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226 results for “growth model”
Figure 5 in Growth patterns, sexual dimorphism, and maturation modeled in Pachypleurosauria from Middle Triassic of central Europe (Diapsida: Sauropterygia)
Figure 5. Comparison of humerus length at birth (Lbirth), asymptotic length (AL), age at which sexual maturity is reached (ASM), and onset of maturation for pachypleurosaurs with a modeled growth record. Onset of maturation within life is estimated as ratio of the age at which sexual maturity is reached and asymptotic age (ASM / AA). It is also assessed as ratio of the age at which sexual maturity is reached and age at death (ASM / AD). White = Lbirth, black = AL, blue = ASM, red = ASM / AA, and brown = ASM / AD. High within-taxon variability in traits could suggest a sexual dimorphism in size and maturation in pachypleurosaur taxa. For values of life-history traits of specimens refer to Table 2, and for ratios to Table 3.
Figure 4 in Growth patterns, sexual dimorphism, and maturation modeled in Pachypleurosauria from Middle Triassic of central Europe (Diapsida: Sauropterygia)
Figure 4. Allometric comparison of different life-history traits of pachypleurosaurs and Simosaurus to extant reptiles. (a) Mass at birth vs. body mass, (b) age at which sexual maturity is reached vs. body mass, (c) longevity vs. body mass, and (d) maximum growth rates vs. body mass. In all panels black triangles mark extant reptile species, red symbols pachypleurosaurs, and black crosses the nothosaur genus Simosaurus (values taken from Klein and Griebeler, 2016). Red squares = Dactylosaurus, circles = Anarosaurus, triangles = aff. N. pusillus, triangle with cross = N. pusillus, asterisk = N. edwardsii, and diamond = Serpianosaurus. Ordinary least squares regression lines and 95 % prediction intervals are shown for extant species. Varanus niloticus (grey triangle) is highlighted because it is only somewhat larger than the pachypleurosaurs studied here. Data on body mass, mass at birth (N = 782), age at which sexual maturity is reached (N = 411), and longevity (N = 1014) of extant squamates are compiled from Scharf et al. (2015). Data on body mass and maximum growth rate of reptiles (squamates, crocodiles, and turtles, N = 66) are taken from Werner and Griebeler (2014). Masses at birth of pachypleurosaurs (and Simosaurus) are larger than expected from the 95 % prediction interval for a similar-sized squamate, whereas pachypleurosaurs longevities and maximum growth rates (including that of Simosaurus) almost fit within the respective intervals. The majority of pachypleurosaurs reach sexual maturity earlier than expected for a similar-sized squamate. Overall, pachypleurosaurs (and Simosaurus) have a considerably higher mass at birth and they clearly mature earlier than a similar-sized squamate.
Figure 3 in Growth patterns, sexual dimorphism, and maturation modeled in Pachypleurosauria from Middle Triassic of central Europe (Diapsida: Sauropterygia)
Figure 3. Growth record and established growth models for pachypleurosaurs. The statistically best growth models are shown for each specimen. These have the highest Akaike weights (Burnham and Anderson, 2002) compared to the others which were also applicable to the growth record of the specific specimen (see Table S1). Specimens are marked by colors. Growth curves on the same specimen are marked by different line types (solid, dotted) in equal color. Parameter values of models and fitting statistics are summarized in Table S1. Neusticosaurus pusillus specimens SMNS 92125 and SMNS 50372c are from the Germanic Basin (aff. N. pusillus), and specimens PIMUZ T 4178 and PIMUZ T 4211 are from the Alpine Triassic.
Figure 2 in Growth patterns, sexual dimorphism, and maturation modeled in Pachypleurosauria from Middle Triassic of central Europe (Diapsida: Sauropterygia)
Figure 2. Growth record in Dactylosaurus from the Germanic Basin (Lower Muschelkalk, early Anisian), in aff. N. pusillus from the Germanic Basin (Lower Keuper, late Ladinian) and in Neusticosaurus spp. and in Serpianosaurus from the Alpine Triassic (Anisian/Ladinian). (a) aff. N. pusillus SMNS 92125. (b) N. pusillus PIMUZ T 4211. (c) aff. N. pusillus SMNS 50372c. (d) Dactylosaurus MB.R.786. (e) Dactylosaurus MB.R. 776.2. (f) N. edwardsii PIMUZ T4758. (g) Serpianosaurus PIMUZ T 4510. (h) Wijk 09-472. Abbreviations: sc, subcycles; sm, sexual maturity. Panels (a, b, d, e) are in normal light, (c, h) are in polarized light, and (f, g) are in polarized light with gypsum filter (lambda). Scale bar is 0.5 mm.
Figure 1 in Growth patterns, sexual dimorphism, and maturation modeled in Pachypleurosauria from Middle Triassic of central Europe (Diapsida: Sauropterygia)
Figure 1. Details of medulla, bone tissue, and vascularization of Dactylosaurus from the early Anisian (Lower Muschelkalk; Germanic Basin) and aff. N. pusillus from the late Ladinian (Lower Keuper; Germanic Basin). (a) Medullary region distally to midshaft in Dactylosaurus humerus MB.R. 801.2. consisting of small round erosion cavities surrounded by endosteal bone and embedded in a matrix of calcified cartilage. The medullary region is surrounded by a sharp line (arrow). (b) Medullary region closer to midshaft in Dactylosaurus humerus MB.R. 771.5 displaying a small free cavity, a few small erosion cavities surrounded by endosteal bone and calcified cartilage at the border to the periosteal region all encompassed by a sharp line (arrow). Around the medullary cavity slow-deposited (i.e., highly organized) hatchling bone tissue is visible. (c) The medullary region and inner cortex in aff. N. pusillus humerus SMNS 50372b is nearly completely filled by endosteal bone. The area is surrounded by the sharp line (arrow), although the sample was taken nearly at the midshaft. Scattered longitudinal primary osteons occur in this sample. (d) Cross section of aff. N. pusillus humerus SMNS 58025a which shows an irregular medullary region and remodeling in form of erosion cavities scattered into the periosteal bone. (e) Medullary region and inner cortex of aff. N. pusillus humerus SMNS 50372c. The medullary region consists of few small erosion cavities and endosteal bone. The innermost cortex is made of fast-deposited hatchling bone tissue, which is surrounded by a distinct annulus. (f) Medullary region and inner cortex of aff. N. pusillus humerus SMNS 92125. The medullary region consists of a small cavity surrounded by a thick layer of endosteal bone, which are encompassed by a sharp line and calcified cartilage. The innermost cortex is made of a slow-deposited hatchling bone tissue. (g) Cross section of N. pusillus humerus PIMUZ T 3975. The medullary region is completely filled by endosteal bone. The area is surrounded by some erosion cavities. (h) Medullary region and inner cortex at midshaft in Dactylosaurus humerus MB.R. 776.2 showing a free cavity surrounded by a thick layer of endosteal bone. On the right side are remains of preserved fast-deposited (i.e., less organized) hatchling bone tissue. On the right side, the layer of horizontally oriented fine fibers is visible (arrow). (i) Medullary region and inner cortex in Anarosaurus humerus Wijk 13-194. The relatively large, free medullary cavity is surrounded by a thin, and in this sample incomplete, layer of endosteal bone. The innermost cortex is made of a fast-deposited (i.e., highly organized) hatchling bone tissue, which is surrounded by a distinct annulus. A second annulus is clearly visible in the lower part of the picture. Distance between annuli changes considerably towards the preaxial bone side (arrows mark spilt). Abbreviations: cc, calcified cartilage; eb, endosteal bone; ec, erosion cavity; htb, hatchling bone tissue; ffho, fine fibers horizontally oriented; mc, medullary cavity; mr, medullary region; po, primary osteon. All pictures are in polarized light. Scale bar is 0.5 mm if not labeled otherwise.
Рис. 3. Изменение ΔΛины теΛа у Bufo sachalinensis с возрастом: A — самки; B — самцы Fig. 3. The von Bertalanffy growth models for Bufo sachalinensis: A — females; B — males in Age structure and sexual dimorphism of the Far Eastern toad, Bufo sachalinensis Nikolsky, 1905 in the Ussurisky Nature Reserve
Рис. 3. Изменение ΔΛины теΛа у Bufo sachalinensis с возрастом: A — самки; B — самцы Fig. 3. The von Bertalanffy growth models for Bufo sachalinensis: A — females; B — males
Artificial Intelligence and the Future of Smart Cities-Figure 2. Traditional growth model vs. adapted growth model Source: Adapted after Purdy & Daugherty, 2016
<p>The use of AI is not limited to smart buildings or transportation. It covers a wide range of application from medical diagnosis, to robot control and virtual assistance scientific tools. Nowadays, AI can be encountered in many services such as: cars speech recognitions, industrial robots, intelligent vacuum cleaners or fridges and so further. It can also be used in smart homes which permits by using hundreds or even thousands of sensors to provide services according to our preferences such as: ambient assisted living, energy saving etc. According to Skouby et al. (2014), AI also can be utilized in smart homes by adding personalized features in form of context awareness which allows AI to move beyond automation level. These authors designed a four-layer pyramid which encases the ICTs based infrastructure for future smart cites (Figure 3).</p>
Figure 4 in Estimation of individual growth of the violet oyster Chama coralloides Reeve, 1846 (Bivalvia: Venerida) using Schnute model cases
Figure 4. Fitted growth curves for the best cases of the Schnute model for a population of C. coralloides in Acapulco, Guerrero, Mexico (* indicates the best model).
Figure 2 in Estimation of individual growth of the violet oyster Chama coralloides Reeve, 1846 (Bivalvia: Venerida) using Schnute model cases
Figure 2. Monthly frequency distribution of lengths (bars) and modal groups (curves) for C. coralloides in Acapulco, Guerrero, Mexico.
Dataset 3 - Mathematical Modeling of Growth for Climbing Plants
<p>This dateset collects some models of climbing plants in the framework of the Task 3.4 of the Growbot project. In particular, it focuses on models describing the climbing plants' secondary growth, emphasizing such a behavior as an optimal way to allocate biomass and maximize climbing plants's reach.</p> <p>The models are described in the following preprints:</p> <table> <tbody> <tr> <td> <ol> <li><em>A 2D Model to describe the mechano-sensory behaviour of self-supporting shoots of climbing plants against gravity </em>(2023), G. Vecchiato; T. Hattermann; M. Palladino; P. Heuret; N. P. Rowe; P. Marcati, <strong>submitted preprint</strong></li> <li><em>Searcher-Shoot: a Reinforcement Learning approach to understand climbing plant behaviour</em> (2023), L. Nasti; G. Vecchiato; T. Hattermann; P. Heuret; N. P. Rowe; M. Palladino; P. Marcati, <strong>preprint</strong></li> <li><em>An optimal control approach to the problem of the longest self-supporting structure</em> (2023), G. Vecchiato; M. Palladino; P. Marcati, <strong>submitted preprint</strong></li> <li><em>Modeling intertwining of growing shoots</em> (2023), O. Giannopoulou; G. Vecchiato; M. Palladino; M. Thielen; T. Speck; P. Marcati, <strong>preprint</strong></li> </ol> </td> </tr> </tbody> </table>
Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
<ol> <li>How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates have gained in popularity.</li> <li>Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. </li> <li>We extend realized LTREs in two ways. First, we further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows us to compare the resultant effect on the growth rate of different environmental factors that may each act via multiple vital rates. Second, we show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.</li> <li>We illustrate how to use the approach in an IPM for data from a 26-year study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density, and unexplained random variation via multiple vital rates, and from demographic stochasticity.</li> <li>The case study suggests that variation in first-year survival via the random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.</li> </ol>
High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
<p>This dataset contains raw files from a mass spectrometric analysis of the exo-metabolome of the green macroalga <em>Ulva mutabilis</em> (Chlorophyta).</p>
A mathematical model to predict network growth in physarum polycephalum as a function of extracellular matrix viscosity, measured by a novel viscometer
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Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
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Data for: Growing faster, longer or both? Modelling plastic response of Juniperus communis growth phenology to climate change
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Modelling seasonal dynamics of secondary growth in R
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A generalized numerical model for clonal growth in scleractinian coral colonies
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Data from: A dynamical model of growth and maturation in Drosophila
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Data from: A new mechanistic model for individual growth suggests upregulated maintenance costs when food is scarce in an insect
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Data set to ''Volcano growth versus deformation by strike-slip faults: morphometric characterization through analogue modelling'
<p>This data set is the supplementary material to Grosse et al. (2020) 'Volcano growth versus deformation by strike-slip faults: morphometric characterization through analogue modelling', published in Tectonophysics (https://doi.org/10.1016/j.tecto.2020.228411). The data set consists of (1) 249 digital elevation models (DEMs) of each step of the the analogue experiments carried out, in standard ENVI format, zipped; and (2) an Excel file containing the DEM-derived morphometric parameters for each of the analogue models.</p> <p>Experiments were carried out at the analogue modelling lab of the Department of Geography at the Vrije Universiteit Brussel (Belgium). A granular mixture of fine-grained quartz sand and kaolin clay was used as analogue material. Experiments were conducted on a fixed table, on which a basal layer of granular material was placed. A basal plate attached to a step-motor was used to simulate pure strike-slip displacements of the basal layer. Volcano growth was simulated by depositing loads of granular material on top of the basal layer from a point source. The analogue models were photographed at regular time intervals during the experiments using four digital cameras. The photographs were used to generate synthetic digital elevation models (DEMs) with 0.2 mm spatial resolution of each step of the analogue models by applying the MICMAC digital stereo-photogrammetry software. The ENVI software was used to re-sample the DEMs to a 0.5 mm spatial resolution and apply the noise-reduction Lee filter. Morphometric data were then extracted from the DEMs by applying two IDL-language algorithms: NETVOLC, used to automatically calculate the volcano edifice basal outline, and MORVOLC, used to extract a set of morphometric parameters.</p>
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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)
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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.