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369 results for “droplet”
A droplet digital polymerase chain reaction assay to detect rare helminth parasites infecting natural host populations (Vancouver Island 2023, University of Wisconsin Madison Laboratory colony 2024)
Helminth infections represent a significant challenge to human, livestock, and wildlife health, yet they remain relatively under-studied, especially in terms of their ecological impacts. Better understanding of how these parasites spread in wildlife populations could improve our ability to predict and manage disease transmission across various species. Traditional detection methods, such as visually identifying parasites in environmental samples or infected hosts, often fall short, especially during the early stages of infection when parasite loads are minimal. In this study, we introduce a highly sensitive and precise droplet digital PCR (ddPCR) assay that quantifies helminth DNA in aquatic habitats, focusing on the 18S rRNA gene as a marker. These data utilize the model host-parasite system between the tapeworm Schistocephalus solidus, and its cyclopoid copepod host, Acanthocyclops robustus. The molecular assays are built around creating an infection standard in the lab, where copepods were singly infected with a single tapeworm parasite. We extracted DNA from 100 infected adults and used this as a standard to translate gene copy numbers from the ddPCR reactions to actual animal values. After creating a known lab standard, we then use the generated probes and primers to detect (and quantify!) infection burdens in field samples, which include both water filter samples (eDNA) and zooplankton tows from several lakes around Vancouver Island, B.C. The data presented here include well-specific data from ddPCR runs (amplitude of individual level oil droplets in the reaction) as well as each ddPCR analysis in its entirety. In order to prove the specificity of probes and probe-primers, we include here ddPCR runs of closely related helminth species, Schistocephalus cotti and Schistocephalus pungitii. We also consider the binding to another genera of copepod, the calanoid Eurytomora. All of the data wrangling, analysis, and data visualization are included as .Rmd files in th
Data for the 'Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation'
<p>All numerical data used in the manuscript <strong>“Evaluation of global simulations of aerosol particle number and cloud condensation nuclei, and implications for cloud droplet formation” </strong>by G. S. Fanourgakis et al. ACP (2019) are categorized and provided in a number of files. All files are in the hdf format. A readme file is also provided.</p> <p>These data files have been created by G. S. Fanourgakis (fanourg@uoc.gr)</p> <p>Details on the data are provided in Fanourgakis et al. Atmos. Chem. Phys. 2019 https://doi.org/10.5194/acp-2018-1340 (e-mail to <a href="mailto:mariak@uoc.gr">mariak@uoc.gr</a> ; <a href="mailto:athanasios.nenes@epfl.ch">athanasios.nenes@epfl.ch</a> )</p> <p>For an in-depth understanding of the description below, a study of the above mentioned manuscript is required.</p> <p>(A) Station model results</p> <p>The station results can be found in files with filenames of the form:</p> <p>station $MODEL.nc</p> <p>The “$MODEL” (as well as all names starting with “$”) indicates a variable, and more specifically one of the models participated in the present study. The values of this variable are tabulated in Table 1 in the readme file.</p> <p>In each file a number of computational results are provided by the specified model for all nine (9) stations that provided observational data. The name of the variable is formed as:</p> <p>st $STATION $FIELDhour st $STATION $FIELD month</p> <p>where all possible values of the variables $STATION and $FIELD are tabulated in Tables 2 and 3 in the readme file, respectively. The extension _hour denotes that hourly values for the field are provided, while the extension _month the monthly average of this quantity. For example, the variable</p> <p>st Finokalia CCN02 hour</p> <p>found in the file station_TM4-ECPL.nc, contains the hourly values of the CCN<sub>0<em>.</em>2 </sub>at the Finokalia station as computed by the TM4-ECPL model. In a similar way, in the file station_EMAC.nc, the variable below gives the monthly values of dust at Vavihill as computed with the EMAC model.</p> <p>st Vavihill DU month</p> <p>Notice also that in all files hourly and monthly data are provided for the time period from 1-1-2011 up to 31-12-2015 (60 months and 43,824 hours)</p> <p>(B) Station observational results</p> <p>There is one file that contains all observational data from Schmale et al., SCIENTIFIC DATA | 4:170003 | DOI: 10.1038/sdata.2017.3, 2017 (<a href="mailto:julia.schmale@psi.ch">julia.schmale@psi.ch</a>) and the data that were computed based on the observations (i.e. number of cloud droplets) (contact person: athanasios.nenes@epfl.ch). The file is</p> <p>station observations.nc</p> <p>while the following fields are contained in there:</p> <p>st $STATION $FIELDhour</p> <p>st $STATION $FIELD month</p> <p>The values of variables are given in the Tables 2 and 3 in the readme file. The time period covered is from 1-1-2011 up to 31-12-2015. Notice that due to the lack of observations a lot of data are missing. For missing observational data the value -9999.999 is given. Contact person for the observational data is Julia Schmale (julia.schmale@psi.ch).</p> <p>(C) Station Multi-model Median</p> <p>Monthly averages of the models can be found in the file</p> <p>station MMM.nc</p> <p>The following fields can be found in the file</p> <p>st $STATION $FIELD month median</p> <p>st $STATION$FIELD month quart25</p> <p>st $STATION$FIELD month quart75</p> <p>where the values of the variables $STATION and $FIELD can be found in Tables 2 and 3, respectively. The extension median corresponds to the multi-model median, while the quart25 and quart75 to the 25 % and 75 % quartiles, respectively.</p> <p>(D) Global model results</p> <p>In the following single file can be found for each of the models the surface distribution of various fields.</p> <p>results global models year2011.nc</p> <p>They correspond to the annual mean of the year 2011. The resolution of the grid is 1<sup>◦ </sup>× 1<sup>◦</sup>. The file contains the following variables:</p> <p>$FIELD $MODEL</p> <p>The $FIELD and $MODEL can be found in Tables 3 and 1, respectively.</p> <p>(E) Global average results</p> <p>In the file</p> <p>surface_ global_average_year2011.nc</p> <p>can be found in 5<sup>◦</sup>×5<sup>◦ </sup>resolution, the Multi-model median of surface distribution of the various fields denoted in Table 3 and their corresponding diversity. The names of the variables are formed as:</p> <p>med $FIELD</p> <p>div $FIELD</p> <p>where, ‘med’ stands for median and ‘div’ for diversity calculated as standard deviation divided by the mean of the model results.</p> <p>Tables and details on the fields provided are given in the readme file.</p>
Generated WSP: Validation of a water-sensitive paper-based method for the characterization of agricultural spray droplets
<p>Synthetic images were generated in a Python environment using the OpenCV library to replicate the distribution of droplets in WSP. The images display droplet stains represented by blue circles (255,0,0) on a yellow background (0,255,255) to enhance contrast and enable more precise analysis. The synthetic images were created in two distinct resolutions, namely 640x480 and 2560x1440 pixels, with the aim of reproducing the output of two specific digital microscopes: the Jiusion 640x480 and the Jiusion HD 2560x1440 (Shenzen, China). The resolution is chosen based on the expected practical application, ensuring that any image analysis algorithm developed can effectively process images with similar characteristics to those obtained under real conditions by these microscopes. Each pixel in this configuration corresponds to a physical size of 18.125 µm in images with a resolution of 640x480, and a size of 6.875 µm in images with a resolution of 2560x1440. Multiple patterns were created to simulate various configurations of droplet stains in WSP. The sizes of single droplet stains varied between 100 and 600 µm, with spacings of either 1000 µm or 2000 µm between drops (see attached figure). Furthermore, the same size range was utilised to generate patterns with double and overlaid droplet stains, with a consistent spacing of 2800 µm between each stain (see attached figure). The implementation of this systematic method guarantees the accurate calibration and application of image analysis algorithms in real-world situations. This allows for the representation of precise measurements and spacing that would be encountered in actual experimental conditions.</p>
Data and code related to the paper: "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation"
<p>This archive contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Kyriacos Yiannacou, Vipul Sharma and Veikko Sariola, "Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation", <em>Langmuir</em> 2022, 38, 38, 11557–11564.</p> <p><a href="https://doi.org/10.1021/acs.langmuir.2c01061">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>The acoustofluidic controller software is the same as in our previous paper and is archived <a href="https://doi.org/10.5281/zenodo.4593021">here</a>.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure(s) and/or movie(s). Within each folder, the raw data files are under the folder `data/`. Once ran, the scripts produce another folder called `output/`, to which they place the created plots and movies. Most folder contain a script name `plot_*.m` that makes the figure(s) and `video_*.m` that generates the video(s). You will need `ffmpeg` installed to convert the serial images into a video.<br> </p>
Output data for "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023)
<p>Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 2: strong surfactants" by Vepsäläinen et al. (2023).</p><p>Output data is included for 50 nm particles containing sodium myristate (c14na) and myristic acid (myristica), mixed with NaCl (nacl) in different surfactant mass fractions. Data about the critical points is also included for particles containing sodium myristate for particle size range 50-200 nm.</p><p>Plotters have been provided for the following:</p><ul><li>Part2_plotter_50_200_nm: Plots the critical supersaturations, diameters, and the relative change in cloud droplet concentrations for dry particles with 50-200 nm diameters containing c14na</li><li>Part2_plotter_50nm: Plots the Köhler curves, surface tension and partitioning factors for 50 nm particles containing c14na</li><li>Part2_plotter_50nm_myristica: Plots the Köhler curves and surface tensions for 50 nm particles containing myristica and also plots c14na for comparison (separate output files for the compounds and c14na data here is different than for the Part2_plotter_50nm plotter)</li></ul><p>Each plotter needs the user to set the location where the output files are stored. </p><p>In addition, a function is included:</p><ul><li>relative_change_in_cloud_droplet_number_conc: This function is called in "Part2_plotter_50_200_nm" and calculates the relative change in cloud droplet number concentration from the critical supersaturations.</li></ul>
Dataset 1 for Publication: Separation-dependent near-field effects in Mie scattering spectra of two optically trapped aerosol droplets
<p>Dataset for Publication: ASCII files of Mie spectra for each experimentally analysed run, calibrated wavelength files, and brightfield images at each interdroplet separation.</p>
Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface
<p>The data set for the submited publication "Effect of Surfactants on 1,2-Dichloroethane-in-Water Droplet Impacts at Electrified Liquid-Liquid Interface". </p>
Droplet-based Microfluidics Reveals Insights into Cross-Coupling Mechanisms over Single-Atom Heterogeneous Catalysts
<p>Data set supporting the publication of : "Droplet-based Microfluidics Reveals Insights into Cross-Coupling Mechanisms over Single-Atom Heterogeneous Catalysts" (<a href="https://doi.org/10.1002/anie.202401056">https://doi.org/10.1002/anie.202401056</a>) by T. Moragues, G. Giannakakis, A. Ruiz-Ferrando, C. N. Borca, T. Huthwelker, A. Bugaev, A. J. deMello, J. Pérez-Ramírez and S. Mitchell.</p>
Effect of sticky rice germ oil droplet spraying on chrysanthemum thrips resistance and metabolome
<p>This dataset contains experimental results from full plant assays with Chrysamthemum plants that were conducted to test the effectiveness of sprayng solutions containing sticky rice oil droplets for trapping of small arthropods on plants. The experiments were conducted at the Institute of Biology Leiden, Leiden University the Netherlands.</p> <p>The first dataset contains the results of the full plant assays with thrips.</p> <p>The second dataset contains the results of 1H NMR and GC-MS signals of leaf samples of sprayed chrysanthemum plants.</p> <p> </p> <p>Version history:</p> <p>Version 2: Included the RAW data on % coverage of plants for the two plant assays that had been left out during earlier submission</p> <p>Updated the metadatasheets within the excel files to be more complete.</p> <p>Version 3: Included a new excel sheet in the GC-MS and NMR data file in which a subset of the RAW HS-GC-MS and 1H NMR data, namely those peaks and delta signals that were identified and matchedd to compound id after untargeted analysis, are presented together with the name of the compounds or classes of compounds as mentioned in the manuscript.</p> <p>No changes were made to the plant assay data file</p> <p> </p> <p>In the "Dataset_TBierman_RGO_thrips_1HNMR_GC-MS_V3" excel file:</p> <p>Sheets: "Processed 1H NMR data" and "Processed HS-GC-MS data"</p> <p>contain processed 1H NMR and GC-MS data of chrysanthemum leaves, harvested after 10 or 25 days, of plants that were sprayed with water or vegetable-oil derived adhesives and infested with thrips or not.</p> <p>Sheet: "Quantitative data selected comp" contains a subset of the data where signals were found significant in the untargeted analysis have been annotated to their compound identity.</p> <p>In the "Dataset_TBierman_RGO_thrips_plantassay1_and_2_V3" excel file:</p> <p>Sheets "Plant_assay_1_RGO_thrips_d10_25" and "Plant_assay_2_RGO_thrips_d25" contain the raw plant assay data</p> <p>Sheets "Plant_assay_1_RGO_coverage" and "Plant_assay_2_RGO_coverage" contain the summary values of the estimated coverage with adhesive oil droplets of each respective experiment on the left side while on the right side the raw data is presented </p> <p> </p>
Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022)
<p>Output data of the different models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022).</p> <p>Output data is included for 50 nm particles containing malonic acid (mna), succinic acid (sca) and glutaric acid (glutarica), mixed with ammonium sulphate (AS) in different organic mass fractions. </p> <p>A plotter that allows the user to plot the Köhler curves, surface tensions and organic<br> partitioning factors during droplet growth from the model output data provided is included. </p>
Molecular Dynamics simulations of spreading droplets
<p>This dataset contains the results of non-equilibrium Molecular Dynamic simulations of 2-dimensional SPC/E water nanodroplets spontaneously spreading over silica-like walls, performed using Gromacs. The main purpose of these simulations is to study the motion of three-phases contact lines over high-friction surfaces and to test contact line friction models.</p> <p>Further details can be found in 'documentation.pdf'.</p>
Molecular Dynamics simulations of shear droplets
<p>This dataset contains the results of non-equilibrium Molecular Dynamic simulations of 2-dimensional SPC/E water nanodroplets confined between silica-like walls and under shear flow conditions, performed using Gromacs. The main purposes of these simulations are: a) to study the motion of three-phases contact lines over high-friction surfaces, b) to study the critical transition leading to droplet breakage and c) to test the modelling and prediction capabilities of continuous fluid dynamics simulation methods. The investigation of the points above is illustrated in an article, which has been digitally published on the Journal of Fluid Mechanics (doi:10.1017/jfm.2022.219, see references); please refer to the paper for a detailed description of the molecular simulations and of the tested CFD methods. The publication of this dataset not only grants the reproducibility of the results discussed in the article, but also serves as collection of benchmarks for the fellow researchers willing to test improved and/or alternative models to describe the motion of contact lines.</p>
Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe: Measurement Data
<p>This repository contains the measurement data that was used for the publication "Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe".</p>
Dataset for "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport"
<p>Dataset for figures and tables of the article "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport" submitted to "International Journal of Multiphase Flow".</p>
Light and confocal micrographs on the response of Mesotaenium endlicherianum SAG 12.97 to a bifactorial environmental gradient, the accumulation of lipid droplets, and the heterologous expression and localisation of signature LD protein homologs to tobacco pollen tubes
<p>These micrographs accompany the work "Environmental gradients reveal stress hubs predating plant terrestrialization", posted as a pre-print on bioRxiv https://doi.org/10.1101/2022.10.17.512551 </p> <p>The light and confocal micrographs show the response of Mesotaenium endlicherianum SAG 12.97 to a bifactorial environmental gradient, especially their accumulation of lipid droplets (LDs); in confocal micrographs, LDs appeared as distinct structures upon staining with BODIPY.</p> <p>Further confocal micrographs show the heterologous expression and localisation of signature LD protein homologs detected in Mesotaenium endlicherianum SAG 12.97; heterologous expression was carried out in tobacco pollen tubes were also stained with BODIPY and proteins were tagged with mCherry.</p>
LIF-based quantification of the species transport during droplet impact onto thin liquid films (Dataset)
<p>This database includes Supplementary Data and videos for <em>Experiments in Fluids </em>manuscript: LIF-based quantification of the species transport during droplet impact onto thin liquid films.</p> <p>Number of figure in the file name is changed:</p> <ul> <li>fig.2 to fig. 3</li> <li>fig.3 to fig. 4</li> <li>fig.4 to fig. 5</li> <li>fig.5 to fig. 6</li> <li>fig.8 to fig. 11</li> <li>fig.9 to fig. 12</li> <li>fig.10 to fig. 13</li> <li>fig.11 to fig. 14</li> <li>fig.12 to fig. 15</li> <li>fig.13 to fig. 16</li> <li>fig.14 to fig. 17</li> </ul> <p> </p>
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
Piston-Expansion-Tube Videos Showing Droplet Evaporation
<p>Both videos show high speed recordings of the spontaneous condensation of small droplets of homogeneous size after rapidly expanding a cylindrical volume. After the expansion and droplet creation, the heat of the walls is evaporating the droplets again. The cameras optical axis is oriented parallel to the cylinder axis. A small square area of 8.5 mm side length next to the cylindrical wall is recorded. The particles are illuminated by a laser sheet of about 0.5 mm thickness. The recording frequency is 500 Hz.</p> <p>height/depht of the cylinder: 0.021 m<br> radius of the cylinder: 0.035 m<br> gas: Nitrogen<br> condensing component: n-Propanol<br> gas temperature before expansion: 299.0 K<br> cylinder inner surface temperature: 299.0 K<br> pressure before expansion: 1.0E5 Pa<br> pressure past espansion: 0.55E5 Pa<br> partial pressure of condensing component before expansion: 628 Pa<br> droplet radius past expansion: 100E-9 m</p> <p>Video 5E12nuclei.mp4:</p> <p>droplet concentration: 5E12 1/m³ (rough estimation)</p> <p>Video 8E10nuclei.mp4:</p> <p>droplet concentration: 8E10 1/m³ (counted in one of the video frames)</p> <p>The two different droplet concentrations are achieved solely by the speed of the expansion. The higher the speed, the more nuclei will be created.</p> <p>For more information see <a href="https://doi.org/10.5281/zenodo.4449246">https://doi.org/10.5281/zenodo.4449246</a> (diploma thesis, German language).</p>
Dataset for "Indoor transmission of respiratory droplets under different ventilation systems using Eulerian approach"
<p>Dataset for figures and tables of the article "Indoor transmission of respiratory droplets under different ventilation systems using Eulerian approach".</p>
Quantifying local stiffness and forces in soft biological tissues using droplet optical microcavities
<p>Dataset for publication Quantifying local stiffness and forces in soft biological tissues using droplet optical microcavities</p>
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OpenNeuro
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