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242 results for “microfluidics”
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>
Dataset for Evaluation of a novel microfluidic chip-like device for purifying bovine frozen-thawed semen for in vitro fertilization
<p>VetCount<sup>TM</sup> Harvester (MotilityCount ApS, Copenhagen, Denmark) is a novel sperm<br> purification device. It consists of two chambers separated by a 10 μM microporous<br> membrane. Untreated semen is applied in one chamber, sperm collection medium<br> in the other. Motile sperm cells are selected by actively swimming through the<br> membrane pores into the medium containing chamber. After 30 min incubation,<br> the sperm collection medium can be aspirated and the purified sperm is ready<br> for further use.<br> In a first experiment we assessed sperm quality and recovery of frozen-thawed semen<br> from six different bulls (n = 6) prior to and after purification with the<br> VetCount<sup>TM</sup> Harvester or BoviPure<sup>TM</sup> gradient centrifugation, a commercial available standard technique. In a second approach, a competitive fertilization assay was performed. Ten straws per bull were pooled, split<br> in two subsamples, and simultaneously purified either with the VetCount<sup>TM</sup> Harvester<br> or BoviPure<sup>TM</sup> gradient centrifugation. Following purification, sperm cells<br> from each treatment group were fluorescently labeled with either MitoTracker<sup>TM</sup><br> Red FM or MitoTracker<sup>TM</sup> Green FM. <em>In vitro</em> matured oocytes were inseminated with<br> equal numbers of red and green labeled sperm. Eighteen hours after fertilization,<br> fluorescence microscopy was used to determine the origin of the fertilizing spermatozoon.</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>
Rapid Fabrication of Membrane-IntegratedThermoplastic Elastomer Microfluidic Devices
<p>Txt files contain experimental data sets used to obtain the results of Fig. 3 and 4 and the OB1 code. Data files for Fig 3. are named GapSize_membraneConfiguration_#_delaminationPressure and data files for Fig 4. Are named agingTime_membraneConfiguration_incubationCondition_#.</p>
Stability characterization of microfluidic lipid-stabilized double emulsions under physiologically-relevant conditions
<p>Double emulsions (DEs) are water-in-oil-in-water (or oil-in-water-in-oil) droplets with the potential to deliver combinatory therapies due to their ability to co-localize hydrophilic and hydrophobic molecules in the same carrier. However, DEs are thermodynamically unstable and only kinetically trapped. Extending this transitory state, rendering DEs more stable, would widen the possibilities of real-world applications, yet characterization of their stability in physiologically-relevant conditions is lacking. In this work, we used microfluidics to produce lipid-stabilized DEs with reproducible monodispersity and high encapsulation efficiency. We investigated DE stability under a range of physico-chemical parameters such as temperature, pH and mechanical stimulus. Stability through time was inversely proportional to temperature. DEs were significantly stable up to 8 days at 4 oC, 5 days at RT and 2 days at 37 oC. When encapsulating a cargo, DE stability decreased significantly. When exposed to a pH change, unloaded DEs were only significantly unstable at the extremes (pH 1 and 13), largely outside physiological ranges. When exposed to flow, unloaded DEs behaved similarly regardless of the mechanical stimulus applied, with approximately 70% remaining after 100 flow cycles of 10s. These results indicate that lipid-stabilized DEs produced via microfluidics could be tailored to endure physiologically-relevant conditions and act as carriers for drug delivery. Special attention should be given to the composition of the solutions, e.g. osmolarity ratio between inner and outer solutions, and the interaction of the molecules, e.g. carrier and cargo, involved in the final formulation.</p>
A versatile microfluidic platform measures hyphal interactions between Fusarium graminearum and Clonostachys rosea in real-time
<p>Routinely, fungal-fungal interactions (FFIs) are studied on agar surfaces. However, this format restricts high-resolution dynamic imaging. To gain experimental access to FFIs at the hyphal level in real-time, we developed a microfluidic platform, a FFI device. This device utilises microchannel geometry to enhance the visibility of hyphal growth and provides control channels to allow comparisons between localised and systemic effects. We demonstrate its function by investigating the FFI between the biological control agent (BCA) <em>Clonostachys rosea </em>and the plant pathogen <em>Fusarium graminearum. </em>Microscope image analyses confirm the inhibitory effect of the necrotrophic BCA and we show that a loss of fluorescence in parasitised hyphae of GFP-tagged <em>F. graminearum </em>coincides with the detection of GFP in mycelium of <em>C. rosea</em>. The versatility of our device to operate under both water-saturated and nutrient-rich as well as dry and nutrient-deficient conditions, coupled with its spatio-temporal output, opens new opportunities to study relationships between fungi.</p>
Supplementary Material: Microfluidic Fabrication Solutions for Tailor-Designed Fiber Suspensions
<p>Supplementary material for Berthet, H.; du Roure, O.; Lindner, A. Microfluidic Fabrication Solutions for Tailor-Designed Fiber Suspensions. <em>Appl. Sci.</em> <strong>2016</strong>, <em>6</em>, 385.</p> <p><strong>Video S1:</strong> Microfluidic fabrication technique of fibers by in situ photopolymerization</p> <p><strong>Video S2: </strong>In situ microfluidic measurement of the fiber’s Young’s modulus</p> <p><strong>Video S3: </strong>Microfluidic fabrication technique of fibers by super-paramagnetic particles self-assembly</p> <p><strong>Video S4: </strong>Fiber oscillating between the two lateral walls of a microfluidic channel</p> <p><strong>Video S5: </strong>Flow through a constriction of a suspension of parallel fibers fabricated by photo-polymerization</p> <p><strong>Video S6: </strong>Flow through a constriction of a suspension of rigid perpendicular fibers fabricated by photo-polymerization</p> <p><strong>Video S7: </strong>Flow through a constriction of a suspension of flexible perpendicular fibers fabricated by photo-polymerization</p> <p><strong>Video S8: </strong>Concentrated suspension of fibers flowing through a microfluidic constriction</p> <p><strong>Video S9: </strong>Fibers made by colloids self-assembly flowing through a constriction and forming non-permanent clusters.</p>
Dataset for manuscript 'CeyeHao: AI-driven microfluidic flow programming with hierarchically assembled obstacles in microchannel and receptive-field-augmented neural network'
<p>This dataset contains:<br>1. The dataset used to train the models related to the manuscript 'CeyeHao: AI-driven microfluidic flow programming using hierarchically assembled obstacles in microchannel with receptive-field-augmented neural network'.<br>2. A checkpoint of trained 'CEyeNet' proposed in the manuscript.<br>3. Example microchannels designed in the manuscript to produce semantic flow profiles</p> <p>This dataset is intended for research and academic purpose.</p> <p>Detailed description please refer to the enclosed ReadMe.txt.</p>
Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix digital microfluidics
<p>Active-matrix digital microfluidics (AM-DMF), integrated with hundreds of thousands of active electrodes, can simultaneously realize multiple on-chip bio-chemical reactions at the single-cell level. An intelligent detection system is critical for fully automating manipulations of thousands of digitalized bio-samples and programming the subsequent experiments in real time. In this work, we developed a series of deep learning algorithms based on an AM-DMF system for sample detections. We used the U-net model to quantitatively evaluate different splitting methods on sample droplet generation uniformity. The results revealed that droplets generated using the "one-to-two" strategy exhibits optimal uniformity. We used the YOLOv5 model to monitor the droplet splitting success rates over 18 different AM-DMF chips, and a 97.7% splitting success rate was observed. The results indicated that the model precision was 99.980% and the model recall was 99.976% through manual verification. In addition, we used an improved YOLOv8 model to detect single cells in nanoliter droplets effectively. In comparison with manual verification, the results showed that the model achieved a precision of 99.260% and a recall of 99.193%. By leveraging an artificial intelligence enabled smart detection system, AM-DMF has shown great potential as a ubiquitous platform for true lab-on-a-chip.</p>
Coupling the COST reference plasma jet to a microfluidic device: a computational study - Figure Data
Open the record for dataset details and reuse information.
Dataset supporting manuscript entitled 'Partitioning of Small Hydrophobic Molecules into Polydimethylsiloxane in Microfluidic Analytical Devices'
<p>This is the dataset supporting the manuscript 'Surface and bulk modifications of polydimethylsiloxane to reduce absorption/adsorption of small molecules in lab-on-a-chip' published in Micromachines</p>
The multilayer volume-of-fluid method for multiphase flows across scales: breaking waves, microfluidics, and membrane-less electrolyzers
<p>Supplementary movies to PhD thesis <a href="https://doi.org/10.3929/ethz-b-000547518">10.3929/ethz-b-000547518</a></p>
Raw data for "Fluorescence crosstalk reduction by modulated excitation-synchronous acquisition for multispectral analysis in high-throughput droplet microfluidics."
<p>Raw data to quantify the crosstalk reduction and signal resolution improvement by MESA used in Figure 3 and 4.</p> <p><br> </p>
BeeDNA: microfluidic environmental DNA metabarcoding as a tool for connecting plant and pollinator communities
<p><strong>Data repository accompanying the paper 'BeeDNA: microfluidic environmental DNA metabarcoding as a tool for connecting plant and pollinator communities' by Harper et al. (2021).</strong></p> <p><br> <strong>1_Raw_Data.zip</strong><br> This zipped folder contains the raw sequence data (sorted by primer set and demultiplexed) for both sequencing runs (2019-10-24 and 2019-11-11). To decompress each file, run: </p> <pre><code>tar -xvf filename.bz2</code></pre> <p>This will create a folder for each primer set containing the raw reads for each sample/control.</p> <p><br> <strong>2_Anacapa_Bioinformatic_Processing.zip</strong></p> <p>This zipped folder contains all files needed to perform bioinformatic processing with Anacapa. Please process sequence data belonging to each primer set individually (i.e. do not process sequence data belonging to different primer sets together).</p> <p><br> <strong>3_metaBEAT_Bioinformatic_Processing.zip </strong></p> <p>This zipped folder contains the scripts and files needed to perform bioinformatic processing with metaBEAT. Before running the scripts, move the raw reads for each sample belonging to each primer set into the dedicated folder within metaBEAT_Bioinformatic_Processing, e.g. all .fastq files in Raw_Data > BF1_BR1 should be moved to metaBEAT_Bioinformatic_Processing > BF1-BR1 > raw_reads.</p> <p>To run metaBEAT, you will have to install Docker on your computer. Docker is compatible with all major operating systems, but see the Docker documentation for details. On Ubuntu, installing Docker should be as easy as:</p> <pre><code>sudo apt-get install docker.io</code></pre> <p>Once Docker is installed, you can enter the environment by typing:</p> <pre><code>sudo docker run -i -t --net=host --name metaBEAT -v $(pwd):/home/working chrishah/metabeat /bin/bash</code></pre> <p>This will download the metaBEAT image (if not yet present on your computer) and enter the 'container', i.e. the self contained environment (NB: sudo may be necessary in some cases). With the above command, the container's directory /home/working will be mounted to your current working directory (as instructed by $(pwd)). In other words, anything you do in the container's /home/working directory will be synced with your current working directory on your local machine.</p> <p>Please process sequence data belonging to each primer set individually (i.e. do not process sequence data belonging to different primer sets together). An example of expected outputs can be seen in the Jupyter Notebook for the BF1/BR1 primer set from the 2019-11-11 sequencing run.</p> <p><br> <strong>4_Illinois_Invert_Reference_Database.zip</strong></p> <p>This zipped folder contains all files that were used to generate the custom COI and 16S reference databases for invertebrates that occur in Illinois, U.S. You will need to have metaBEAT installed (see above) before you try to run any Jupyter Notebooks (.ipynb files).</p> <p><br> <strong>5_ecoPCR.zip</strong></p> <p>This zipped folder contains all files used to perform ecoPCR for each primer set evaluated for microfluidic eDNA metabarcoding. You will need to <a href="https://git.metabarcoding.org/obitools/ecopcr/wikis/home">install ecoPCR</a> before running any shell scripts.</p> <p><br> <strong>6_Tidied_Data.zip</strong></p> <p>This zipped folder contains the taxonomically assigned data for both sequencing runs produced by metaBEAT and Anacapa. These were copied over from the folders 2_Anacapa_Bioinformatic_Processing and 3_metaBEAT_Bioinformatic_Processing and rearranged into a more logical order. These files are used as the input for data analysis using R.</p> <p><br> <strong>7_Data_Analysis.zip</strong></p> <p>This zipped folder contains all scripts and metadata required to summarise and statistically analyse data in R.</p> <p> </p> <p><strong>Please contact Dr Lynsey Harper (lynsey.harper2@gmail.com) or Dr Mark Davis (davis63@illinois.edu) if you encounter any issues!</strong></p>
"Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading)
<p>This video is the tenth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 14/09/2022.</p> <p>"Tiny Test Tubes" for affordable microfluidic blood measurements at the point of need - Dr Alexander Edwards (University of Reading)</p> <p>Bio: Al Edwards has a background in fundamental immunology combined with expertise in biochemical engineering, he is an interdisciplinary researcher focussed on solving current and future healthcare challenges using an engineering science approach that combines a range of fields from biology, biochemistry, chemistry and physics. He works at the interface between academic technology discovery and industrial development and have experience of both fundamental research and the commercialisation of new technology. The two main challenges he currently works on are the development of affordable microfluidics for clinical diagnostics and microbiology, and the engineering science of complex biologic therapeutics such as vaccines. Alexander's research is funded from a wide range of sources, including NIHR , EPSRC, SBRI Healthcare, the Wellcome Trust, Innovate UK and industry</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/21a78Vql8b0</p>
Dataset for: Phenotyping single-cell motility in microfluidic confinement
<p>Associated dataset and simulation codes for the publication "Phenotyping single-cell motility in microfluidic confinement" (2022), by Samuel A. Bentley, Hannah Laeverenz-Schlogelhofer, Vasileios Anagnostidis, Jan Cammann, Marco G. Mazza, Fabrice Gielen, Kirsty Y. Wan. </p>
A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus
<p>Raw data associated to the study entitled:</p> <p><em>A DNA biosensors-based microfluidic platform for attomolar real-time detection of unamplified SARS-CoV-2 virus</em><strong> </strong></p> <p><em>- </em>Metadata file</p> <p>- Computational data</p> <p>- Extraction data</p> <p>- Fluorescence detection</p> <p>- Fluorescence imaging</p> <p>- Labbooks</p> <p>- Surface characterization</p>
Assessing Hydrodynamic resistance in Microfluidics: A Case Study - datasets
<p><strong><span>Abstract: </span></strong><span>Hydrodynamic resistance is a critical parameter in microfluidics, affecting device functionality and performance.</span><span> However, quantifying hydrodynamic resistance in microfluidics is a challenge due to many influencing factors and the difficulties associated with the precise measurements of low flow rates (< 10 </span><span><span>m</span></span><span>L/min) and pressure drops (< 5 kPa). This article presents a simple experimental test method for assessing hydrodynamic resistance, correlating with theoretical and numerical calculations. The results demonstrate good agreement between benchtop and theoretical data, suggesting a potential standardized method for assessing hydrodynamic resistance in microfluidic devices.</span></p> <p> </p> <p><span>In the files attached: Dataset</span></p> <p> </p> <p><strong><span>Funding:</span></strong><span> This project (20NMR02 MFMET) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. V.S. would like to acknowledge the FCT, I.P., for funding of the Research Unit INESC MN (UID/05367/2020) through pluriannual BASE and PROGRAMATICO and project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy – i4HB</span></p>
Microfluidic solvent extraction of poly(vinyl alcohol) droplets: effect of polymer structure on particle and capsule formation
<p>Raw data from the majority of figures of our 2018 Soft Matter Paper:</p> <p>Selected datasets from figures are excluded, owing to them being transformations of the raw data provided in the same figure.</p> <p> </p>
Supplementary Data - Rectification of Bacterial Diffusion in Microfluidic Labyrinths
<p>Maze5um_ecoli: <em>E. coli </em>trajectories (x- and y-positions, time, index) in a microfluidic maze (height 5µm).<br> Maze8um_ecoli: <em>E. coli</em> trajectories (x- and y-positions, time, index) in a microfluidic maze (height 8µm).<br> OutofMaze5um_ecoli:<em> E. coli</em> trajectories (x- and y-positions, time, index) in a microfluidic channel in absence of a maze (height 5µm).</p> <p>For experimental conditions and details of the tracking algorithm see Weber et al. <em>Frontiers in Physics </em>(2019).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.