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105 results for “Mixture effects”
Effects of intercropping on the herbage production of a binary grass-legume mixture (Hedisarum coronarium L. and Lolium multiflorum Lam.) under artificial shade in Mediterranean rainfed conditions
<p>This dataset refers to the experimental raw data (csv version) collected within the trial reported in the concerned article on the following parameters:</p> <p>1. crop aboveground biomass, splitted per field, mowing, crop, treatment and replicate (crop aboveground biomass.csv)</p> <p>2. cumulated crop aboveground biomass, splitted per field, year, crop, treatment and replicate (cumulated crop aboveground biomass_year.csv)</p> <p>3. cumulated crop aboveground biomass for the two years of the growing cycle, splitted per field, crop, treatment and replicate (cumulated crop aboveground biomass_2years.csv)</p> <p>4. partial and total RYT splitted per year, field and treatment (RYT_year)</p> <p>5. partial and total RYT for the two years of the growing cycle, splitted per field and treatment (RYT_2years)</p> <p> </p> <p><br> </p>
Variations in tree growth provide limited evidence of species mixture effects in Interior West U.S.A. mixed-conifer forests
<p>1. In mixed stands, species complementarity (e.g., facilitation and competition reduction) may enhance forest tree productivity. Although positive mixture effects have been identified in forests worldwide, the majority of studies have focused on two-species interactions in managed systems with high functional diversity. We extended this line of research to examine mixture effects on tree productivity across landscape-scale compositional and environmental gradients in the low functional diversity, fire-suppressed, mixed-conifer forests of the U.S. Interior West.</p> <p>2. We investigated mixture effects on the productivity of <i>Pinus ponderosa</i>, <i>Pseudotsuga menziesii</i>, and <i>Abies concolor</i>. Using region-wide forest inventory data, we created individual-tree generalized linear mixed models and examined the growth of these species across community gradients. We compared the relative influences of stand structure, age, competition, and environmental stress on mixture effects using multi-model inference. We analyzed growth of neighboring tree species to infer whether a mixture effect in a single species translated to the stand-level.</p> <p>3. We found support for a positive mixture effect in <i>P. menziesii</i>, although our results were equivocal in light of a weaker but still plausible alternative model. Growth of <i>P. menziesii</i> neighboring species in mixed stands declined or held constant depending on aridity, suggesting that a positive mixture effect in <i>P. menziesii</i> does not necessarily extend to the stand level. We found no evidence for mixture effects in <i>P. ponderosa</i>, <i>A. concolor</i> or their neighboring species.</p> <p>4. Complementarity appears to have a limited influence on tree growth in the mixed-conifer systems of the U.S. Interior West, reflecting limited functional diversity. Historical changes in stand structure following fire exclusion, particularly high stand densities, may limit the potential for positive species mixture effects. The limited species pool of Interior West forests increases the risk that, without careful management, what functional diversity exists could be lost to compositional changes resulting from stand dynamics or disturbance.</p>
Trait functional diversity explains mixture effects on litter decomposition at the arid end of a climate gradient
<p><span>Litter decomposition is controlled by climate, litter quality and decomposer communities. Because the decomposition of specific litter types is also influenced by the properties of adjacent types, mixing litter types may result in non-additive effects on overall decomposition rates. The strength of these effects seems to depend on the litter functional diversity. However, it is unclear which functional traits or combination of traits explain litter mixture effects and if these depend on the range of trait values and the ecosystems involved. These uncertainties hamper our ability to predict decomposition in plant communities. </span></p> <p><span>We aimed at understanding whether and how functional diversity (measured as functional dispersion, FDis) influences litter decomposition, and how this influence varies among different climates and across decomposition stages. We calculated FDis based on litter traits related to nutrient concentrations or to litter recalcitrance, and tested whether these diversity measures and climatic parameters (soil moisture and temperature) explained litter mixture effects on decomposition. </span></p> <p><span>Additive mixture effects (i.e. decomposition of mixtures equalling the mean decomposition of the single litter types) were common in most of the evaluated climates. Non-additive, negative effects were mainly restricted to the driest and warmest sites, and decreased with time. Non-additive effects increased in magnitude with the mixtures' FDis, with positive effects being related to FDis in nutrient traits and negative effects being related to FDis in recalcitrance traits. </span></p> <p><span>Synthesis: Litter mixing did not have strong effects on decomposition rates across the studied climatic gradient overall, and the direction and intensity of the mixture effects were context-dependent. The effects were stronger and more negative in the dryer ecosystems. Where effects were found, functional diversity calculated from selected groups of traits (related to nutrients or litter recalcitrance) predicted mixture effects, especially where trait ranges were broad, though much of the variation remains unexplained. We propose that functional diversity metrics based on litter traits that are mechanistically relevant, applied to diverse site-specific litter mixtures in different climates, can help to better understand under which conditions and in which direction litter diversity affects decomposition.</span></p>
Data, plotting scripts, and figures for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures"
<p>This bundle of files contains all the data and plotting scripts for "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures", as well as the figures themselves.</p> <p>These results are part of the paper:</p> <p>Tejas Chandrashekhar Mulky and Kyle E. Niemeyer. "Computational study of the effects of density, fuel content, and moisture content on smoldering propagation of cellulose and hemicellulose mixtures," 2018. Accepted for publication in <em>Proceedings of the Combustion Institute</em>, available via <a href="https://arxiv.org/abs/1806.08396">https://arxiv.org/abs/1806.08396</a></p>
Data from: "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat"
<p><em><strong>Data and code used for the study : "From cultivar mixtures to allelic mixtures: opposite effects of allelic richness between genotypes and genotype richness in wheat".</strong></em></p> <p>The script "Manuscript_Analyses.R" contains all code for the statistical analysis presented in the manuscript (main text & supplementary information). This script uses files produced in the folder "Locus-by-locus analysis" as inputs, and "manhattan_custom.R" as a source function ("manhattan_custom.R" is used to highlight SNPs in a given interval and to write specified SNPs name on manhattan plots). The file "Traits_monocultures.csv" contains the 20 functional traits measured on the 179 monoculture plots (see Supplementary Methods for more information on trait measurement). This file is used as an input in the script "Manuscript_Analyses.R".</p> <p>The "Locus-by-locus analysis" folder contains all analyses conducted to test the effect of allelic richness on the four variables of interest: Grain Yield (GY, g/m²), Spike Number per m² (SNb, nb spikes/m²), Thousand Kernel Weight (TKW, g), and Septoria tritici blotch (STB) severity. The locus-by-locus analysis is performed with the script "Allelic_richness_locus_by_locus_analysis.R". This analysis generates a list of .csv files with one file per chromosome. Each file contains the pvalues and estimated effect sizes of the tested SNPs for the given chromosome. These output files are stored in folders named after the variables for which the effect of allelic richness was tested ("RAW_GY", "RAW_SNb", "RAW_TKW", and "RAW_severity"). The script "Allelic_richness_locus_by_locus_output_processing.R" combines all .csv files into a single dataframe and produces three diagnostic plots: Manahattan plots, histograms of p-value distributions, and p-value q-q plots. p-value thresholds were computed based on a Family-Wise Error Rate of 5% using the Galwey correction. This is done in the "pvalue_thresholds" folder with the "Meff_computation.R" script. "Meff_computation.R" uses the "Meff_function.R" as a source function and generates "GY_thresholds.csv" and "STB_thresholds.csv" as outputs (these files contains different thresholds computed according to different methods but we only retained the Galwey method (most recent) for the analyses. Since GY, SNb, and TKW were analyzed with the same number of SNPs (~19K), we used the same significance threshold for the three variables ("GY_thresholds.csv"), whereas we computed a different thresholds for STB ("STB_thresholds.csv") for which we could only include ~6K SNPs in the analysis. The "geno_pos.csv" file contains the physical positions of the SNPs.</p> <p>Upstream the locus-by-locus analysis, phenotypic and genotypic files are prepared in the "Phenoytpic file preparation" and "Genotypic file preparation" folders, respecively.</p> <p>The phenotypic file preparation includes the correction of yield-related variables (GY, SNb, and TKW) for spatial auto-correlation in the "Spatial_analyses_YLD_variables" folder, and the computation of plot-level variables from individual-level variables with the "Allelic_richness_phenotypic_file_prep.R" script. In this script, we compute both absolute plot values (termed "RAW_...) and relative plot values (termed "RYT_..., only for mixture plots). All phenotypic files have the same structure with the same first 6 columns: "focal" = identity of the focal genotype (the one for which the variable is measured, only relevant for variables measured at the individual-level), "neighbor" = identity of the neighbor genotype (the neighbor of the genotype for which the variable is measured, only relevant for variables measured at the individual-level), "pair" = identity of the genotypic pair (combines the identity of the focal and the neighbor genotypes), "assoc" = type of plot ("M" = monoculture or pure stand plot, "P" = mixture plot), "row" = position of the plot along the smallest dimension of the grid (see Figure 1), "column" = position of the plot along the largest dimension of the grid (see Figure 1).</p> <p>The genotypic file preparation is done with the "Allelic_richness_genotypic_file_prep.R" script and includes SNP filtering, computation of matrices of allelic richness, and computation of matrices of genetic similarity between genotypic pairs. The analysis is done separatly for yield-related variables and for STB severity since the two types of variable were not measured on the same set of plots.</p>
Supporting information for "Cosolvent effects on the structure and thermoresponse of a polymer brush: PNIPAM in DMSO-water mixtures"
<p>This deposition contains the data and analysis (Jupyter notebooks) detailed in "Cosolvent effects on the structure and thermoresponse of a PNIPAM brush”. All Jupyter notebooks have also been converted into PDF files for ease of viewing.</p> <p>All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data_analysis.zip” archive. This archive contains three sub-directories:</p> <ul> <li>FTIR <ul> <li>FTIR transmission data of binary DMSO-water mixtures as a function of solvent composition.</li> <li>FTIR deconvolution was performed using software readily available at <a href="https://github.com/haydenrob/spec_deconv">https://github.com/haydenrob/spec_deconv</a>.</li> </ul> </li> <li>Ellipsometry <ul> <li>Data directory containing all raw ellipsometry data.</li> <li>“refellips_Spectroscopic_SL.ipynb” notebooks to reproduce the analysis of a hydrated (solid-liquid) polymer brush. Relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips</a> repo.</li> <li>A spatial map of the polymer brush used for spectroscopic ellipsometry data analysis: “surface_map.png”.</li> <li>“Ellipsometry_logistical_fitting.ipynb” notebook and “DMSO_6mol_results.csv” file for the demonstration of the extraction of a thermotransition temperature from an ellipsometry dataset.</li> </ul> </li> <li>Neutron_reflectometry <ul> <li>Data directory containing all relevant reduced reflectivity profiles from the Platypus reflectometry at ANSTO.</li> <li>“refnx_dry.ipynb” and “refnx_solvent.ipynb” notebooks required to reproduce the analysis pertaining to a dry polymer brush and a solvated brush, respectively.</li> <li>Additional code required to model the hydrated polymer brush and various plotting tools can be in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox</a> repo.</li> </ul> </li> </ul>
Additive and dose-dependent mixture effects of Flumite 200 (flufenzin, acaricide) and Quadris (azoxystrobin, fungicide) on the reproduction and survival of Folsomia candida (Collembola)
<p> Our model organism was Folsomia candida (Collembola). We aimed to gain information on the toxicity of Quadris (azoxystrobin) and Flumite 200 (flufenzine aka. diflovidazine) on survival and reproduction and whether the animals can mitigate the toxicity with soil and/or food avoidance behaviour. Also, we aimed to test the effect of the mixture of these two pesticides. We used the OECD 232 reproduction test, a soil avoidance test, and a food choice test for both single pesticides and their mixture. We prepared the mixtures based on the concentration addition model, so the 50% effective concentrations (EC50) of the single materials were used as one toxic unit with a constant ratio of the two materials in the mixture. In the end, the measured mixture EC and LC (lethal concentration) values were compared to the estimate of the concentration addition model.</p>
Analysis of Effective Stiffness and Anisotropy of AC 16 Asphalt Mixture within NCN project Weave-UNISONO 2021, project No 2021/03/Y/ST8/00079
<p><strong>Summary</strong>:</p> <p>The internal structure of the AC 16 (asphalt concrete mixture) was divided into the mortar phase and the mineral aggregate phase. Static creep tests using the Bending Beam Rheometer were conducted for the mortar phase to fit the rheological model. The aggregate arrangement and orientation were analysed using ImageJ software for the mineral phase. The Finite Element Method (FEM using ABACUS software) meshes were prepared based on images with an assumption of plane strain in 2D formulation. Using the FEM model, the tension/compression tests using selected characteristic directions were conducted, and the effective constrained stiffness moduli were estimated.</p> <p><strong>The dataset includes:</strong></p> <ul> <li>TIFF input and output image of AC16 lateral surface, txt output results file <ul> <li>xz_AC_16 lateral surface_areas colour.tiff</li> <li>xz_AC_16 lateral surface.tiff</li> <li>xz_AC_16 lateral surface ImageJ - results.txt</li> </ul> </li> <li>grey TIFF image for plot profile <ul> <li>grey image for plot profile.tif</li> </ul> </li> <li>BBR test results, CSV raw data <ul> <li>sample 1 mortar.csv</li> <li>sample 2 mortar.csv</li> </ul> </li> <li>Input images: scan in xy plane and scan in xz plane <ul> <li>xy_AC_16 mel-dol.tif</li> <li>xy_AC_16.tif</li> <li>xy_AC_16 ImageJ - results.txt</li> <li>xz_AC_16 mel_dol.tif</li> <li>xz_AC_16.tif</li> <li>xz_AC_16 ImageJ - results.txt</li> </ul> </li> <li>Abaqus Input Files – horizontal and vertical tension <ul> <li>xy_AC_16_horizontal_tension.txt</li> <li>xy_AC_16_vertical_tension.txt</li> <li>xz_yz_AC_16_horizontal_tension.txt</li> <li>xz_yz_AC_16_vertical_tension.txt</li> </ul> </li> </ul>
Variations in tree growth provide limited evidence of species mixture effects in Interior West U.S.A. mixed-conifer forests
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Species mixture effects and climate influence growth, recruitment and mortality in Interior West U.S.A. Populus tremuloides - conifer communities
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Trait functional diversity explains mixture effects on litter decomposition at the arid end of a climate gradient
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Data from: Functional diversity enhances, but exploitative traits reduce tree mixture effects on microbial biomass
1. Soil microorganisms play key roles in terrestrial biodiversity and ecosystem functions. Despite recent progress in elucidating the association between plant diversity and soil microorganisms, it remains unclear whether the functional properties of plant mixtures might alter this association. 2. We examined whether the effects of tree species mixtures on soil microbial biomass were impacted by the functional diversity (FD) and community-weighted-mean (CWM) of tree mixtures, by conducting a global meta-analysis involving 123 paired observations of tree mixtures and the corresponding monocultures from 38 studies in forests. 3. We found that the tree mixture effect on microbial biomass increased with the FD of specific leaf area (SLA), leaf N and P content, as well as the FD based on all of these traits plus leaf dry matter content. Meanwhile, the responses of microbial biomass to tree mixtures decreased with the CWM of SLA, leaf N, and P content. The effects of FD and CWM remained consistent, despite variable tree species richness, stand age and climatic factors. 4. Our results provide a new insight that the functional properties of plants may alter the magnitude of the association between plant diversity and soil microorganisms.
Effects of three-dimensional soil heterogeneity and species composition on plant biomass and biomass allocation of grass-mixtures
<p>Soil heterogeneity significantly affects plant dynamics such as plant growth and biomass. Most studies developed soil heterogeneity in two dimensions, i.e. either horizontally or vertically. However, soil heterogeneity in natural ecosystems varies both horizontally and vertically i.e. in three dimensions. Previous studies on plant biomass and biomass allocation rarely considered the joint effects of soil heterogeneity and species composition. Thus, to investigate such joint effects on plant biomass and biomass allocation, a controlled experiment was conducted, where three levels of soil heterogeneity and seven types of species compositions were applied. Such soil heterogeneity was developed by filling nutrient-rich and nutrient-poor substrates in an alternative pattern in pots with different patch sizes (small, medium or large), and species compositions was achieved by applying three plant species (i.e. Festuca elata, Bromus inermis, Elymus breviaristatus) in all possible combinations (growing either in monoculture or in mixtures). Results showed that patch size significantly impacted plant biomass and biomass allocation, which differed among plant species. Specially, at the pot scale, with increasing patch size, shoot biomass decreased, while root biomass and R: S ratio increased, and total biomass tended to show a unimodal pattern, where the medium patch supported higher total biomass. Moreover, at the substrate scale, more shoot biomass and total biomass were found in nutrient-rich substrate. Furthermore, at the community scale, two of the three target plant species growing in monoculture had more shoot biomass than those growing together with other species. Thus, our results indicate soil heterogeneity significantly affected plant biomass and biomass allocation, which differ among plant species, though more research is needed on the generalization on biomass allocation. We propose that soil heterogeneity should be considered more explicitly in studies with more species in long-term experiments.</p>
A synchrotron X-ray scattering study of the crystallization behavior of mixtures of confectionary triacylglycerides: effect of chemical composition and shear on polymorphism and kinetics
<p>Processed and raw data associated at the publication: <a href="https://www.sciencedirect.com/science/article/pii/S0963996923014126" target="_blank" rel="noopener">A synchrotron X-ray scattering study of the crystallization behavior of mixtures of confectionary triacylglycerides: effect of chemical composition and shear on polymorphism and kinetics - ScienceDirect</a></p>
Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact
<p>This repository includes characterization data (H<sub>2</sub>-O<sub>2</sub> titration and N<sub>2</sub> adsorption t-plot) associated with the publication: N.Korica, A.Ben Hassine, H.Dao Thi, L.Bergaoui, K.Van Geem, P.S.F.Mendes, J.De Clercq, J.W.Thybaut “Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact”, submitted to Fuel journal in December 2021.</p> <p>The impact of carbon number of reacting alkanes and cycloalkanes on mixture effects in hydroconversion over Pt/HUSY has been studied by experiments performed on high-throughput setup over Pt/HUSY catalyst with three different Pt loadings and HUSY zeolite with Si/Al molar ratio of 6. The dispersion of platinum over HUSY zeolite was determined by H<sub>2</sub>-O<sub>2</sub> titration, while the specific surface area was determined by t-plot N<sub>2</sub> adsorption. These data are classified based on the platinum loading and the characterization method.</p> <p>0.3 wt%Pt (H2-O2 titration) : The dispersion of 0.3 wt%Pt/HUSY</p> <p>0.3 wt%Pt (N2 ads t-plot) : The specific surface area of 0.3 wt%Pt/HUSY</p> <p>0.1 wt%Pt (H2-O2 titration) : The dispersion of 0.1 wt%Pt/HUSY</p> <p>0.1 wt%Pt (N2 ads t-plot) : The specific surface area of 0.1 wt%Pt/HUSY</p> <p>0.07 wt%Pt (H2-O2 titration) : The dispersion of 0.07 wt%Pt/HUSY</p> <p>0.07 wt%Pt (N2 ads t-plot) : The specific surface area of 0.07 wt%Pt/HUSY</p>
Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact
<p>This repository includes experimental data associated with the publication: N.Korica, A.Ben Hassine, H.Dao Thi, L.Bergaoui, K.Van Geem, P.S.F.Mendes, J.De Clercq, J.W.Thybaut “Mixture effects in alkane/cycloalkane hydroconversion over Pt/HUSY : carbon number impact”, submitted to Fuel journal in December 2021.</p> <p>The impact of carbon number of reacting alkanes and cycloalkanes on mixture effects in hydroconversion over Pt/HUSY has been studied by experiments on high-throughput setup by feeding equimolar mixtures of n-octane and tert-butylcyclohexane, and n-decane and methylcyclohexane. In order to investigate the above-mentioned impact, the kinetic behavior was examined at various experimental conditions, over Pt/HUSY catalyst with three different Pt loadings and HUSY zeolite with Si/Al molar ratio of 6.</p> <p>The process conditions which were used for every feed are summarized:</p> <ul> <li>Pure n-octane <ul> <li>Catalysts : 0.07 and 0.1 wt%Pt/HUSY (poorly- and well-balanced catalyst for pure <em>n</em>-octane)</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Pure tert-butylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Equimolar mixture of n-octane and tert-butylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of each reactant, bar : 0.05</li> </ul> </li> <li>Pure n-decane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY (poorly- and well-balanced catalyst for pure <em>n</em>-decane)</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Pure methylcyclohexane <ul> <li>Catalysts : 0.07 and 0.1 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of reactant, bar : 0.05</li> </ul> </li> <li>Equimolar mixture of n-decane and methylcyclohexane <ul> <li>Catalysts : 0.07 and 0.3 wt%Pt/HUSY</li> <li>Temperature, K : 523 ; 543</li> <li>Pressure, bar : 10 ; 20</li> <li>Partial pressure of each reactant, bar : 0.05</li> </ul> </li> </ul> <p>The kinetics of hydroconversion of different alkane/cycloalkane feeds were compared based on conversion of reactants and yields to isomers. The data are classified based on figures in the Article.</p> <p>Figure 5 : <em>n</em>-Octane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure 6 : Octane isomer yields as a function of <em>n</em>-octane conversion - comparison of experiments with pure <em>n</em>-octane and in mixture with tert-butylcyclohexane</p> <p>Figure 7 : tert-Butylcyclohexane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure 8 : Butylcyclohexane isomer yields as a function of tert-butylcyclohexane conversion comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure 9 : <em>n</em>-Decane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure 10 : Decane isomer yields as a function of <em>n</em>-decane conversion - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure 11 : Methylcyclohexane conversion as a function of space time at 10 bar pressure - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p> <p>Figure S9 : <em>n</em>-Octane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure S10 : <em>n</em>-Octane conversion as a function of space time - comparison of experiments with pure <em>n</em>-octane and in mixture with methylcycyclohexane and tert-butylcyclohexane</p> <p>Figure S13 : tert-Butylcyclohexane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure S14 : tert-Butylcyclohexane conversion as a function of space time - comparison of experiments with pure tert-butylcyclohexane and in mixture with <em>n</em>-octane</p> <p>Figure S15 : <em>n</em>-Decane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure S16 : <em>n</em>-Decane conversion as a function of space time - comparison of experiments with pure <em>n</em>-decane and in mixture with methylcyclohexane</p> <p>Figure S17 : Methylcyclohexane conversion as a function of space time at 20 bar pressure - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p> <p>Figure S18 : Methylcyclohexane conversion as a function of space time - comparison of experiments with pure methylcyclohexane and in mixture with <em>n</em>-decane</p>
Development of Cost-Effective High-Modulus Asphalt 5. Report Date Aug. 2021 Concrete (HMAC) Mixtures Using Crumb Rubber and Local Construction Materials in Louisiana
<p>One of the emerging solutions to enhance the durability of asphalt pavements is the use of a French asphalt mix<br> known as “High-Modulus Asphalt Concrete (HMAC).” This mix uses a hard asphalt binder, high binder content<br> (about 6%), and low air voids content as compared to Superpave mixtures. The key objective of this study was<br> to develop a cost-effective HMAC mixture using crumb rubber and local materials in Louisiana. To achieve this<br> objective, four HMAC mixtures were prepared using two asphalt binders (PG 82-22 and PG 76-22 plus 10%<br> crumb rubber) and two Reclaimed Asphalt Pavement (RAP) contents (20% and 40%); additionally, a<br> conventional Superpave mixture in Louisiana was prepared as a control mixture. The laboratory performance<br> of these five mixtures was evaluated in terms of workability, dynamic modulus, rutting resistance, and cracking<br> resistance. The AASHTOWare Pavement ME Design software was also used to estimate the long-term field<br> performance of these mixtures. Results indicated that the HMAC mixture prepared with 10% crumb rubber and<br> 20% RAP successfully met the French mix design specifications for HMAC and LaDOTD specifications. This<br> HMAC mix outperformed the control Superpave mix in terms of dynamic modulus, rutting resistance, and<br> cracking resistance. Additionally, this HMAC mixture can reduce the required asphalt thickness by 1.5 or 2<br> inches based on traffic level. The cost-effectiveness analysis indicated that this HMAC mixture was more costeffective<br> than conventional Superpave mixtures in Louisiana. In addition, this mixture is environmentallyfriendly<br> since it can reduce the disposal of scrap tires in landfills.</p>
The effect of conditions of thermal treatment of Ti+Al+C mixture on the formation of MAX phases
<p>Datasets for article The effect of conditions of thermal treatment of Ti+Al+C mixture on the formation of MAX phases. </p>
The effects of N-addition on litter mixture effects depend on decomposition time: a case from mixed-litter decomposition in the Gurbantunggut Desert
<p>Changes in nitrogen (N) deposition and litter mixtures have been shown to influence ecosystem processes such as litter decomposition. However, the interactive effects of litter mixing and N-deposition on decomposition process in desert regions remain poorly identified. We assessed the simultaneous effects of both N addition and litter mixture on mass loss in a litterbag decomposition experiment using six native plants in single-species samples with diverse quality and 14 species-combinations in the Gurbantunggut Desert under two N addition treatments (control and N addition). The N addition had no significant effect on decomposition rate of single-species litter (except <em>Haloxylon ammodendron</em>), whereas litter mass loss and decomposition rate differed significantly among species, with variations positively correlated with initial phosphorus concentration and negatively correlated with initial lignin concentration. After 18 months, the average mass loss across litter mixtures did not overall differ from those predicted from single-species either in control or N addition treatments, that is, mixing of different species had no non-additive effects on decomposition. The N addition, however, did modify the direction of mixture effects, and interacted with incubation time. Added N transformed synergistic effects of litter mixtures to antagonistic effects on mass loss after 1 month of decomposition, while transforming neutral effects of litter mixture to synergistic effects after 6 months of decomposition. Our results demonstrated that initial chemical properties played an important role in litter decomposition, while no effects of litter mixture on decomposition process in this desert region. The N addition altered the litter mixture effects on mass loss with incubation time, implying that increased N deposition in the future may have profound effects on carbon turnover to a greater extent than previously thought in desert ecosystems.</p>
The effects of N-addition on litter mixture effects depend on decomposition time: a case from mixed-litter decomposition in the Gurbantunggut Desert
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.