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3,484 results for “Plastics”
Computational analyses of dynamic visual courtship display reveal diet-dependent and plastic male signaling in Rabidosa rabida wolf spiders
<p>It has long been a challenge to quantify the variation in dynamic motions to understand how those displays function in animal communication. The traditional approach is dependent on labor-intensive manual identification/annotation by experts. However, the recent progress in computational techniques provides researchers with toolsets for rapid, objective, and reproducible quantification of dynamic visual displays. In the present study, we investigated the effects of diet manipulation on dynamic visual components of male courtship displays of <em>Rabidosa</em> <em>rabida</em> wolf spiders using machine learning algorithms. Our results suggest that (i) the computational approach can provide an insight into the variation in the dynamic visual display between high- and low-diet males which is not clearly shown with the traditional approach and (ii) males may plastically alter their courtship display according to the body size of females they encounter. Through the present study, we add an example of the utilization of recent computational techniques for understanding the evolution of animal behaviors.</p>
Data and scripts from "Unsupervised learning for structure detection in plastically deformed crystals"
<p>This documents contains the scripts and dataset used for the paper "Unsupervised learning for structure detection in plastically deformed crystals".</p> <p> </p> <p>More precisely it contains 4 folders :</p> <p><br> DumpForFigures : subfolder containing the atomic positions in .dump format (see lammps documentation) used for the article figures.</p> <p>DumpForTraining : subfolder containing the atomic position in .dump format (see lammps documentation) used for training the autoencoder.</p> <p>ScriptsToDetectStructuresFromDump : subfolder containing the script sused to detect the substructures of the system by combining autoencoder and clustering methods. This folder contains a readme with the details of the contents.</p> <p>ScriptToGenerateDump : subfolder containing the scripts used to generate the atomic data with molecular dynamics. These data are then used to train the autoencoder. This folder contains a readme with the details of the contents.</p> <p>REQUIREMENTS :</p> <p> </p> <p>Lammps</p> <p>Python3 with packages :</p> <p>-numpy</p> <p>-matplotlib</p> <p>-pyscal</p> <p>-sci-kit learn</p> <p>-pytorch</p> <p>-glob</p> <p> </p> <p> </p> <p> </p>
Anticipatory plasticity: frog embryos respond to environmental cues by producing an adaptive phenotype at hatching
<p>Developmental plasticity can occur at any life stage, but a context in which it might be crucial is when individuals that produce specific phenotypes early in development gain a competitive advantage at a later life stage. Here we asked if pre-hatching (embryonic) exposure to a nutrient-rich resource can impact hatchling morphology in tadpoles of Mexican spadefoot toads, <em>Spea multiplicata</em>. Induction of a distinctive carnivore morph can occur when a tadpole eats live fairy shrimp. We investigated whether cues from fairy shrimp, detected as embryos, determine hatchling morphology in a manner allowing individuals to take advantage of this nutritious resource. We found that hatchlings with embryonic exposure to shrimp were larger and had larger jaw muscles––traits that increase their ability to compete for shrimp. Thus, embryos can assess and respond to environmental cues by producing preemptive resource-use phenotypes. Such anticipatory plasticity may be an important but understudied form of developmental plasticity.</p>
NIR-MFCO dataset: Near-infrared-based false-color images of post-consumer plastics at different material flow compositions and material flow presentations
<p>Determining mass-based material flow compositions (MFCOs) is crucial for assessing and optimizing the recycling of post-consumer plastics. Currently, MFCOs in plastic recycling are mostly determined through manual sorting analysis, but the use of inline near-infrared (NIR) sensors holds potential to automate the characterization process, paving the way for novel sensor-based material flow characterization (SBMC) applications. The NIR-MFCO dataset aims to expedite SBMC research by providing NIR-based false-color images of plastic material flows with their corresponding MFCOs. The false-color images were created through the pixel-based classification of binary material mixtures using a hyperspectral imaging camera (EVK HELIOS NIR G2-320; 990 nm – 1678 nm wavelength range) and the on-chip classification algorithm (CLASS 32). The resulting NIR-MFCO dataset includes <em>n</em> = 880 false-color images from three test series: (T1) high-density polyethylene (HDPE) and polyethylene terephthalate (PET) flakes, (T2a) post-consumer HDPE packaging and PET bottles, and (T2b) post-consumer HDPE packaging and beverage cartons for <em>n</em> = 11 different HDPE shares (0% - 50%) at four different material flow presentations (singled, monolayer, bulk height H1, bulk height H2). The dataset can be used, e.g., to train machine learning algorithms, evaluate the accuracy of inline SBMC applications, and deepen the understanding of segregation effects of anthropogenic material flows, thus further advancing SBMC research and enhancing post-consumer plastic recycling.</p>
On the use of antibiotics in plasticity research: gastropod shells unveil a tale of caution
<p>1) Through phenotypic plasticity, individual genotypes can produce multiple phenotypes dependent on the environment. In the modern world, anthropogenic influences such as man-made pharmaceuticals are increasingly prevalent. They might alter observable patterns of plasticity and distort our conclusions regarding the adaptive potential of natural populations.</p> <p>2) Antibiotics are nowadays nearly ubiquitous in aquatic environments and prophylactic antibiotic use is also becoming more common to optimize animal survival and reproductive output in artificial settings. In the well-studied plasticity model system <em>Physella acuta</em>, prophylactic erythromycin treatment acts against gram-positive bacteria and thereby reduces mortality.</p> <p>3) Here, we study its consequences for inducible defence formation in the same species. In a 2×2 split-clutch design, we reared 635 <em>P. acuta</em> in either the presence or absence of this antibiotic, followed by 28-day exposure to either high or low predation risk as perceived through conspecific alarm cues.</p> <p>4) Under antibiotic treatment, risk-induced increases in shell thickness, a well-known plastic response in this model system, were larger and consistently detectable. Antibiotic treatment reduced shell thickness in low-risk individuals, suggesting that in controls, undiscovered pathogen infection increased shell thickness under low risk. Family variation in risk-induced plasticity was low, but the large variation in responses to antibiotics among families suggests different pathogen susceptibility between genotypes. Lastly, individuals that developed thicker shells had reduced total mass, which highlights resource trade-offs.</p> <p>5) Antibiotics thus have the potential to uncover a larger extent of plasticity, but might counterintuitively distort plasticity estimates for natural populations where pathogens are a part of natural ecology.</p>
Spring temperature drives phenotypic selection on plasticity of flowering time
<p>Data on field observations of flowering time and fitness of the perennial forest herb <em>Lathyrus vernus</em> and on spring temperature from weather station data. The dataset includes 22 years of data (1987–1996 and 2006–2017) from a <em>L. vernus</em> population located in a deciduous forest in Tullgarn, SE Sweden (58.9496 N, 17.6097 E). It includes records from 837 individuals (607 from 1987 to 1996, and 230 from 2006 to 2017), and from 2478 flowering events.</p> <p>The dataset includes the following variables:</p> <p>year: year of the recording</p> <p>id_nr: numeric plant id</p> <p>id: unique plant id (combination of numeric plant id and period)</p> <p>fcode: flowering code (1 if the plant flowered on that year, 0 if not)</p> <p>FFD: First Flowering Date</p> <p>n_fl: number of flowers</p> <p>n_fr: number of fruits</p> <p>totseed: total number of seeds</p> <p>intactseed: total number of intact seeds (not damaged by seed predator beetles)</p> <p>shoot_vol: shoot volume</p> <p>period: old (1987–1996) or new (2006–2017)</p> <p>n_years_fl_fitness: number of years when data on flowering and fitness is available</p> <p>n_years_study: number of years when the plant was included in the study</p> <p>mean_4: Average daily mean temperature of April (calculated from nearby meteorological station data)</p> <p>cmean_4: Mean-centred average daily mean temperature of April</p>
Data of "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator"
<p><strong>General</strong></p> <p>Data of <a href="http://doi.org/10.1016/j.ijsolstr.2023.112470">https://doi.org/10.1016/j.ijsolstr.2023.112470</a> related to MOAMMM project.</p> <p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator.",<br> journal = "International Journal of Solids and Structures",<br> year = "2023",<br> volume = "283",<br> pages = "112470",<br> doi = "10.1016/j.ijsolstr.2023.112470",<br> author = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels"</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p> <p><strong>Description</strong></p> <p>BI code and results of the inference of a pressure-dependent visco-elastic visco-plastic model developed in [NGU16] with a umat implementation in <a href="https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP">https://gitlab.uliege.be/moammm/moammmPublic/code/-/tree/main/MaterialModels/FiniteStrain/Finite_VEVP</a>. The BI is described in [WU23] .The experimental results used in the BI are reported in [COB22,COB22b]. To run the BI you need the open source code <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> If you use these data or model, we would be grateful if you could cite the related papers.</p> <p><strong>Bibliography</strong></p> <ul> <li>[WU23] L. Wu, C. Anglade, L. Cobian, M. Monclus, J. Segurado, F. Karayagiz, U. Santos Freitas, L. Noels, Bayesian inference of high-dimensional finite-strain visco-elastic-visco-plastic model parameters for additive manufactured polymers and neural network based material parameters generator, International Journal of Solids and Structures (2023) 112470: https://doi.org/10.1016/j.ijsolstr.2023.112470</li> <li>[COB22] L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556: https://doi.org/10.1016/j.polymertesting.2022.107556 (in Open access)</li> <li>[COB22b] Data of “. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus, Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behaviour, Polymer Testing 110 (2022) 107556” http://dx.doi.org/10.5281/zenodo.6136935 (in Open access)</li> <li>[NGU16] V. D. Nguyen, F. Lani, T. Pardoen, X. Morelle, L. Noels, A large strain hyperelastic viscoelastic-viscoplastic-damage constitutive model based on a multi-mechanism non-local damage continuum for amorphous glassy polymers. International Journal of Solids and Structures 96 (2016): 192-216; https://dx.doi.org/10.1016/j.ijsolstr.2016.06.008, Open access: https://orbi.uliege.be/handle/2268/197898</li> </ul> <p><strong>Directories</strong></p> <p>All the codes and experimental results are in five directories:</p> <ol> <li>experimentalTests: experimental data, see the README.txt in each subdirectory for details</li> <li>BayesianVE: BI of the visco-elastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE range <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVE_H.dat and Load_ExpVE_V.dat, which keep the experimental observations and loading conditions to perform the BI.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVE_H.dat and Load_ExpVE_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VE_V2Step and VE_H: BI for viscoelastic properties of "V" specimen (VE_V2Step) and "H" specimen (VE_H) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VE_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>MCMC_VE_....dat in the VE_V2Step and VE_H directories are the BI results</li> <li>When proceeding in two steps in VE_V2Step, a first step generates MCMC_VE_VN8_1st.dat whose posterior is used as prior in the second step to generate MCMC_VE_VN8_2nd.dat</li> </ol> </li> <li>CheckBayRes: to visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions of a BI parameter sample (read last sample by default, V or H direction can be selected at line</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> <li>uses as input VE_V2Step/MCMC_VE_....dat or VE_H/MCMC_VE_....dat</li> <li>uses local ViscoElasticTest.py, line.geo, line. msh as interface with https://gitlab.onelab.info/cm3/cm3Libraries code</li> <li>uses local functions plotExpLoad_Unload.py, plotExp.py</li> </ol> </li> <li>ViscoElasticTest.py, line.geo, line.msh: interface with <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code used by VE_V2Step and VE_H to call the VEVP model</li> </ol> </li> <li>BayesianVEVP: BI of the visco-elastic and visco-plastic parameters <ol> <li>PlotExperimentalCurves: to vizualize the experimental curves and prepare the observations for the BI in the VE-VP ranges <ol> <li>Loadcase_H.py and Loadcase_V.py read experimental results and create Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat, which keep the experimental observations and loading conditions to perform BI at the viscoplastic stage.</li> <li>PrintDir_H & PrintDir_V subdirectories with the functions called by Loadcase_H.py and Loadcase_V.py</li> <li>Load_ExpVEVP_H.dat and Load_ExpVEVP_V.dat created files with the observations and loading conditions to perform the BI</li> </ol> </li> <li>VP_V2step and VP_H2step: BI for viscoelastic-viscoplastic properties of "V" specimen (VP_V2Step) and "H" specimen (VP_H2Step) <ol> <li>BI_allpos_sequence.py runs the BI using Predict_VETest.py and creates the MCMC_VP_....dat</li> <li>WarmStart = True is used to restart an inference</li> <li>It starts from the VE prosterior as prior, see point 2, and generates a MCMC_VP_?_1of2Steps.dat (? being H or V)</li> <li>Then using MCMC_VP_?_1of2Steps.dat posterior to get a new prior, it generates MCMC_VP_?_2of2Steps.dat (? being H or V)</li> </ol> </li> <li>CheckBayRes: visualize predictions of a BI sample and experimental curves <ol> <li>MCMCRes.py is used to check the numerical predictions with 3 BI parameter samples ([28000, 45000,70000] by default, V or H direction can be selected at line 12) using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>plot_hist.py is used to plot histograms of all the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>Plot_Prop.py plots joints histograms of the inferred parameters using the samples of BayesianVEVP/VP_?2step/MCMC_VP_?_2of2Steps.dat (? being H or V)</li> <li>ResKGEmu.py plots the evolution of elastic properties with time</li> </ol> </li> <li>VEVPTest.py: interface with https://gitlab.onelab.info/cm3/cm3Libraries code used by VP_V2Step and VP_H2Step to call the VEVP model</li> </ol> </li> <li>RandomParametersGenerator: used to generate the parameters from the BI samples, with the same statistical content <ol> <li>Generator <ol> <li>DataProcess.py: creates normalized data for training from final inferred parameters in ../MCMC_ResData and creates ?_dirNormData (? being H or V)</li> <li>KmeanDataProcess.py: performs clustering for the data of H_dirNormDat and creates H_dirNormData_2cluster (no need for V direction because not bimodal)</li> <li>Gan_V.py and Gan_H.py are used to train the random material parameter generators and create the VDir_Gan or HDir_Gan200_0/HDir_Gan200_1</li> <li>GenerateParameters.py generates random parameters using the Gan files VDir_Gan or HDir_Gan200_0/HDir_Gan200_1 and checks the joint histograms of generated parameters, generated parameters are in V_GenData and H_GenData</li> <li>Ganlib.py is used by the generator</li> </ol> </li> <li>CheckRes <ol> <li>GenDataRes.py is used to check the numerical predictions with the generated parameter samples, see point 4) (using V_GenData and H_GenData).</li> <li>Plot_PropGen.py plots joints histograms of the generated parameters using the samples of V_GenData or H_GenData</li> </ol> </li> </ol> </li> <li>MCMC_ResData:All final data used in the paper (they can substitute the ones used here above) <ol> <li>H_direction and V_direction keep the MCMC random walk results of BI.</li> <li>RandomParameterGenerator keeps results of the generator Paper</li> </ol> </li> </ol> <p><strong>Figures of [WU23]</strong></p> <ul> <li>Fig. 5: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_V/plotExp_T.py or ./PrintDir_V/plotExp_C.py or ./PrintDir_V/plotExp_R.py</li> <li>Fig. 7: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 8: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 9: BayesianVEVP/CheckBayRes/MCMCRes.py with direct = "H" (requires<a href="https://gitlab.onelab.info/cm3/cm3Libraries"> https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 11: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" and then with direct = "H" and with Var = [0,1,20,24,28,29,30,31]</li> <li>Fig. 12: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "V" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 13: RandomParametersGenerator/CheckRes/GenDataRes.py with direct = "H" (requires <a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a> code)</li> <li>Fig. 14A: From directory BayesianVE/PlotExperimentalCurves, run python3 ./PrintDir_H/plotExp_T.py or ./PrintDir_H/plotExp_C.py or ./PrintDir_H/plotExp_R.py</li> <li>Fig. 15C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "V", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 16C: BayesainVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 17C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "V"</li> <li>Fig. 18C: BayesianVEVP/CheckBayRes/Plot_Prop.py with direct = "H", Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 19C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 20C: BayesianVEVP/CheckBayRes/plot_hist.py with direct = "H"</li> <li>Fig. 21D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "V" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> <li>Fig. 22D: RandomParametersGenerator/CheckRes/Plot_PropGen.py with direct = "H" , Var = [2,3,8,9,14,15,18,19] and [20,21,22,23,24,25,26,27]</li> </ul> <p> </p> <p> </p>
Social plasticity enhances signal-preference co-divergence
<p>Data associated to the manuscript titled:</p> <p>Social plasticity enhances signal-preference co-divergence</p> <p>by</p> <p>Camille Desjonquères, Bretta Speck, Sara Seidita, Lauren A. Cirino, Ignacio Escalante, Clinton Sergi, Jak Maliszewski, Gerlinde Hoebel, Nathan W. Bailey, Rafael L. Rodríguez</p>
Data for: Weaker selection on genes with treatment-specific expression consistent with a limit on plasticity evolution in Arabidopsis thaliana
<p>Differential gene expression between environments often underlies phenotypic plasticity. However, environment-specific expression patterns are hypothesized to relax selection on genes, and thus limit plasticity evolution. We collated over 27 terabases of RNA-sequencing data on <em>Arabidopsis thaliana</em> from over 300 peer-reviewed studies and 200 treatment conditions to investigate this hypothesis. Consistent with relaxed selection, genes with more treatment-specific expression have higher levels of nucleotide diversity and divergence at nonsynonymous sites but lack stronger signals of positive selection. This result persisted even after controlling for expression level, gene length, GC content, the tissue specificity of expression, and technical variation between studies. Overall, our investigation supports the existence of a hypothesized trade-off between the environment specificity of a gene's expression and the strength of selection on said gene in <em>A. thaliana</em>. Future studies should leverage multiple genome-scale datasets to tease apart the contributions of many variables in limiting plasticity evolution.</p>
Microbiome, mixotrophic algae, zooplankton, and fish amino acid and phospholipid fatty acid content in terrestrial and plastic carbon treatments
<p>Data includes amino acid (µg AA mg DW<sup>-1</sup>) and phospholipid fatty acid content (µg FA mg DW<sup>-1</sup>) of the microbiome, mixotrophic algae, zooplankton, and fish<em> </em>from the four-trophic level experiment. The experiment included control (no addition), 13.5% <sup>13</sup>C-labelled beech leaves (<em>Fagus sylvatica</em>), 97% <sup>13</sup>C-labelled lignin-hemicellulose extracted from wheat (<em>Triticum aestivum</em>, ~80% lignin, 13% hemicellulose), and 99% <sup>13</sup>C-labelled polystyrene (microplastic). Incubation time in humic lake water was 14 days in the control, leaf, and lignin experiment but 56 days for polystyrene, which after mixotrophic algae (<em>Cryptomonas </em>sp.) was introduced to the bottles. In the next step, herbivorous zooplankton (<em>Daphnia magna</em>) consumed microbes, mixotrophic algae, and particles for five days which after they were used as the diet to zebrafish (<em>Danio rerio</em>) during a five-day experiment.</p>
Dopamine mediates the pea aphid wing plasticity
<p><span>Many organisms exhibit phenotypic plasticity, in which developmental processes result in different phenotypes depending on their environmental context. We focus on the molecular mechanisms underlying that environmental response. Pea aphids (<em>Acyrthosiphon</em> <em>pisum</em>) show a wing dimorphism, in which pea aphid mothers produce winged or wingless daughters when exposed to a crowded or low-density environment, respectively. We investigated the role of dopamine in mediating this wing plasticity, motivated by a previous study that found higher dopamine titers in wingless- versus winged-producing aphid mothers. In this study, we found that manipulating dopamine levels in aphid mothers affected the number of winged offspring they produced. Specifically, asexual female adults injected with a dopamine agonist produced a lower percentage of winged offspring, while asexual females injected with a dopamine antagonist produced a higher percentage of winged offspring, matching expectations based on the titer difference. We also found that genes involved in dopamine synthesis, degradation, and signaling were not differentially expressed between wingless- and winged-producing aphids. This result indicates that titer regulation happens in a non-transcriptional manner or that we sampled non-relevant timepoints or tissue. Overall, our work emphasizes that dopamine is an important component of how organisms process information about their environments.</span></p>
Thermal phenotypic plasticity of pre- and post-copulatory male harm buffers sexual conflict in wild Drosophila melanogaster
<div> <p><span>Strong sexual selection frequently leads to sexual conflict and ensuing male harm, whereby males increase their reproductive success at the expense of harming females. Male harm is a widespread evolutionary phenomenon with a strong bearing on population viability. Thus, understanding how it unfolds in the wild is a current priority. Here, we sampled a wild </span><span><em>Drosophila</em> <em>melanogaster</em></span><span> population and studied male harm across the normal range of temperatures under which it reproduces optimally in nature by comparing female lifetime reproductive success and underlying male harm mechanisms under monogamy (i.e., low male competition/harm) vs. polyandry (i.e., high male competition/harm). While females had equal lifetime reproductive success across temperatures under monogamy, polyandry resulted in a maximum decrease of female fitness at 24°C (35%), reducing its impact at both 20°C (22%), and 28°C (10%). Furthermore, female fitness components and pre- (i.e., harassment) and post-copulatory (i.e., ejaculate toxicity) mechanisms of male harm were asymmetrically affected by temperature. At 20ºC, male harassment of females was reduced, and polyandry accelerated female actuarial ageing. In contrast, the effect of mating on female receptivity (a component of ejaculate toxicity) was only modulated at 28ºC, where the mating costs for females decreased and polyandry mostly resulted in accelerated reproductive ageing. We thus show that, across a natural thermal range, sexual conflict processes and their effects on female fitness components are plastic and complex. As a result, the net effect of male harm on overall population viability is likely to be lower than previously surmised. We discuss how such plasticity may affect selection, adaptation and, ultimately, evolutionary rescue under a warming climate. </span><span> </span></p> </div>
Acer and Quercus root traits plasticity
<p><u><span>Introduction</span></u><span>: Plant nutrient acquisition strategies range along a spectrum from autonomous foraging to investment in cooperative foraging through mycorrhizal associations. However, in temperate ecosystems, </span><span>many plant species encounter contrasted levels of symbiont availability in open fields versus closed forests<span>. Little is known about how fungal partner availability may be associated with intraspecific variation in other root foraging traits in natural settings.</span></span></p> <p><u><span>Methods</span></u><span>: Here, we addressed this issue by sampling saplings from two tree species: the arbuscular mycorrhizal (AM) <em>Acer rubrum</em> and the ectomycorrhizal (ECM) <em>Quercus rubra</em> from open fields (AM-dominated) and adjacent forest plots (ECM-dominated). For each species and environment, we measured morphological, architectural, and symbiotic root traits.</span></p> <p><u><span>Results</span></u><span>: For the open field, <em>Quercus</em> had greater specific root length (SRL) while <em>Acer</em> had higher AM colonization and root diameter. In the closed forest, the opposite pattern was observed, namely <em>Quercus</em> had higher ECM colonization and <em>Acer</em> greater SRL. </span></p> <p><u><span>Discussion</span></u><span>: Both species showed evidence of a shift toward autonomous root foraging in the habitat with low expected symbiont abundance (open field for <em>Quercus</em> and forest for <em>Acer</em>). </span><span>Although the confounding effects of site abiotic properties could not be strictly controlled in this study, these results suggest that plants might adjust root foraging traits according to local habitat conditions.</span></p> <p><u><span>Synthesis</span></u><span>: Our results shed new light on the intraspecific variation in plant position along the so-called "collaboration gradient", and suggest that mycorrhizal symbiont availability, along with other factors such as competition and site properties, may contribute to this variation.</span></p>
Raw simulation results for Second-order homogenisation of crystal plasticity and martensitic transformation
<p>This dataset contains the raw simulation results used in the article entitled "Second-order homogenisation of crystal plasticity and martensitic transformation" (under review - 2023/05/15), by Igor A. Rodrigues Lopes, Miguel Vieira de Carvalho, João A. Marques da Silva, Rui P. Cardoso Coelho and Francisco M. Andrade Pires (INEGI and Faculty of Engineering of the University of Porto, Portugal).<br> These numerical results have been generated with the in-house finite element code LINKS.</p> <p>The dataset contains the datafiles, with information about the finite element mesh, plain text files with readable results, and vtu files that allow visualising the results in Paraview.</p>
Data for: Chronic exposure to odors at naturally occurring concentrations triggers limited plasticity in early stages of Drosophila olfactory processing
<p>In insects and mammals, olfactory experience in early life alters olfactory behavior and function in later life. In the vinegar fly <em>Drosophila</em>, flies chronically exposed to a high concentration of a monomolecular odor exhibit reduced behavioral aversion to the familiar odor when it is re-encountered. This change in olfactory behavior has been attributed to selective decreases in the sensitivity of second-order olfactory projection neurons (PNs) in the antennal lobe that respond to the overrepresented odor. However, since odorant compounds do not occur at similarly high concentrations in natural sources, the role of odor experience-dependent plasticity in natural environments is unclear. Here, we investigated olfactory plasticity in the antennal lobe of flies chronically exposed to odors at concentrations that are typically encountered in natural odor sources. These stimuli were chosen to each strongly and selectively excite a single class of primary olfactory receptor neuron (ORN), thus facilitating a rigorous assessment of the selectivity of olfactory plasticity for PNs directly excited by overrepresented stimuli. Unexpectedly, we found that chronic exposure to three such odors did not result in decreased PN sensitivity but rather mildly increased responses to weak stimuli in most PN types. Odor-evoked PN activity in response to stronger stimuli was mostly unaffected by odor experience. When present, plasticity was observed broadly in multiple PN types and thus was not selective for PNs receiving direct input from the chronically active ORNs. We further investigated the DL5 olfactory coding channel and found that chronic odor-mediated excitation of its input ORNs did not affect PN intrinsic properties, local inhibitory innervation, ORN responses, or ORN-PN synaptic strength; however, broad-acting lateral excitation evoked by some odors was increased. These results show that PN odor coding is only mildly affected by strong persistent activation of a single olfactory input, highlighting the stability of early stages of insect olfactory processing to significant perturbations in the sensory environment.</p>
Accompanying data for paper Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments
<p>Links</p> <ul> <li>Data DOI: <a href="https://doi.org/10.5281/zenodo.7729452">10.5281/zenodo.7729452</a></li> <li>Article <em>Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments</em>, DOI: <a href="https://doi.org/10.46298/jtcam.8913">10.46298/jtcam.8913</a></li> </ul> <p>Authors</p> <ul> <li>Xiang Kong, <a href="mailto:xiang.kong@minesparis.psl.eu">xiang.kong@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-0835-3826">0000-0002-0835-3826</a></li> <li>Jianqiang Chen, Pratt & Whitney Canada, 1000 Boul. Marie-Victorin, Longueuil, QC J4G 1A1 Canada</li> <li>Yazid Madi, <a href="mailto:yazid.madi@minesparis.psl.eu">yazid.madi@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-3530-8668">0000-0002-3530-8668</a></li> <li>Djamel Missoum-Benziane, <a href="mailto:djamel.missoum-benziane@minesparis.psl.eu">djamel.missoum-benziane@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-9877-8261">0000-0002-9877-8261</a></li> <li>Jacques Besson, <a href="mailto:jacques.besson@minesparis.psl.eu">jacques.besson@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0003-1975-2408">0000-0003-1975-2408</a></li> <li>Thilo F. Morgeneyer, <a href="mailto:thilo.morgeneyer@minesparis.psl.eu">thilo.morgeneyer@minesparis.psl.eu</a>, MINES Paris, PSL University, Centre des Matériaux, CNRS UMR 7633, Evry France, ORCID: <a href="https://orcid.org/0000-0002-0278-9565">0000-0002-0278-9565</a></li> </ul> <p>Language</p> <ul> <li>English</li> </ul> <p>Licence</p> <ul> <li>CC BY 4</li> </ul> <p>Contributions</p> <ul> <li>Conception and design of study, revising the manuscript critically for important intellectual content: TFM, JB.</li> <li>Acquisition of data: XK, JC, YM.</li> <li>Analysis and/or interpretation of data: XK, DMB, TFM.</li> </ul> <p>Associated article</p> <p>Xiang Kong, Jianqiang Chen, Yazid Madi, Djamel Missoum-Benziane, Jacques Besson, Thilo Morgeneyer "Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments" Journal of Theoretical, Computational and Applied Mechanics (JTCAM), March 13, 2023, DOI: <a href="https://doi.org/10.46298/jtcam.8913">10.46298/jtcam.8913</a>, HAL: <a href="https://hal.science/hal-03497233v3">hal-03497233v3</a></p> <p>Keywords</p> <ul> <li>plastic anisotropy</li> <li>mechanical testing</li> <li>non-proportional loading</li> <li>static loading</li> <li>ductile fracture</li> </ul> <p>Data collection: period and details</p> <ul> <li>Sept 2018 - June 2022, PhD period of Xiang Kong</li> <li>Mechanical experiments mainly were performed at the Centre des Materiaux in Evry, France, except the laminographic experiment which was performed at the ESRF ID19b in Grenoble, France, while numerical simulations were performed in <a href="http://www.zset-software.com/">Z-set/Zebulon</a> on the cluster at the Centre des Materiaux.</li> </ul> <p>Recommended citation line for the data</p> <p>Xiang Kong, Jianqiang Chen, Yazid Madi, Djamel Missoum-Benziane, Jacques Besson, & Thilo F. Morgeneyer. (2023). Accompanying data for paper Plasticity and ductility of an anisotropic recrystallized AA2198 Al-Cu-Li alloy in T3 and T8 conditions during proportional and non-proportional loading paths: simulations and experiments [Data set]. <a href="https://doi.org/10.5281/zenodo.7729452">10.5281/zenodo.7729452</a></p> <p>Funding sources</p> <ul> <li>ANR (Lambda project: ANR17-CE08-0051 and Alicandte project)</li> </ul> <p>Data structure and information</p> <ul> <li>The output data were used to produce Figures 6-9, 16-19, 23 from the associated article.</li> <li>Folder/files structure: <ul> <li><code>Experiments_Simulations_results/</code> <ul> <li><code>2198T3R/</code> - folder containing results for material AA2198T3R</li> <li><code>T3R_EXP_*.csv</code> - experimental data files</li> <li><code>T3R_SIM_*.csv</code> - simulation output</li> <li><code>README.md</code></li> <li><code>2198T8R/</code> - folder containing results for material AA2198T8R</li> <li><code>T8R_EXP_*.csv</code> - experimental data files</li> <li><code>T8R_SIM_*.csv</code> - simulation output</li> <li><code>README.md</code></li> <li><code>plot_T3R.py</code> - python script plotting results for material AA2198T3R</li> <li><code>plot_T8R.py</code> - python script plotting results for material AA2198T8R</li> <li><code>README.md</code></li> </ul> </li> <li><code>Simulation_input_files/</code> <ul> <li><code>2198T3R.mat,2198T8R.mat,steel.mat</code> - material properties files for Z-set/Zébulon</li> <li><code>postprocess.inp</code> - postprocessing input file for Z-set/Zébulon</li> <li><code>*.inp</code> - FE solver input files for Z-set/Zébulon</li> <li><code>post.py</code> - Python script to plot processed data</li> <li><code>README.md</code></li> <li><code>mesh_files/</code> <ul> <li><code>scale.inp</code> - Z-set/Zébulon mesh modifier</li> <li><code>ST_*.geof</code> - mesh files in Z-set/Zébulon format</li> <li><code>README.md</code></li> </ul> </li> </ul> </li> <li><code>.solidipes/</code> - curation tool metadata (not a part of the dataset)</li> </ul> </li> </ul>
Understanding and leveraging phenotypic plasticity during metastasis formation - Dataset
<p>This repo contains the dataset from the manuscript "Understanding and leveraging phenotypic plasticity during metastasis formation" by Shah et al. This repo contains `.npy`, `numpy.ndarray` files created by the Python software package NumPy. Please see the `README.md` in the code repository (https://doi.org/10.5281/zenodo.7989748) for installation and usage.</p>
The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity
<p>Raw data and code to generate the figures of the publication "The locus coeruleus broadcasts prediction errors across the cortex to promote sensorimotor plasticity" <a href="https://doi.org/10.7554/eLife.85111.2">https://doi.org/10.7554/eLife.85111</a>. </p>
Evolution of butterfly seasonal plasticity driven by climate change varies across life stages
<p>Photoperiod is a common cue for seasonal plasticity and phenology, but climate change can create cue-environment mismatches for organisms that rely on it. Evolution could potentially correct these mismatches, but phenology often depends on multiple plastic decisions made during different life stages and seasons that may evolve separately. For example, <em>Pararge aegeria</em> (Speckled wood butterfly) has photoperiod-cued seasonal life history plasticity in two different life stages: larval development time and pupal diapause. We tested for climate-change-associated evolution of this plasticity by replicating common garden experiments conducted on two Swedish populations 30 years ago. We found evidence for evolutionary change in the contemporary larval reaction norm—although these changes differed between populations—but no evidence for evolution of the pupal reaction norm. This variation in evolution across life stages demonstrates the need to consider how climate change affects the whole life cycle to understand its impacts on phenology.</p>
After the sun: A nanoscale comparison of the surface chemical composition of UV and soil weathered plastics data
<p>The present folder contains the raw data as well as the R script used in the following publication : <em>After the sun: A nanoscale comparison of the surface chemical composition of UV and soil weathered plastics. </em>To extract the data:</p> <p> </p> <ol> <li>Download the .zip file and extract all the files it contains in a folder, without changing the hierarchy</li> <li>Open the Rproject by double-clicking on it</li> <li>Run the script and explore the outputs in the <em>Script_01_output </em>folder</li> </ol> <p>The data extracted from the HDF5 files before any processing are saved as .csv files in the <em>CSV </em>folder.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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