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9 results for “Shape Matching”
Data from: Global plant-frugivore trait matching is shaped by climate and biogeographic history
<p>Species interactions are influenced by the trait structure of local multi-trophic communities. However, it remains unclear whether mutualistic interactions, in particular, can drive trait patterns at the global scale, where climatic constraints and biogeographic processes gain importance. Here we evaluate global relationships between traits of frugivorous birds and palms (<em>Arecaceae</em>), and how these relationships are affected, directly or indirectly, by assemblage richness, climate and biogeographic history. We leverage a new and expanded gape size dataset for nearly all avian frugivores, and find a positive relationship between gape size and fruit size, that is, trait matching, which is influenced indirectly by palm richness and climate. We also uncover a latitudinal gradient in trait matching strength, which increases towards the tropics and varies among zoogeographic realms. Taken together, our results suggest trophic interactions have consistent influences on trait structure, but that abiotic, biogeographic and richness effects also play important, though sometimes indirect, roles in shaping the functional biogeography of mutualisms.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 2. New neuronal model, based on matching a wave-like representation of the output.
<p>For the new model, shown in Figure 2, the ‘differences’ between the outputs is measured and combined, to produce a kind of wave-like shape. If output point 1 has a value of 10, and output point 2 has a value of 5, for example, then this results in the shape moving down 5 points on the wave shape. This shape will, of course, change depending on the order that the dataset values are presented in. Also, if new data is presented, then that will produce a different shape. However, there is still an association between the input values and the output values and it is still this relation that is being learnt. The neural network needs to be able to learn a function that can generalise over the presented datasets, so that it can recognise the relation in previously unseen data as well. Generalising over trying to learn a wave-like shape, or trying to match the output values exactly looks quite similar, suggesting that the process is at least valid. In Figure 2, note that a transposition to move the learned shape up or down first is possible, before a weight value would try to scale it. If the initial match of the combined inputs can be as close as possible, then these adjustments will become less and will be more for fine tuning.</p>
Data from: Pre‐adaptation and adaptation shape trait‐environment matching in the Neotropics
<p>The resources provided here comprise raw and final data sets used in our study 'Preadaptation and adaptation shape trait-environment matching in the Neotropics'<span> (DOI: 10.1111/geb</span>.<span>13730) comprising curated g</span>eographic occurrences for 156 Swartzia taxa across its distribution in the Neotropics, phylogenetic information for 69 Swartzia taxa, and corresponding climatic (i.e. 19 climatic variables taken from WorldClim), soil (<span>i.e. </span>sand, nitrogen, and clay content taken from the SoilGrids 2.0 portal), and trait data <span>for each taxa</span> (i.e. <span>trait measurements for </span>lefleats, petals, and fruits)<span>. We also provide t</span>he script used to extract trait information from botanical monographs<span>.</span></p>
Data from: Global plant-frugivore trait matching is shaped by climate and biogeographic history
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Data from: Pre‐adaptation and adaptation shape trait‐environment matching in the Neotropics
Open the record for dataset details and reuse information.
Homology judgements of pre-Darwinian naturalists explained by general human shape matching abilities
<p>Dataset relative to the following publication:</p> <p>Stegmann, U., & Schmidt, F. (2023). Homology judgements of pre-evolutionary naturalists explained by general human shape matching abilities. <em>Scientific Reports, 13</em>, 12269.</p> <p>Each experiment folder contains the data relative to one experiment, together with a text file with comments.</p>
The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms
<p>Data used in the "The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms"</p>
METHODS. Bovine ilia were used in the simulations because their histological structure (a fibrolamellar cortex overlying cancellous bone26) was found to match that of the Triceratops ilium. Bone sections 10 x 50 x 縠 3.0 cm with cortices ranging from 0.5 to 5.5 mm in depth (the range of initial cortical-thickness estimates based on gross morphology) were mounted on a servohydraulic mechanical loading frame (MTS Bionix, Minneapolis) and penetrated with an aluminium-bronze T. rex tooth replica. The replica was cast from an actual adult T. rex maxillary tooth, after casts made from some ofthe deeper bite marks revealed the size and shape of the teeth that had impacted the pelvis8 • The replica was penetrated into the ilia sections at 1 mm s-1 to a depth of 11.5 mm, equivalent to the maximum depth of the deepest ilium bite mark8 • Forces were measured with an MTS 25 N strain-gauge-based axial load cell accurate to 0.2%. The forces increased with increasing penetration depth even after the cortical layer had been perforated and the underlying cancellous bone was being crushed. The increase in force with penetration depth is attributed to a greater cortical surface area coming into contact with the semi-conical penetrator tooth as it descended through the ilia. in Bite-force estimation for Tyrannosaurus rex from tooth-marked bones
METHODS. Bovine ilia were used in the simulations because their histological structure (a fibrolamellar cortex overlying cancellous bone26) was found to match that of the Triceratops ilium. Bone sections 10 x 50 x 縠 3.0 cm with cortices ranging from 0.5 to 5.5 mm in depth (the range of initial cortical-thickness estimates based on gross morphology) were mounted on a servohydraulic mechanical loading frame (MTS Bionix, Minneapolis) and penetrated with an aluminium-bronze T. rex tooth replica. The replica was cast from an actual adult T. rex maxillary tooth, after casts made from some ofthe deeper bite marks revealed the size and shape of the teeth that had impacted the pelvis8 • The replica was penetrated into the ilia sections at 1 mm s-1 to a depth of 11.5 mm, equivalent to the maximum depth of the deepest ilium bite mark8 • Forces were measured with an MTS 25 N strain-gauge-based axial load cell accurate to 0.2%. The forces increased with increasing penetration depth even after the cortical layer had been perforated and the underlying cancellous bone was being crushed. The increase in force with penetration depth is attributed to a greater cortical surface area coming into contact with the semi-conical penetrator tooth as it descended through the ilia.
Mir-132/212 shapes age-dependent transcriptome and is required for binocular matching of orientation preference and depth perception.
GEO Series GSE95649. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.