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242 results for “microfluidics”
Supplementary Files - Transcriptomic analysis of CPM-positive hiPSCs-derived liver progenitor cells in a microfluidic device shows zonation-like patterns.
<p>Supplementary Files for the paper intitled Transcriptomic analysis of CPM-positive hiPSCs-derived liver progenitor cells in a microfluidic device shows zonation-like patterns. </p>
injection of PVA MBs via microcatheter in microfluid channel
<p>jinjection tests operating the syringe manually, also in reverse flux, to verify the absence of obstructions and damages.Thereafter, the injection syringe was mounted on a programmable, step-by-step motor driven syringe pump system which was set at different constant volumetric flow rates, ranging from 0.05 ml/min to 1.12 ml/min according to material and methods, section "<em>Handling of microcatheters with PVA MBs for insertion in microfluidic channels"</em></p>
Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
<p>This is the dataset for the work titled and authored by:</p> <p><strong>Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution</strong></p> <p>Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig</p> <p>We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.</p> <p>Software can be found under this DOI:</p> <p>10.5281/zenodo.846448</p>
Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation
<p>Data for "Quantitative modelling of nutrient-limited growth of bacterial colonies in microfluidic cultivation"</p> <p> </p> <p>GrowthChannelExperiments contains the data-folders of the following growth channel experiments:<br> ***********************************************************************************************</p> <p>Name Feeding Concentration [in units of 0.195mM PCA]<br> nd004_series1 0.5<br> nd004_series2 0.5<br> nd004_series3 0.5<br> nd004_series4 2.0<br> nd004_series5 2.0<br> nd004_series6 2.0<br> nd004_series7 3.0<br> nd004_series8 3.0<br> nd112_series2 0.25<br> nd112_series3 0.25<br> nd112_series7 3.0<br> nd112_series8 3.0</p> <p>Every folder contains:<br> - a tif-file with captured image series<br> - a PIV*-folder with four PIV-files for every frame pair. The four files belong to intermediate results of the multistep PIV. The final PIV-result is given in the file step2*.dat.nmt.<br> The PIV result will be stored in a plain text file. Each line in this file correspond to each PIV vector and comprised of 16 columns:<br> x y ux1 uy1 mag1 ang1 p1 ux2 uy2 mag2 ang2 p2 ux0 uy0 mag0 flag<br> -- (x,y) is the position of the vector (center of the interrogation window).<br> -- ux1, uy1 are the x and y component of the vector (displacement) obtained from the 1st correlation peak.<br> -- mag1 is the magnitude (norm) of the vector.<br> -- ang1, is the angle between the current vector and the vector interpolated from previous PIV iteration.<br> -- p1 is the correlation value of the 1st peak.<br> -- ux2,uy2,mag2,ang2,p2 are the values for the vector obtained from the 2nd correlation peak.<br> -- ux0, uy0, mag0 are the vector value at (x,y) interpolated from previous PIV iteration.<br> -- flag is a column used for mark whether this vector value is interpolated (marked as 999) or switched between 1st and 2nd peak (marked as 21), or invalid (-1). <br> According to the PIV-Fiji-plugin as provided by Qingzong Tseng, used also in : <br> Tseng, Q. et al. Spatial organization of the extracellular matrix regulates cell-cell junction positioning. Proc. Natl. Acad. Sci. 109, 1506–1511 (2012)<br> - two traj*.dat files, belonging to particle positions of the corresponding simulation with monod/teissier uptake. <br> Columns correspond to <br> 1 : time | 2 : cellID | 3 : rx | 4 : ry | 5 : rz | 6: species | 7 : vx | 8 : vy | 9 : vz | 10 : fx | 11 : fy | 12 : fz | 13 : B(g) |<br> -- rx,ry,rz 3D coordinates of particle<br> -- species is either 0 (living cell) or 1 (wall-particle)<br> -- vx,vy,vz 3D velocity of particle<br> -- fx,fy,fz 3D force of particle<br> -- B(g) growth force constant dependent on local g-concentration<br> Note that due to the simulation being 2D, rx=constant and vx=0=fx.<br> - two g*.dat files, belonging to nutrient concentrations of the corresponding simulation with monod/teissier uptake. <br> Columns correspond to <br> 1 : time | 2 : gridx | 3 : gridy | 4 : gridz | 5 : g-conc | 6: kcons | 7 : kprod | 8: Dlocal |<br> -- gridx,gridy,gridz coordinates of lattice side<br> -- kcons local nutrient consumption rate<br> -- kprod local nutrient production rate (always zero)<br> -- Dlocal local diffusion constant</p> <p> </p> <p>GrowthChamberExperiments contains the the data-folders of the following growth chamber experiments:<br> ***************************************************************************************************</p> <p>Name Feeding Concentration [in units of 0.195mM PCA]<br> nd143_xy009 1.0<br> nd143_xy013 1.0<br> nd143_xy025 1.0<br> nd143_xy032 1.0<br> nd143_xy059 1.0<br> nd143_xy060 1.0<br> nd143_xy061 1.0<br> nd143_xy165 0.1<br> nd143_xy184 0.1<br> nd143_xy214 0.1</p> <p>Every folder contains:<br> - a tif-file with captured image series<br> - five traj*.dat files, belonging to particle positions of the corresponding simulation with monod-uptake and five different ratios of the diffusion constants in- and outside the colony.<br> - five g*.dat files, belonging to nutrient concentrations of the corresponding simulation with monod-uptake and five different ratios of the diffusion constants in- and outside the colony.</p>
MFMET webinar - 02. Flow in microfluidics
<p>This video presents flow in microfluidics. It shows measurements of flow and pressure, and a characterisation method for determining hydrodynamic resistance.</p> <p><br>Further reading can be found:<br><a href="https://mfmet.eu/publications">https://mfmet.eu/publications</a><br>A2.2.2: Development of test protocols for microfluidic devices. (2023). <a href="https://doi.org/10.5281/zenodo.7845431">Link</a><br>Whitepaper flow resistivity testing (1.0). (2023) Zenodo. <a href="https://doi.org/10.5281/zenodo.7919134">https://doi.org/10.5281/zenodo.7919134</a><br>A1.3.2 - Existing operational conditions and existing operation protocols for flow control. (2023). <a href="https://doi.org/10.5281/zenodo.10116586">Link</a></p> <p>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
MFMET webinar - 01. The role of Metrology and Standardization in microfluidic technology development
<p>This video presents the MFMET project, and it introduces metrology and standardization in the context of microfluidics.<br> <br>Further readings can be found:<br> <br><a href="https://mfmet.eu">https://mfmet.eu</a><br> <br><a href="../communities/mfmet">https://zenodo.org/communities/mfmet</a></p> <p> </p> <p>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
Comparison of measurement protocols for internal channels of transparent microfluidic devices - datasets
<p>Supplementary material to "Comparison of measurement protocols for internal channels of transparent microfluidic devices" submitted to Micromachines.</p> <p>Data accompanying comparison of measurement protocols.</p> <p> </p> <p>Further readings can be found at <a href="https://mfmet.eu/publications">https://mfmet.eu/publications</a></p> <p>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
Photoactive integrated microfluidic valves for on-chip fluid control
<p>This dataset is acompannying the publication: Photoactive integrated microfluidic valves for on-chip fluid control.</p>
Data from: Microfluidic Enrichment Barcoding (MEBarcoding): a new method for high throughput plant DNA barcoding
<p>DNA barcoding has become a valuable tool to support species identification with a broad range of applications in fields such as traditional taxonomy, ecology, forensics, food analysis, and environmental science. We introduce Microfluidics Enrichment Barcoding (MEBarcoding) for plant DNA Barcoding, a cost-effective method for high throughput DNA barcoding. MEBarcoding uses the Fluidigm Access Array™ to simultaneously amplify targeted regions for 48 DNA samples and hundreds of PCR primer pairs (a total of 23,040 PCR products) during a single thermal cycling protocol. A second generation instrument from Fluidigm, called the Juno™, can accommodate 192 DNA samples simultaneously. As a proof of concept, we developed a microfluidic PCR workflow using the Fluidigm Access Array™ and Illumina MiSeq to generate new sequences from 96 samples for each of the four primary DNA barcode loci in plants: rbcL, matK, trnH-psbA, and ITS (384 total sequences). This workflow was used to build a reference library that includes 78 families and 96 genera from all major plant lineages, including bryophytes, ferns and lycophytes, gymnosperms, and all major groups of angiosperms, which are currently lacking in public databases. Our results demonstrate that this technique offers a highly efficient alternative method to traditional PCR and Sanger sequencing by increasing the estimated number of plant DNA barcodes that can be sequenced by a single technician in one week by 800%, at a reduced cost, and by generating a barcode library with a more comprehensive taxonomic coverage.</p>
Quantitative Characterisation of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control
<p>Data, computational software, and supplemental movies generated in the study: "Quantitative Characterisation of α-Synuclein Aggregation in Living Cells through Automated Microfluidics Feedback Control."</p>
Microfluidic chambers
<p>a) Scheme of the microfluidic chambers used for the experiments. <strong>b)</strong> SEM image of the tip of a nanopipette. To prevent charging effects, the pipette was coated with a 5nm gold layer. Its inner diameter is around 60nm. <strong>c) </strong>Photograph of a microfluidic chamber. The filling holes are also used to put the silver electrodes.</p>
Raw image of microfluidic experiment
<p>Experimental raw images under the conditions of an injection rate of 10 μL/min and a cementation solution concentration of 0.5 mol/L.</p>
Transwell-Based Microfluidic Platform for High-Resolution Imaging of Airway Tissues
<div> <div> <div> <div> <p>This dataset contains original data collected during our study on the development and characterization of a transwell-based microfluidic platform designed for high-resolution imaging of airway tissues. It includes quantitative measurements of various features of live airway epithelium tissues, such as Trans-Epithelial Electrical Resistance (TEER), Cilia Beating Frequency (CBF), LDH Release (a cytotoxicity assay), tissue thickness, and cell number. Additionally, the dataset provides results from computational simulations modeling the shear stress in the microchannel generated by perfusion.</p> </div> </div> </div> </div>
Data for Responsive Soft Interface Liquid Crystal Microfluidics
<p>The data used in the article entitled Responsive Soft Interface Liquid Crystal Microfluidics.</p>
Rapid Identification of Bacterial isolates Using Microfluidic Adaptive Channels and Multiplexed Fluorescence Microscopy
<p>Dataset for: Rapid Identification of Bacterial isolates Using Microfluidic Adaptive Channels and Multiplexed Fluorescence Microscopy</p> <p>doi: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4LC00325J">10.1039/D4LC00325J</a></p>
Raw images, video, and data file for the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials"
<p>This dataset includes the raw images and data file in the manuscript "Fabrication of Low-Cost, High-Resolution Open Capillary Microfluidics towards Self-Sustaining, Long-Term Hydration of Engineered Living Materials", specifically:</p> <ul> <li>Raw images for the optimized print with the PEGDA-glycerol-water resin (Figure 2 & Figure S2)</li> <li>Raw images for the optimized print with the PEGDA-glycerol-LB resin (Figure 2)</li> <li>Raw images for the optimized print with the BSA-PEGDA-water resin (Figure 3)</li> <li>Raw images and video for the spontaneous capillary flow of LB media in a PEGDA-glycerol-LB microfluidic chip (Figure 4)</li> <li>Raw data for the UV-vis spectrum of LB media (Figure S4)</li> </ul>
Trained network for segmentation of yeast cell from brightfield images in microfluidic traps - DetecDiv (id02)
<pre>Trained network for segmentation of yeast cell from brightfield images in microfluidic traps. Related to the dataset: <a href="https://doi.org/10.5281/zenodo.5553771">https://doi.org/10.5281/zenodo.5553771</a></pre> <p><strong>------------------------------------------</strong></p> <p><strong>Author(s)</strong>: Théo, ASPERT</p> <p><strong>Contact email</strong>: theo.aspert@gmail.com</p> <p><strong>Affiliation</strong>: IGBMC, Université de Strasbourg</p> <p><strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.</p>
Phase-contrast time-lapses of seven bacterial species growing in microfluidic mother machine traps
<p>This dataset and software accompany the article "Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification" for reproducing the results.</p> <p>In the study, deep-learning models are trained to classify phase-contrast videos (time-lapses) of bacteria growing in microfluidic chip traps. The dataset consists of lab isolates of the species Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Enterococcus faecalis, Proteus mirabilis, and Staphylococcus aureus. The video clips have around 30 frames each, captured during one hour of growth (2 minutes between each frame). The whole dataset consists of around 620,000 images from 19,500 traps.</p> <p>Additionally, the package contains software to re-run the experiments, generate output metrics, and build the graphs in the article.</p>
Dataset supporting "From mouth to gut: microfluidic in vitro simulation of human gastro-intestinal digestion and intestinal permeability"
<p>Manuscript abstract:</p> <p>Reproducible <em>in vitro</em> studies of bioaccessibility, intestinal absorption and bioavailability are key to the successful development of novel foods and drugs aiming for oral administration. There is currently a lack of methods that offer the finesse required to study these parameters for valuable molecules in small volumes - as is the case of nanomaterials under research. Here, we describe a modular microfluidic-based platform for total simulation of the human gastro-intestinal tract. Digestion-chips and cell-based gut-chips were fabricated from PDMS by soft lithography. On-chip digestion was validated using a fluorescently-labelled casein derivative, which followed typical Michaelis-Menten kinetics and showed temporal resolution and good agreement with well-established bench-top protocols. Irreversible inhibition of serine proteases using Pefabloc® SC and a 1:6 dilution was sufficient to mitigate the cytotoxicity of simulated digestion fluids. Caco-2/HT29-MTX co-cultures were grown on-chip under continuous flow for 7 days to obtain a differentiated cell monolayer forming a 3D villi-like epithelium with clear tight junction formation, and with an apparent permeability (P<sub>app</sub>) of Lucifer Yellow closely approximating values reported <em>ex vivo</em> (3.7x10<sup>-6</sup> ± 1.4x10<sup>-6</sup> vs 4.0x10<sup>-6</sup> ± 2.2x10<sup>-6</sup>). Digesta from the Digestion-chips were flowed through the Gut-Chip demonstrating the capacity to study sample digestion and intestinal permeability in a single microfluidic platform holding great promise for its use in pharmacokinetics studies.</p>
Optical trapping of micro-particles and bacterial cells in single channel and flow-focusing microfluidic devices
<p><strong>Video 1</strong> - The video shows the flow-focusing and trapping of 1.84 μm bacteria-sized particles flowing at a sample flow rate of 0.1 μL/min. The horizontal sheath flow rate 1 μL/min and the vertical sheath flow rate is 0.5 μL/min. Trapping is achieved using a maximum laser power of 250mW. </p> <p><strong>Video 2</strong> - The video shows the flow and fluorescence trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 3</strong> - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 1 μL/min. Increased flow rate results in continuous transient trapping of the cells is achieved at a trapping power of 250mW. The microchannel surface is not treated with pluronic F-127, therefore lot of particles stick to the channel surface. </p> <p><strong>Video 4</strong> - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 5</strong> - The video shows the flow and trapping of <em>E. coli</em> MG1655 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 6</strong> - The video shows the flow and trapping of <em>S. aureus</em> 6538 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p>
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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.