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226 results for “growth model”
Simulation/Plot Data of collisional growth column model
<p>This dataset contains</p> <ul> <li>Simulation data of collisional column model as used in the GMD-study by Unterstrasser et al., 2020: Collisional growth in a particle-based cloud microphysical model: Insights from column model simulations using LCM1D (v1.0)</li> </ul> <ol> <li>Bott_1D.zip and Wang_1D.zip contain simulation data of reference bin models</li> <li>AON_1D.zip contains simulation data of the new column model with Lagrangian microphysics treating collisional grwoth with the All-or-Nothing algorithm</li> </ol> <ul> <li>Scripts to reproduce all figures (that show simulation data) of the above mentioned study.<br> Unpack Plot_scripts.zip;<br> In order to run the plot scripts it is also necessary to install the ColumnModel code which is hosted on GitHub. The (frozen) model version used here is released in a separate Zenodo upload (DOI: 10.5281/zenodo.4031214).</li> <li>Figure files and their according source files<br> Unpack Plots.zip</li> </ul> <p> </p> <p> </p>
Mechanical stimulation prevents impairment of axon growth and overcompensates microtubules destabilization in cellular models of Alzheimer's disease related Tau pathology
<p>Data and metadata associated to a publication 10.3389/fmed.2025.1519628</p>
Dataset: Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: a moment of truth?
<p><span>Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; <em>Δ</em>) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (<em>Gallus gallus domesticus</em>) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues,<em> δ</em><sup>13</sup>C and <em>δ </em><sup>15</sup>N values differed among dietary groups. <em>Δ</em><sup>13</sup>C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For <em>Δ</em><sup>15</sup>N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type</span><span><span>, </span></span><span><span>with f</span></span><span>emales having higher liver <em>Δ</em><sup>15</sup>N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, they demonstrate experimentally that these types of models reliably characterize consumed diets when appropriately parameterized.<span> </span></span></p>
South-East US historical and projected population per county for FUTURES urban growth modeling in GRASS GIS
<p>South East US historical (2001-2019) and projected (2020-2100) population per county for 6 states (NC, SC, TN, GA, AL, FL). Historical data come from The National Vital Statistics System (https://seer.cancer.gov/popdata/download.html) and future data are projected by Hauer 2019 (https://doi.org/10.1038/sdata.2019.5) for SSP2 scenario. Data are formatted for r.futures.demand module, which is a GRASS GIS addon for computing future land demand for FUTURES urban growth model. </p>
Plant root growth against a mechanical obstacle: The early growth response of a maize root facing an axial resistance is consistent with the Lockhart model
<p>Plant root growth is dramatically reduced in compacted soils, affecting the growth of the whole plant. Through a model experiment coupling force and kinematics measurements, we probed the force-growth relationship of a primary root contacting a stiff resisting obstacle, that mimics the strongest soil impedance variation encountered by a growing root. The growth of maize roots just emerging from a corseting agarose gel and contacting a force sensor (acting as an obstacle) was monitored by time-lapse imaging simultaneously to the force.<br><br>The evolution of the velocity field along the root was obtained from kinematics analysis of the root texture with a PIV derived-technique. A triangular fit was introduced to retrieve the elemental elongation rate or strain rate. A parameter-free model based on the Lockhart law quantitatively predicts how the force at the obstacle modifies several features of the growth distribution (length of the growth zone, maximal elemental elongation rate, velocity) during the first 10 minutes. These results suggest a strong similarity of the early growth responses elicited either by a directional stress (contact) or by an isotropic perturbation (hyperosmotic bath).</p>
Figure 2 in A population growth model of Tetranychus urticae Koch (Acari: Tetranychidae)
Figure 2. Growth of total population of T. urticae on two bean fitted to logistic curve.
Figure 1 in A population growth model of Tetranychus urticae Koch (Acari: Tetranychidae)
Figure 1. Population fluctuation of total population of T. urticae on two bean fields in 2016.
Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation
<p>Data for "Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation"</p> <p> </p> <p>GrowthChannelExperiments contains the data-folders of the following growth channel experiments:<br> ***********************************************************************************************</p> <p>Name Feeding Concentration [in units of 0.195mM PCA]<br> nd004_series1 0.5<br> nd004_series2 0.5<br> nd004_series3 0.5<br> nd004_series4 2.0<br> nd004_series5 2.0<br> nd004_series6 2.0<br> nd004_series7 3.0<br> nd004_series8 3.0<br> nd112_series2 0.25<br> nd112_series3 0.25<br> nd112_series7 3.0<br> nd112_series8 3.0</p> <p>Every folder contains:<br> - a tif-file with captured image series<br> - a PIV*-folder with four PIV-files for every frame pair. The four files belong to intermediate results of the multistep PIV. The final PIV-result is given in the file step2*.dat.nmt.<br> The PIV result will be stored in a plain text file. Each line in this file correspond to each PIV vector and comprised of 16 columns:<br> x y ux1 uy1 mag1 ang1 p1 ux2 uy2 mag2 ang2 p2 ux0 uy0 mag0 flag<br> -- (x,y) is the position of the vector (center of the interrogation window).<br> -- ux1, uy1 are the x and y component of the vector (displacement) obtained from the 1st correlation peak.<br> -- mag1 is the magnitude (norm) of the vector.<br> -- ang1, is the angle between the current vector and the vector interpolated from previous PIV iteration.<br> -- p1 is the correlation value of the 1st peak.<br> -- ux2,uy2,mag2,ang2,p2 are the values for the vector obtained from the 2nd correlation peak.<br> -- ux0, uy0, mag0 are the vector value at (x,y) interpolated from previous PIV iteration.<br> -- flag is a column used for mark whether this vector value is interpolated (marked as 999) or switched between 1st and 2nd peak (marked as 21), or invalid (-1). <br> According to the PIV-Fiji-plugin as provided by Qingzong Tseng, used also in : <br> Tseng, Q. et al. Spatial organization of the extracellular matrix regulates cell-cell junction positioning. Proc. Natl. Acad. Sci. 109, 1506–1511 (2012)<br> - two traj*.dat files, belonging to particle positions of the corresponding simulation with monod/teissier uptake. <br> Columns correspond to <br> 1 : time | 2 : cellID | 3 : rx | 4 : ry | 5 : rz | 6: species | 7 : vx | 8 : vy | 9 : vz | 10 : fx | 11 : fy | 12 : fz | 13 : B(g) |<br> -- rx,ry,rz 3D coordinates of particle<br> -- species is either 0 (living cell) or 1 (wall-particle)<br> -- vx,vy,vz 3D velocity of particle<br> -- fx,fy,fz 3D force of particle<br> -- B(g) growth force constant dependent on local g-concentration<br> Note that due to the simulation being 2D, rx=constant and vx=0=fx.<br> - two g*.dat files, belonging to nutrient concentrations of the corresponding simulation with monod/teissier uptake. <br> Columns correspond to <br> 1 : time | 2 : gridx | 3 : gridy | 4 : gridz | 5 : g-conc | 6: kcons | 7 : kprod | 8: Dlocal |<br> -- gridx,gridy,gridz coordinates of lattice side<br> -- kcons local nutrient consumption rate<br> -- kprod local nutrient production rate (always zero)<br> -- Dlocal local diffusion constant</p> <p> </p> <p>GrowthChamberExperiments contains the the data-folders of the following growth chamber experiments:<br> ***************************************************************************************************</p> <p>Name Feeding Concentration [in units of 0.195mM PCA]<br> nd143_xy009 1.0<br> nd143_xy013 1.0<br> nd143_xy025 1.0<br> nd143_xy032 1.0<br> nd143_xy059 1.0<br> nd143_xy060 1.0<br> nd143_xy061 1.0<br> nd143_xy165 0.1<br> nd143_xy184 0.1<br> nd143_xy214 0.1</p> <p>Every folder contains:<br> - a tif-file with captured image series<br> - five traj*.dat files, belonging to particle positions of the corresponding simulation with monod-uptake and five different ratios of the diffusion constants in- and outside the colony.<br> - five g*.dat files, belonging to nutrient concentrations of the corresponding simulation with monod-uptake and five different ratios of the diffusion constants in- and outside the colony.</p>
Model dataset (Daily growth and Maximum daily shrinkage)
<p>Dadaset was used to create two models to verify the relationship between the stages of visual cocoa flushes and stem diameter using dendrometer sensors:</p> <p>Daily net growth x Flushes<br>Maximum Daily Shrinkage x Flushes</p> <p>Dadaset used to write the article (Growing cocoa in semi-arid climate and the rhythmicity of stem growth and leaf flushing determined by dendrometers)</p>
Supplementary materials for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model"
<h2>Overview</h2> <p>This folder contains supplementary materials corresponding to the analysis conducted for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model". The folder is structured into two .zip files. <a href="../api/records/10720907/draft/files/ZENODO_PISCES_MLC.zip/content" target="_blank" rel="noopener noreferrer">ZENODO_PISCES_MLC.zip</a> contains the analysis presented in the paper. BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <h2>ZENODO_PISCES_MLC Folder Structure</h2> <h3>BDM</h3> <ul> <li><strong>MAREDAT_TUNED_SDM.csv</strong>: This file contains the BDM mesozooplankton biomass monthly climatology from MAREDAT data.</li> </ul> <h3>CODE</h3> <p>This directory contains Jupyter Notebook files (<code>.ipynb</code>) and related Python scripts used for data analysis and visualization. Below is a list of the files:</p> <ul> <li><strong>Code_Fig3_FigA8_FigA17.ipynb</strong>: Jupyter Notebook for generating figures 3, A8, and A17.</li> <li><strong>Code_Fig4.ipynb</strong>: Jupyter Notebook for generating figure 4.</li> <li><strong>Code_Fig5_FigA12_FigA13.ipynb</strong>: Jupyter Notebook for generating figures 5, A12, and A13.</li> <li><strong>Code_Fig6.ipynb</strong>: Jupyter Notebook for generating figure 6.</li> <li><strong>Code_Fig7.ipynb</strong>: Jupyter Notebook for generating figure 7.</li> <li><strong>Code_FigA10.ipynb</strong>: Jupyter Notebook for generating figure A10.</li> <li><strong>Code_FigA11.ipynb</strong>: Jupyter Notebook for generating figure A11.</li> <li><strong>Code_FigA14.ipynb</strong>: Jupyter Notebook for generating figure A14.</li> <li><strong>Code_FigA15.ipynb</strong>: Jupyter Notebook for generating figure A15.</li> <li><strong>Code_FigA16.ipynb</strong>: Jupyter Notebook for generating figure A16.</li> <li><strong>Code_FigA1.ipynb</strong>: Jupyter Notebook for generating figure A1.</li> <li><strong>Code_FigA2.ipynb</strong>: Jupyter Notebook for generating figure A2.</li> <li><strong>Code_FigA6_FigA7.ipynb</strong>: Jupyter Notebook for generating figures A6 and A7.</li> <li><strong>Code_FigA9.ipynb</strong>: Jupyter Notebook for generating figure A9.</li> <li><strong>Code_POC_metrics_not_in_the_paper.ipynb</strong>: Jupyter Notebook containing metrics related to particulate organic carbon (POC) not included in the paper.</li> <li><strong>Code_Table3.ipynb</strong>: Jupyter Notebook for generating table 3.</li> <li><strong>Code_Table4.ipynb</strong>: Jupyter Notebook for generating table 4.</li> <li><strong>Code_Table5.ipynb</strong>: Jupyter Notebook for generating table 5.</li> <li><strong>GlobalEstimatesAbstract.ipynb</strong>: Jupyter Notebook containing global estimates abstract.</li> <li><strong>mlctools</strong>: Python package containing utility functions for the analysis.</li> </ul> <h3>OBS</h3> <p>This directory contains observed data used in the analysis:</p> <ul> <li><strong>BATS_zooplankton.csv</strong>: Zooplankton data from the Bermuda Atlantic Time-series Study (BATS).</li> <li><strong>CHL2.nc</strong>: Chlorophyll data in NetCDF format.</li> <li><strong>climatology_n_0_5.nc</strong>: Climatological data in NetCDF format.</li> <li><strong>HOTS_zooplankton.csv</strong>: Zooplankton data from the Hawaii Ocean Time-series (HOTS).</li> </ul> <h3>OUTPUT</h3> <p>This directory contains output files from PISCES simulations (yearly, monthly and 5-day-average outputs). </p> <ul> <li><strong>0class</strong>: Output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>0classregrid</strong>: Regridded output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>10classes</strong>: Output files for the '10classes' classification corresponding to PISCES-MOG.</li> <li><strong>10classesregrid</strong>: Regridded output files from PISCES-MOG.</li> <li><strong>2classes</strong>: Output files from PISCES-MOG-2LS.</li> <li><strong>2classesregrid</strong>: Regridded output files from PISCES-MOG-2LS.</li> <li><strong>NOALLOregrid</strong>: Regridded output files from PISCES-MOG-NA.</li> </ul> <h3>PLOT</h3> <p>This directory contains plots generated during the analysis:</p> <h3>TEMP</h3> <p>This directory contains temporary files used during the analysis, including data files and matrices.</p> <h2>BDM-MAREDAT-ZENODO Folder </h2> <p>BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <p>For any inquiries or data access requests, please contact corentin.clerc -at- usys.ethz.ch</p>
Inputs, results data and analysis script for the evaluation of the PDG-Arena forest growth model on beech-fir stands
<p>Supplementary files for simulations in Rouet et al. (2024): PDG-Arena: An eco-physiological model for characterizing tree-tree interactions in heterogeneous and mixed stands (doi: <a href="https://doi.org/10.1101/2024.02.09.579667" target="_blank" rel="noopener">10.1101/2024.02.09.579667</a>).</p> <p>This repository is an archive of the github repository PDG-Arena-extra (release v1.0.3), accessible at <a href="https://github.com/camille-rouet/PDG-Arena-extra/tree/v1.0.3" target="_blank" rel="noopener">https://github.com/camille-rouet/PDG-Arena-extra/tree/v1.0.3</a>.</p>
Figure 3 in Estimation of individual growth of the violet oyster Chama coralloides Reeve, 1846 (Bivalvia: Venerida) using Schnute model cases
Figure 3. Modal progression of individual cohorts for C. coralloides in Acapulco, Guerrero, Mexico.
Figure 1 in Estimation of individual growth of the violet oyster Chama coralloides Reeve, 1846 (Bivalvia: Venerida) using Schnute model cases
Figure 1. Monthly frequency of length of C. coralloides in Acapulco, Guerrero, Mexico.
Model and Data for the T&C-CROP Validation Paper: T&C-CROP: Representing mechanistic crop growth with a terrestrial biosphere model (T&C,v1.5): Model formulation and validation.
<p>Here included is the code used to run T&C-CROP as used for the GMD paper submission alongside with the necessary weather data and raw field data used as part of the validation exercise. </p> <p> </p>
Molybdate application in the early stages of shrimp growth suppresses sulphide formation in a shrimp pond bottom model
<p><span>Oxygen depletion and sulphide formation, resulting from the accumulation of organic waste, are common challenges in shrimp ponds that could result in complete harvest failure. The stage at which these circumstances occur during the shrimp growth period remains elusive, yet, knowledge of the timing of oxygen depletion and sulphide formation is essential to enable remediating actions. Here, we used an experimental shrimp pond model at different stages in the shrimp growth period to determine when oxygen depletion and sulphide production occur. Microscale depth measurements of oxygen and H<sub>2</sub>S were determined using microelectrodes to visualize their profiles at different depths of the water-sediment interface and the sediment. We evaluated the potential of different molybdate concentrations at different stages to determine the optimal conditions to suppress H<sub>2</sub>S formation. Oxygen depletion and sulphide production took place in the middle of the shrimp growth cycle. The addition of molybdate was only effective in the early stages of the onset of oxygen depletion and H<sub>2</sub>S formation, and residual molybdate was required to ensure a continues suppression of sulphate reduction to H<sub>2</sub>S. However, oxygen depletion could not be prevented and reintroduction of oxygen did not occur when molybdate was added. In conclusion, molybdate appeared to be an effective strategy to suppress H<sub>2</sub>S formation at the onset of its production in shrimp pond bottom model.</span></p>
Seasonality of cyanobacteria and eukaryotes in Lake Geneva and the impacts of cyanotoxins on growth of the model ciliate Tetrahymena pyriformis
<p><span>Toxic cyanobacteria are likely to be favored by global warming and other human impacts, posing significant threats to aquatic ecosystems. While cyanobacterial blooms in eutrophic lakes are widely investigated, the dynamics of cyanobacteria and the effects of their toxins and bioactive metabolites on the plankton communities in mesotrophic and oligotrophic lakes are less well understood. Here we investigated seasonal dynamics of cyanobacteria, eukaryotic algae and cyanotoxins in oligo-mesotrophic Lake Geneva—the largest and deepest lake in western Europe. High-throughput sequencing of the 16S rRNA genes in 143 samples along a water column revealed that Lake Geneva hosts diverse, co-dominant cyanobacterial genera, including <em>Planktothrix</em>, <em>Cyanobium</em>, <em>Pseudanabaena</em>, and <em>Aphanizomenon. </em>The abundance of the <em>mcyA</em> gene marker for microcystin production was highly correlated with total cyanobacteria abundance, obtained from qPCR of the 16S rRNA genes. Targeted LC-HRMS/MS analysis demonstrated peak concentrations of cyanotoxins in September and December 2021 at the deep chlorophyll-a maximum layer, reaching up to 1474 ng/l for anabaenopeptins and 144 ng/l for microcystins. The toxin peaks did not correlate with the abundance or variations in the cyanobacteria or eukaryote community, but they were correlated in time with seasonal lows in the abundances of ciliates (18S rRNA analysis). Laboratory exposure tests demonstrated that growth of the model ciliate <em>Tetrahymena pyriformis </em>was inhibited by Microcystin-RR and Anabaenopeptin A at environmentally relevant concentrations in the ng/l-range, in natural lake water, </span><span>synthetic freshwater, and growth media spiked with the cyanotoxins. Our findings suggest that even low concentrations (in the ng/l-range) of microcystins and anabaenopeptins, reduce growth of ciliates such as <em>T. pyriformis</em> and can be expected to have wider impacts on the eukaryote communities. </span></p>
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling (Source code and data)
<p>This dataset contains</p> <ul> <li>the source code</li> <li>the data and examples</li> <li>the material subroutine with examples of uniaxial strain and stress</li> </ul> <p>of the inelastic Constitutive Artificial Neural Network (iCANN) enhanced by the concept of homeostatic surfaces to discover tensional homeostasis.</p> <p>The corresponding publication is:</p> <p>Holthusen, H., Brepols, T., Linka, K., & Kuhl, E..<em> </em></p> <p><em>Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling.</em></p> <p> </p> <p><strong>Standalone_Materialroutine</strong></p> <ul> <li>00_Materialroutine: Contains the material subroutine implemented in FORTRAN</li> <li>01_uniaxial_strain: Example of the material subroutine in a uniaxial strain driven manner</li> <li>02_uniaxial_stress: Example of the material subroutine in a uniaxial stress driven manner</li> </ul> <p> </p> <p><strong>TensorFlow</strong></p> <ul> <li> <p>iCANN:</p> <ul> <li> <p>01_Biax/biax_l1: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L1 (Lasso) regularization</p> </li> <li> <p>01_Biax/biax_l2: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L2 (ridge) regularization</p> </li> <li> <p>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L1 (Lasso) regularization</p> </li> <li>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L2 (ridge) regularization</li> </ul> </li> <li> <p>iCANN_ABS_activation: Same four examples as above, however, with the absolute value activation function</p> </li> <li> <p>installed_packages: File containing a list of installed Python modules used to implement the iCANN</p> </li> </ul> <p>The TensorFlow implementations in all 01_Biax/ and 02_Uniax/ sub-directories are the same.</p> <p>The implementation in iCANN_ABS_activation is different with respect to the activation functions of the pseudo potential.</p> <p> </p> <p>The experimental data for the cross and stripe specimen are taken from the literature:</p> <p>Eichinger, J. F., Paukner, D., Szafron, J. M., Aydin, R. C., Humphrey, J. D., & Cyron, C. J. (2020).</p> <p>Computer-controlled biaxial bioreactor for investigating cell-mediated homeostasis in tissue equivalents. <em>Journal of biomechanical engineering</em>, <em>142</em>(7), 071011.</p> <p><a href="https://doi.org/10.1115/1.4046201">https://doi.org/10.1115/1.4046201</a></p>
Saguaro recruitment data obtained by inverse-growth modelling
<p>Each year, an individual mature large saguaro cactus produces about one million seeds in attractive juicy fruits that lure seed predators and seed dispersers in a three-month feast. From the million seeds produced, however, only a few will persist into mature saguaros. A century of research on saguaro population dynamics has led to the conclusion that saguaro recruitment is an episodic event that depends on the convergence of suitable conditions for survival during the critical early stages. Because most data have been collected in Arizona, particularly in the surroundings of Tucson, most research has relied on a limited amount of environmental variation. In this study, we upscaled this knowledge on saguaro recruitment to a regional scale with a new method that used the inverse-growth modeling of 1,487 saguaros belonging to 13 populations in a latitudinal gradient ranging from arid desert to tropical thornscrub forest in Sonora, Mexico. Using generalized linear and additive mixed models, we created two 110-year-long saguaro recruitment curves: one driven only by previous size, and the second driven by size, drought, and soil structure. We found evidence that saguaro recruitment is indeed episodic with periodicities of 20–30 years possibly related to strong El Niño Southern Oscillation events. Our results suggest that saguaros rely on multidecadal periodic pulses of good beneficial years to incorporate new individuals into their populations. Inverse-growth modelling can be used in a wide variety of plant species to study their recruitment dynamics.</p>
Supplemental information and Data for: Colloidal physics modeling reveals how per-ribosome productivity increases with growth rate in E. coli
<p>Faster growing cells must synthesize proteins more quickly. Increased ribosome abundance only partly accounts for increases in total protein synthesis rates. The productivity of individual ribosomes must increase too, almost doubling by an unknown mechanism. Prior models point to diffusive transport as a limiting factor but surface a paradox: faster growing cells are more crowded, yet crowding slows diffusion. We suspected physical crowding, transport, and stoichiometry, considered together, might reveal a more nuanced explanation. To investigate, we built a first-principles physics-based model of <em>E. coli</em> cytoplasm in which Brownian motion and diffusion arise directly from physical interactions between individual molecules of finite size, density, and physiological abundance. Using our microscopically-detailed model, we predict that physical transport of individual ternary complexes accounts for ~80% of translation elongation latency. We also find that volumetric crowding increases at faster growth even as cytoplasmic mass density remains relatively constant. Despite slowed diffusion, we predict that improved proximity between ternary complexes and ribosomes wins out, illustrating a simple physics-based mechanism for how individual elongating ribosomes become more productive. We speculate how crowding imposes a physical limit on growth rate and undergirds cellular behavior more broadly. Unfitted colloidal-scale modeling offers systems biology a complementary "physics engine" for exploring how cellular-scale behaviors arise from physical transport and reactions among individual molecules.</p>
Supporting Data and Guidance: Modeling policy pathways to maximize renewable energy growth and investment in Democratic Republic of the Congo using OSeMOSYS
<p>This repository contains data files and guidance documents that are supplementary materials to accompany the policy paper "Modeling policy pathways to maximize renewable energy growth and investment in Democratic Republic of the Congo using OSeMOSYS" available on Research Square here: <a href="https://www.researchsquare.com/article/rs-2702275/v1">https://www.researchsquare.com/article/rs-2702275/v1</a></p>
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.