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14 results for “Droplet Dynamics”
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>
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>
Droplet digital PCR (ddPCR) as a tool for investigating dynamics of cryptic symbionts
<p>Interactions among symbiotic organisms and their hosts are major drivers of ecological and evolutionary processes. Monitoring the infection patterns among natural populations and identifying factors affecting these interactions is critical for understanding symbiont-host relationships. However, many of these interactions remain understudied since the knowledge about the symbiont species is lacking and hinders the development of appropriate tools. In this study, we developed a digital droplet PCR (ddPCR) assay based on apicomplexan COX1 gene to detect an undescribed agamococcidian symbiont. We show that the method gives precise and reproducible results and enables detecting cryptic symbionts in low target concentration. We further exemplify the assay's use to survey seasonally sampled natural host (Pygospio elegans) populations for symbiont infection dynamics. We found that symbiont prevalence differs spatially but does not show seasonal changes. Infection load differed between populations and was low in spring and significantly increased towards fall in all populations. We also found that the symbiont prevalence is affected by host length and population density. Larger hosts were more likely to be infected and high host densities were found to have lower probability of infection. The observed variations could be due to characteristics of both symbiont and host biology, especially the seasonal variation in encounter rates. Our findings show that the developed ddPCR assay is a robust tool for detecting undescribed symbionts that are otherwise difficult to quantify, enabling further insight into the impact cryptic symbionts have on their hosts.</p>
Droplet digital PCR (ddPCR) as a tool for investigating dynamics of cryptic symbionts
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Molecular Probes for Tracking Lipid Droplet Membrane Dynamics
<p><strong><span>Abstract</span></strong></p> <p><span>Lipid droplets (LDs) and their membrane proteins play crucial roles in lipid metabolism, signaling, and information transport within cells. LDs feature a unique monolayer lipid membrane that has not been extensively studied due to the lack of suitable molecular probes that are able to distinguish this membrane from the LD lipid core. In this work, we present a three-pronged molecular probe design strategy that combines lipophilicity-based organelle targeting with microenvironment-dependent activation. As a proof-of-concept, we designed an <u>LD</u> <u>m</u>embrane labeling pro-probe called<strong> LDM</strong>, which selectively localizes around LD membranes. Upon activation by the HClO/ClO</span><sup><span>−</span></sup><span> microenvironment that surrounds LDs, <strong>LDM</strong> pro-probe undergoes a color change and releases<strong> LDM-OH</strong> probe that binds to LD membrane proteins. This localizes the probe to the LD-as</span><span>sociated protein space which is restricted to the membrane thus enabling visualization of the ring-like LD membrane. By utilizing<strong> LDM</strong>, we identified the dynamic mechanism of LD membrane contacts and their protein accumulation parameters. Furthermore, using <strong>LDM</strong> in liver cancer cells allowed us to examine the changes in LD/mitochondrial protein accumulation caused by the state of starvation these cells encounter. This led to the discovery that liver cancer cells respond to energy stress during hunger by enhancing LD-mitochondria interactions. Taken together, <strong>LDM</strong> represents the first molecular probe for imaging LD membranes in live cells, and represents an attractive tool for further investigations into the specific regulatory mechanisms and drug discovery associa</span><span>ted with LD related metabolism diseases.</span></p> <p><span> </span></p> <p><em><span>Keywords:</span></em><span> Molecular Imaging, Cancer, Lipid droplets, Super-resolution Imaging</span></p>
Data from: Vacuole dynamics and popping-based motility in liquid droplets of DNA
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Chaotic Dynamics in a Two-Droplet Pilot Wave System: A Numerical Simulation
<p>Data set and results for our university modeling project.</p>
Lipid droplets modulate autophagy receptor SQST-1/SQSTM1 dynamics and lifespan
GEO Series GSE204953. Caenorhabditis elegans. 18 samples. Type: Expression profiling by high throughput sequencing.
Dynamic Enlargement and Mobilization of Lipid Droplets in Pluripotent Cells Coordinate Morphogenesis during Mouse Peri-implantation Development
GEO Series GSE165563. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
The fatty liver disease-causing protein PNPLA3-I148M alters lipid droplet-Golgi dynamics
GEO Series GSE261297. Homo sapiens. 60 samples. Type: Expression profiling by high throughput sequencing.
Integrated omics studies delineate the formation and dynamics of lipid droplets in Rhodococcus opacus PD630 for biofuel feedstock
GEO Series GSE42381. Rhodococcus opacus. 3 samples. Type: Expression profiling by high throughput sequencing.
Refractive index determination of dynamic droplets in a flow by analyzing light scattering signals with a machine learning approach
<p>This container includes the measurement data, python script and weights of trained machine learning model associated with the scientific work, which will be presented in 2025 at the <em><strong>Turbulence, Heat and Mass Transfer 11</strong> </em>conference in Tokyo.</p> <p><strong>Title:</strong> Refractive Index Determination of Dynamic Droplets in Flow by Analyzing Light Scattering Signals with a Machine Learning Approach <br><strong>Authors:</strong> W. Schaefer<br><strong>Affiliation:</strong> ai-quanton GmbH, Dr.-Werner-Freyberg-Str. 7, 69514 Laudenbach, Germany <br><strong>Contact:</strong> info@ai-quanton.com </p> <p>The following data files are provided:</p> <ul> <li><strong>Dataset_40_4ch1234.rar (unpacked: Dataset_40_4ch1234.pth)</strong></li> <li><strong>M1_SegmentsTHR40.csv</strong></li> <li><strong>SegmentsTHR40.rar (unpacked: M1_SegmentsTHR40.csv ... M55_SegmentsTHR40.csv)</strong></li> <li><strong>Model_weights_4ch1234.pth</strong></li> </ul> <p> </p> <p><strong>Dataset_40_4ch1234.pth</strong> is a file, containing a ready-to-use dataset of 4-channel signals prepared for use in Python scripts.</p> <p><strong>M1_SegmentsTHR40.csv </strong>is an example of a file used for storing and loading light scattering signals of individual droplets with corresponding additional data. The meaning of each column is:</p> <p>'MID' – measurement ID</p> <p>'FID' – frame ID</p> <p>'SID' – signal ID</p> <p>'CID' – channel ID</p> <p>'NOP' – number of parts</p> <p>'PNM' – part number</p> <p>'TCH' – trigger channel</p> <p>'TLE' – trigger level</p> <p>'TID' – trigger ID</p> <p>'CON' – label used for training</p> <p><strong>SegmentsTHR40.rar</strong> is an archived folder containing .csv files, the same format as M1_SegmentsTHR40.csv.</p> <p><strong>Model_weights_4ch1234.pth </strong>contains weights for a model trained on data from all 4 channels.</p> <p> </p> <p><strong>External files:</strong></p> <p>The correcponding repository to this dataset is published on Azure Dev Ops: <a href="https://dev.azure.com/ai-quanton/PBa202">https://dev.azure.com/ai-quanton/PBa202</a><br>This repository contains the Python script developed for a neural network that determines the refractive index of single droplets by analyzing light scattering signals generated as they pass through a Gaussian beam. </p> <p>The script is designed to build and test a machine learning model capable of accurately predicting refractive indices from light scattering data in dynamic spray environments.</p>
Phenotyping Polarization Dynamics Of Immune Cells Using A Lipid Droplet - Cell Pairing Microfluidic Platform
<p>Raw data from the article : </p> <p><strong>Phenotyping Polarization Dynamics Of Immune Cells Using A Lipid Droplet - Cell Pairing Microfluidic Platform</strong></p> <p>Léa Pinon, Nicolas Ruyssen, Judith Pineau, Olivier Mesdjian, Damien Cuvelier, Rachele Allena, Sophie Asnacios, Atef Asnacios, PaoloPierobon, Jacques Fattaccioli</p>
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