of anhanguerids in medial view: Anhanguera araripensis holotype (BSPG 1982 89) (A) left ramus and (B) right ramus; and (C) Tropeognathus mesembrinus holotype (BSPG 1987 46) left ramus. Arrowheads indicate muscle scars. Scale bars = 20 mm. Abbreviations: adfo, adductor fossa; ang, angular; glfo, glenoid fossa; part, prearticular; rapr, retroarticular process; san, surangular.
Figure 9. A in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 9. A, right mandibular ramus of Aymberedactylus cearensis holotype (MN 7596-V) in ventral view. B, left mandibular ramus of Aymberedactylus cearensis holotype in ventral view and (C) medial view. Arrowheads indicate muscle scars. Scale bars = 20 mm. Abbreviations: adfo, adductor fossa; ang, angular; art, articular; d, dentary; rapr, retroarticular process; san, surangular.
Figure 16 in Reconstruction of the adductor chamber and predicted bite force in pterodactyloids (Pterosauria)
Figure 16. Mandibular rami in dorsal view. A, Aymberedactylus cearensis holotype (MN 7596-V), right ramus. B, Thalassodromeus sethi holotype (DGM 1476-R), left ramus. C, Anhanguera araripensis holotype (BSPG 1982 89), left ramus. D, Pteranodon sp. YPM 2410, left ramus. E, Pteranodon sp. LACM 51130, left ramus. Arrowheads indicate muscle scars. Scale bars = 10 mm. Abbreviations: adfo, adductor fossa; defo, fossa depressoria; lc, lateral cotyle; mc, medial cotyle; rapr, retroarticular process.
Modeling pulsed evolution and time-independent variation improves the confidence level of ancestral and hidden state predictions
<p><span><span><span><span><span><span><span><span><span><span>Ancestral state reconstruction is not only a fundamental tool for studying trait evolution, but also very useful for predicting the unknown trait values (hidden states) of extant species. A well-known problem in ancestral and hidden state predictions is that the uncertainty associated with predictions can be so large that predictions themselves are of little use. Therefore, for meaningful interpretation of predicted traits and hypothesis testing, it is prudent to accurately assess the uncertainty of the predictions. Commonly used constant-rate Brownian motion (BM) model fails to capture the complexity of tempo and mode of trait evolution in nature, making predictions under the BM model vulnerable to lack-of-fit errors from model misspecification. Using empirical data (mammalian body size and bacterial genome size), we show that the distribution of residual Z-scores under the BM model is neither homoscedastic nor normal as expected. Consequently, the 95% confidence intervals (CIs) of predicted traits are so unreliable that the actual coverage probability ranges from 33% (strongly permissive) to 100% (strongly conservative). Alternative methods such as BayesTraits and StableTraits that allow variable rates in evolution improve the predictions but are computationally expensive. Here we develop RasperGade, a method of ancestral and hidden state prediction that uses the Levy process to explicitly model gradual evolution, pulsed evolution and time-independent variation. Using the same empirical data, we show that RasperGade outperforms both BayesTraits and StableTraits and is orders-of-magnitude faster. Our results suggest that, when predicting the ancestral and hidden states of continuous traits, the tempo and mode of evolution should always be assessed and the quality of confidence estimates should always be examined.</span></span></span></span></span></span></span></span></span></span></p>
Is the age of plant communities predicted by the age, stability and soil composition of the underlying landscapes? An investigation of OCBILs
<p></p><p>Old, climatically buffered, infertile landscapes (OCBILs) have been hypothesized to harbour an elevated number of persistent plant lineages and are predicted to occur across different parts of the globe, interspersed with other types of landscapes. We tested whether the mean age of a plant community is associated with occurrence on OCBILs, as predicted by climatic stability and poor soil environments. Using digitized occurrence data for seed plants occurring in Australia (7033 species), sub-Saharan Africa (3990 species) and South America (44 482 species), regions that comprise commonly investigated OCBILs (Southwestern Australian Floristic Region, Greater Cape Floristic Region and campos rupestres), and phylogenies pruned to match the species occurrences, we tested for associations between environmental data (current climate, soil composition, elevation and climatic stability) and two novel metrics developed here that capture the age of a community (mean tip length and mean node height). Our results indicate that plant community ages are influenced by a combination of multiple environmental predictors that vary globally; we did not find statistically strong associations between the environments of OCBIL areas and community age, in contrast to the prediction for these landscapes. The Cape Floristic Region was the only OCBIL that showed a significant, although not strong, overlap with old communities.</p><p></p>
Results on the Pre-Pazy Wing Model for the 3rd Aeroelastic Prediction Workshop
<p>Add SHARPy deformed flutter data for angles of attack 6 and 7 degrees</p>
Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors
<p>Data for Hollon et al. (2021) Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors. <em>Current Biology</em></p>
Predicting parameters for the Quantum Approximate OptimizationAlgorithm for MAX-CUT from the infinite-size limit
<p>This is the raw data used to generate the figures of the paper. The code generating the figures is in a separate GitHub repository: https://github.com/sami-b95/predicting_qaoa_parameters_data</p>
Predictive model of transcriptional elongation control identifies trans regulatory factors from chromatin signatures
<p>Supplementary data for "Predictive model of transcriptional elongation control identifies trans regulatory factors from chromatin signatures" by Toray S. Akcan, Matthias Heinig.</p>
Genes with transcripts predicted to be miR-195 or miR-26b targets by all 5 predictive algorithms included in starBase, or experimentally identified as targets by pulldown assay
<p>Genes with transcripts predicted to be miR-195 or miR-26b targets by all 5 predictive algorithms included in starBase, or experimentally identified as targets by pulldown assay</p>
Dataset: A model of tension-induced fiber growth predicts white matter organization during brain folding
<p>This dataset contains models, MRI data, and code associated with "A model of tension-induced fiber growth predicts white matter organization during brain folding" in <em>Nature Communications </em>(accepted 27 Oct 2021).</p> <p>Included files:</p> <ol> <li>NatCommMRIdata.zip - MRI data and associated MATLAB code for analysis of each subject</li> <li>folding_with_fibers.mph - model file used to generate results in Main Text. Modifiable parameters include stress-dependent fiber elongation rate, initial fiber volume fractions, initial geometry, and cortical growth rate. Compatible with COMSOL Multiphysics (version 5.3).</li> <li>folding_with_fibersECM.mph - model file corresponding to Supplementary Fig. 2. Compatible with COMSOL Multiphysics (version 5.3).</li> <li>folding_with_fibers_linear.mph - model file corresponding to Supplementary Fig. 3. Compatible with COMSOL Multiphysics (version 5.3).</li> </ol> <ol> </ol>
The dataset used in "A max-margin model for predicting residue-base contacts in protein-RNA interactions"
<p>The dataset used in "A max-margin model for predicting residue-base contacts in protein-RNA interactions"</p>
Data set for Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services' trajectories
<p>Data set for the paper: Predicting hospital occupancy for covid-19 patients: a simulation approach based on archetypes of empirical services’ trajectories</p> <p>Based on: Marin-Garcia, J. A., Ruiz, A., Julien, M., & Garcia-Sabater, J. P. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management, 12(1), 76-115. https://doi.org/10.4995/wpom.15332</p> <p> </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.