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226 results for “growth model”
AN OPEN-SOURCE, THREE-DIMENSIONAL GROWTH MODEL OF THE MANDIBLE
<p>This repository contains all geometrical data and metadata belonging to the paper AN OPEN-SOURCE, THREE-DIMENSIONAL GROWTH MODEL OF THE MANDIBLE by the MAGIC Amsterdam research consortium. The following contents are uploaded:</p><p><strong>shapeVectors_original.csv</strong> | shape vectors of the original data<br><strong>shapeVectors_rescaled.csv</strong> | shape vectors of the rescaled data<br>678 x 62589 matrices where the rows are samples and the columns are shape vectors. The shape vectors are formatted<i> [x1, x2, x3, ..., y1, y2, y3, ..., z1, z2, z3, ...].</i></p><p><strong>PCA_coeff_original.csv</strong> | principal component coefficients of the original data<br><strong>PCA_coeff_rescaled.csv</strong> | principal component coefficients of the rescaled data<br>62589 x 677 matrices where each row of these matrices is a variable (x-, y-, or z-coordinate of a vertex) and each column is a principal component.</p><p><strong>PCA_score_original.csv</strong> | principal component scores of the original data<br><strong>PCA_score_rescaled.csv</strong> | principal component scores of the rescaled data<br>678 x 677 matrices where rows correspond to samples and columns correspond to principal components.</p><p><strong>PCA_latent_original.csv</strong> | principal component variances of the original data<br><strong>PCA_latent_rescaled.csv</strong> | principal component variances of the rescaled data<br>677 x 1 vectors where each element is an eigenvalue of a principal component.</p><p><strong>PCA_mu_original.csv</strong> | mean of the original data<br><strong>PCA_mu_rescaled.csv</strong> | mean of the rescaled data<br>1 x 62589 vectors that represent the average shape vector. All (centered) data can be reconstructed as follows: <i>shapeVectors = PCA_score * PCA_coeff' + PCA_mu.</i></p><p><strong>PCA_standardDeviations_original.csv</strong> | standard deviations of each sample for each principal component of the original data.<br><strong>PCA_standardDeviations_rescaled.csv</strong> | standard deviations of each sample for each principal component of the rescaled data.<br>677 x 678 matrices where the rows are principal components and the columns are samples. The standard deviations were calculated as follows: <i>PCA_standardDeviations = PCA_score' ./ sqrt(PCA_latent).</i></p><p><strong>metadata.csv</strong> | This matrix contains the age in years (first column) and biological sex (second column, 1 = male and 2 = female) for all samples (rows).</p><p><strong>connectivityList.csv</strong> | This matrix defines the mesh of the 3D model of the mandible. The vector in each row represents which vertices define a triangle. Indexing starts at 0, so for use in e.g. Matlab, add 1 to all elements.</p>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Cascading effects augment the direct impact of CO2 on phytoplankton growth in a biogeochemical model, links to model results
<p>This dataset provides the output of eight model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2022). In addition to information on the mesh, the dataset contains 1) 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, calcification, grazing rates, calcite concentrations, zooplankton biomass, export fluxes as well as CO<sub>2(aq)</sub>, HCO<sub>3</sub><sup>-</sup> and nutrient concentrations, and 2) a time series of global and North Atlantic coccolithophore biomass, temperature, and CO<sub>2(aq)</sub> concentrations from 1958 to 2018.</p> <p>File names refer to the Figures and Tables in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</p>
Modeled dynamic and thermodynamic sea ice growth in the Arctic 1980-2019 from NAOSIM
<p>This data set is related to the paper "Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth" by Ricker et al. (2021). Please refer to this study for further details.</p> <p>Ricker, R., Kauker, F., Schweiger, A., Hendricks, S., Zhang, J., & Paul, S. (2021). Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth, Journal of Climate, 34(13), 5215-5227. Retrieved Nov 24, 2022, from https://journals.ametsoc.org/view/journals/clim/34/13/JCLI-D-20-0848.1.xml</p>
AdriSC Climate Model Data - For the article: Projecting expected growth period of bivalves in a coastal temperate sea
<p>The recent implementation, development and successful runs of the kilometer-scale atmosphere-ocean Adriatic Sea and Coast (AdriSC) climate model for the historical period of 1987-2017 and for an extreme climate projection (RCP 8.5) for the 2070-2100 period, have provided the necessary dataset to better understand the potential impact of climate change within the Adriatic basin. Here, temperature, salinity and ocean currents were extracted and formatted from the AdriSC ocean model at 1 km resolution. This dataset was then used to reproduce in the past (1987-2017 period) and project in the future (2070-2100 period) the expected growth of five bivalve species in the northern Adriatic Sea at two different locations: Barbariga and along the western coast of Istria. </p> <p> </p>
Dataset - A lung-on-chip model reveals an essential role for alveolar epithelial cells in controlling bacterial growth during early M. tuberculosis infection
<p>Description of the sub-folders<br> Name, type of data, corresponding Figure in the manuscript<br> 3D view of the LoC model - .tiff image stack, Figure 1.</p> <p>Bacterial Growth Rate Data - .tiff image stacks, .csv files and MATLAB code to extract the fluorescence intensity over time, Figure 2, Figure 2 - figure supplement 2, Figure 2 - figure supplement 4, Figure 3, Figure 3 - figure supplement 2, Figure 4.</p> <p>AT Characterization - .tiff image stacks and MATLAB code to extract the number and volume of lamellar bodies from the stack of confocal images, Figure 1, Figure 1 - figure supplement 1, Figue 1 - figure supplement 2.</p> <p>AT Infection in LoC model - .tiff image stacks, Figure 2 - figure supplement 1.</p> <p>AT Infection in vivo - .tiff image stacks, Figure 1 - figure supplement 3.</p> <p>Simulations of in vivo infections - .dat files of growth rates in macrophages for the WT and ESX-1 deficient populations and MATLAB code to simulate an infection from this data, Figure 4.</p> <p> </p>
Data from: A dynamical model of growth and maturation in Drosophila
<p>The decision to stop growing and mature into an adult is a critical point in development that determines adult body size, impacting multiple aspects of an adult's biology. In many animals, growth-cessation is a consequence of hormone release that appears to be tied to attainment of particular body size or condition. Nevertheless, the size-sensing mechanism animals use to initiate hormone synthesis is poorly understood. Here we develop a simple mathematical model of growth cessation in <em>Drosophila melanogaster</em>, which is ostensibly triggered by attainment of a critical weight early in the last instar. Attainment of critical weight is correlated with synthesis of the steroid hormone ecdysone, which causes a larva to stop growing, pupate and metamorphose into the adult form. Our model suggests that, contrary to expectation, the size-sensing mechanism that initiates metamorphosis occurs before the larva reaches critical weight; that is, the critical-weight phenomenon is a downstream consequence of an earlier size-dependent developmental decision, not a decision point itself. Further, this size-sensing mechanism does not require a direct assessment of body size, but emerges from the interactions between body size, ecdysone and nutritional signaling. Because many aspects of our model are evolutionarily conserved among all animals, the model may provide a general framework for understanding how animals commit to maturing from their juvenile to adult form.</p>
Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data
<p>This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).</p> <p>First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.</p> <ul> <li><strong>Cl Concentrations - Wang et al. (2021).zip:</strong> Original Cl concentrations field from Wang et al. (2021). </li> <li><strong>CH4 Fluxes - Saunois et al. (2020).zip: </strong>Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> <p>Second, we store the processed input data generated for the purpose of our study.</p> <ul> <li><strong>CH4 Fluxes - LMDz9696.zip:</strong> Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>d13C Signatures - LMDz9696.zip:</strong> δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>dD Signatures - LMDz9696.zip:</strong> δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>OH O1D Concentrations - LMDz9696-INCA.zip:</strong> OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li><strong>Masks regions.zip</strong>: Masks for the regions used for the input data and the analysis.</li> </ul> <p> </p>
Data from: A new mechanistic model for individual growth suggests upregulated maintenance costs when food is scarce in an insect
<p>In order to calibrate and evaluate a recently developed growth model, the Maintenance-Growth Model (MGM), for the case of growth under food restriction, empirical data for house crickets (<em>Acheta</em> <em>domesticus</em>) were collected and analysed. This data set contains data for individually reared crickets growing under two different regimes of controlled food limitation as well as data for food-limited cohorts of growing house crickets. The sets include temporal data for body mass and ingestion as well as age and size at maturation (imago emergence). The data for food-limited cohorts were collected prior to this study and parts of it have previously been analysed and presented in a publication on animal self-thinning. </p>
DeepBacs – Escherichia coli growth stage object detection dataset and YOLOv2 model
<p>Training and test images of E. coli cells for object detection and classification using YOLOv2, as well as a trained YOLOv2 model.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>The example shows a bright field image of live <em>E. coli</em> cells and the respective annotation for specific growth stages.</p> <p> </p> <p><strong>Training and test dataset</strong></p> <p><strong>Data type</strong>: Paired microscopy images (bright field) and annotations in PASCAL VOC format</p> <p><strong>Microscopy data type</strong>: 2D bright field images recorded at 1 min interval</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli</em> MG1655 wild type strain (CGSC #6300).</p> <p><strong>File format</strong>: .png (8-bit)</p> <p><strong>Image size</strong>: 256 x 256 px² (158 nm / pixel), 100/15 individual frames (training/test dataset)</p> <p>1024 x 1024 px² (79 nm / pixel), 9 regions of interest with 80 frames @ 1 min time interval (live-cell time series)</p> <p><strong>Image preprocessing</strong>: Raw images were recorded in 16-bit mode (image size 512x512 px² @ 158 nm/px). 256 x 256 px² patches were extracted from individual frames and converted into 8-bit .png images after adjusting brightness and contrast. Annotation was performed online using <em>LabelImg </em>(https://github.com/tzutalin/labelImg).</p> <p> </p> <p><strong>YOLOv2 model</strong></p> <p>The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 100 manually annotated images (image dimensions: (256, 256)) with a batch size of 8 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12.1). Key python packages used include tensorflow (v 0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 11.0.221). The training was accelerated using a Tesla T4 GPU and data were augmented by a factor of 4 using flipping and rotation.</p> <p>The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook.</p> <p> </p> <p><strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
Dataset Friebus-Kardash et al, A chemerin peptide analog stimulates tumor growth in two xenograft mouse models of human colorectal carcinoma
<p>Dataset Friebus-Kardash et al, A chemerin peptide analog stimulates tumor growth in two xenograft mouse models of human colorectal carcinoma</p> <p> </p>
Data for: Growing faster, longer or both? Modelling plastic response of Juniperus communis growth phenology to climate change
<p>Aim: Plant growth and phenology plastically respond to changing climatic conditions both in space and time. Species-specific levels of growth plasticity determine biogeographical patterns and the adaptive capacity of species to climate change. However, a direct assessment of spatial and temporal variability in radial-growth dynamics is complicated, as long records of cambial phenology do not exist.</p> <p>Location: 16 sites across European distribution margins of <em>Juniperus communis</em> L. (the Mediterranean, the Arctic, the Alps and the Urals).</p> <p>Time period: 1940-2016</p> <p>Major taxa studied: <em>Juniperus communis</em></p> <p>Methods: We applied the Vaganov-Shashkin process-based model of wood formation to estimate trends in growing season duration and growth kinetics since 1940. We assumed that <em>J. communis</em> would exhibit spatially and temporally variable growth patterns reflecting local climatic conditions.</p> <p>Results: Our simulations indicate regional differences in growth dynamics and plastic responses to climate warming. Mean growing season duration is the longest at Mediterranean sites and, recently, there is a significant trend towards its extension of up to 0.44 days per year. However, this stimulating effect of longer growing season is counteracted by declining summer growth rates caused by amplified drought stress. Consequently, overall trends in simulated ring-widths are marginal in the Mediterranean. By contrast, durations of growing seasons in the Arctic show lower and mostly non-significant trends. However, spring and summer growth rates follow increasing temperatures, leading to a growth increase of up to 0.32 % per year.</p> <p>Main conclusions: This study highlights the plasticity in growth phenology of widely distributed shrubs to climate warming–an earlier onset of cambial activity that offsets the negative effects of summer droughts in the Mediterranean and, conversely, an intensification of growth rates during the short growing seasons in the Arctic. Such plastic growth responsiveness allows woody plants to adapt to the local pace of climate change.</p>
Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model? — Supporting Data
<p>Supporting data for Martens et al. Deep Learning for Reaction-Diffusion Glioma Growth Modelling: Towards a Fully Personalised Model? arXiv:2111.13404.</p>
Modelling seasonal dynamics of secondary growth in R
<p>The monitoring of seasonal radial growth of woody plants addresses the ultimate question of when, how, and why trees grow. Assessing the growth dynamics is important to quantify the effect of environmental drivers and understand how woody species will deal with the ongoing climatic changes. One of the crucial steps in the analyses of seasonal radial growth is to model the dynamics of xylem and phloem formation based on increment measurements on samples taken at relatively short intervals during the growing season. The most common approach is the use of the Gompertz equation, while other approaches, such as general additive models (GAMs) and generalised linear models (GLMs), have also been tested in recent years. For the first time, we explored artificial neural networks with Bayesian regularisation algorithm (BRNNs) and show that this method is easy to use, resistant to overfitting, tends to yield s-shaped curves and is therefore suitable for deriving temporal dynamics of secondary tree growth. We propose two data processing algorithms that allow more flexible fits. The main result of our work is the XPSgrowth() function implemented in the radial Tree Growth (rTG) R package, that can be used to evaluate and compare three modelling approaches: BRNN, GAM and the Gompertz function. The newly developed function, tested on intra-seasonal xylem and phloem formation data, has potential applications in many ecological and environmental disciplines where growth is expressed as a function of time. Different approaches were evaluated in terms of prediction error, while fitted curves were visually compared to derive their main characteristics. Our results suggest that there is no single best fitting method, therefore we recommend testing different fitting methods and selection of the optimal one.</p>
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], <a href="https://doi.org/10.5194/tc-2021-333">https://doi.org/10.5194/tc-2021-333</a>, in review, 2021.</p> <p> </p> <p>Scripts for producing data and figures can be found at:</p> <p>https://doi.org/10.5281/zenodo.6561431</p> <p> </p> <p> </p>
Codes for Purgar et al. 2022: Investigating the ability of growth models to predict in situ Vibrio spp. abundances
<p>Model simulations and analysis of the Vibrio spp. growth models. <br> Prepared to accompany the publication, Purgar et al. 2022 "Investigating the ability of growth models to predict in situ<br> Vibrio spp. abundances" in Microorganisms, Special Issue „Microbial Communities in Changing Aquatic Environments“. </p> <p>Description of the files can be found in the Readme file.</p>
How does STICS crop model simulate crop growth and productivity under shade conditions ?
<p>The STICS crop model has been used to predict the response of winter wheat to different shade conditions from an artificial shade treatment. Detailed information on the modeling procedure will be available in the following paper: “ How does STICS crop model simulate crop growth and productivity under shade conditions ” in Field Crops Research Journal.</p> <p>To launch a simulation with STICS, several input data files and parameters are required. The files used in this study are available below: The different inputs files required to launch a simulation:</p> <ul> <li>The complete plant parameters file “<em>Winter_wheat_adjusted_plt.txt</em>”</li> <li>The weather data used “<em>Weather_tot.txt</em>”</li> </ul> <p>The data are compiled at a daily time scale for each treatment: CS constant shade; PS periodic shade; NS no shade. In this data frame, “<em>Temp_min</em>” and “<em>Temp_max</em>” are the minimal and maximal air temperature in degree celcius; “<em>Global_radiation</em>” is the daily cumulated global radiation in MJ/m²; “Rainfall” is the daily cumulated rainfall in mm; “<em>Wind</em>” is the mean wind speed in m/s; and “<em>Relative_humidity</em>” is the mean air relative humidity in %.</p> <ul> <li>The initial soil parameters <em>“INI_2014-2015_ini.txt”</em> and <em>“INI_2015-2016_ini.txt”</em> respectively for the growing season 2014-15 and 2015-16</li> <li>The general soil parameters for both growing season : <em>“Soil_sols.txt”</em></li> <li>The technical itinerary<em> “TEC_2015_tec.txt” and “TEC_2016_tec.txt” </em>respectively for the growing season 2014-15 and 2015-16.</li> </ul>
Figure 3 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 3: Temperature shift experiment from 18 °C to 30 °C. (A) After the temperature shift, the longitudinal growth of the propagules of equal length in three different tripartite communities was measured with ImageJ software and compared with the established model system for
Figure 2 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 2: Bioassay for morphogenetic activity performed at 18 °C. Using a tripartite community with Ulva mutabilis, the morphogenetic activity of the thallusin-releasing bacteria Maribacter sp. was complemented by one out of the four strains isolated from the surface of Ulva ohnoi. Under standard conditions, axenic gametes (A) were cultivated in the tissue culture flask with the tested bacteria alone (B–E), in the presence of Roseovarius sp. (G–J) or with Maribacter sp. MS6 (L–O) in comparison to the controls (F and K). Arrows with closed heads indicate protrusion formation due to the lack of thallusin released by Maribacter sp. Arrows with open heads indicate rhizoid formation in the presence of Maribacter sp. Magnification bar = 100 µm.
Figure 1 in High-temperature stress induces bacteria-specific adverse and reversible effects on Ulva (Chlorophyta) growth and its chemosphere in a reductionist model system
Figure 1: Workflow. Selected bacteria IH2, IH18, IH25, and G8 were collected from the surface of Ulva ohnoi, phenocopying the activity of Roseovarius sp. MS2 and forming a tripartite community with Maribacter sp. MS6 and the gametophyte of Ulva mutabilis (morphotype "slender"; strain FSU-UM5-1). Ulva mutabilis (25 mg dry weight) was cultivated with the two bacterial strains (OD620 = 0.001) under standard conditions (Wichard and Oertel 2010). Propagules of equal length were stressed by a temperature shift from 18 °C to 30 °C using continuous light (80 µmol photon m−2 s−1) to avoid chronobiological effects. Axenic cultures and tripartite communities were prepared according to Spoerner et al. (2012). exo-Metabolomics and multivariate analysis of the metabolite profiling of the supernatant (150 mL) of four tripartite communities were performed according to Alsufyani et al. (2017) and Ghaderiardakani et al. (2022). Drawings of Ulva were taken from Wichard (2023) under the terms of CC BY 4.0. Created with BioRender.com.
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