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3 results for “peak artifacts”
Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - PEX5 applied experiment
<p>This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in <a href="https://github.com/aseltmann/fluotracify/blob/data/data/exp-220120-correlate-ptu/LabBook-exp-220120-correlate-ptu.org">this file</a> (see a rendered version <a href="https://aseltmann.github.io/fluotracify/data/LabBook-all.html#sec-2-5">here</a>). This parent project (<a href="https://github.com/aseltmann/fluotracify">https://github.com/aseltmann/fluotracify</a>) also contains examples of how to use these models and related Python code.</p> <p>The following connected paper is currently under review and should be cited together with these model versions: <em>Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)</em></p> <p><strong>Note on the file formats and notation:</strong></p> <p>"Primary data" refers to the <code>.ptu</code> files, so the actual TCSPC data. "Secondary data" refers to the <code>.pqres</code> files, which are derived files by the proprietary PicoQuant software.</p> <p><strong>Note on sample preparation (from the Supplementary Note of the paper above):</strong></p> <p>The peak artifacts measurements were produced by 20 nM Trypanosoma brucei-PEX5 N-term fused to eGFP in solution, and the corresponding control measurements by 5 nM Homo sapiens-PEX5 N-term fused to eGFP in solution. The detailed sample preparation is described elsewhere. We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 20 s for PEX5 experiments.</p>
Fluorescence correlation spectroscopy TCSPC data with and without peak artifacts - AlexaFluor 488 applied experiment
<p>This is a dataset of Fluorescence Correlation Spectroscopy (FCS) Time-Correlated Single Photon Counting (TCSPC) data with and without peak artifacts. The provenance of the data is recorded in <a href="https://github.com/aseltmann/fluotracify/blob/data/data/exp-220120-correlate-ptu/LabBook-exp-220120-correlate-ptu.org">this file</a> (see a rendered version <a href="https://aseltmann.github.io/fluotracify/data/LabBook-all.html#sec-2-5">here</a>). This parent project (<a href="https://github.com/aseltmann/fluotracify">https://github.com/aseltmann/fluotracify</a>]) also contains examples of how to use these models and related Python code.</p> <p>The following connected paper is currently under review and should be cited together with these model versions: <em>Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)</em></p> <p><strong>Note on the file formats and notation:</strong></p> <p>"Primary data" refers to the <code>.ptu</code> files, so the actual TCSPC data. "Secondary data" refers to the <code>.pqres</code> files, which are derived files by the proprietary PicoQuant software.</p> <p><strong>Note on sample preparation (from the Supplementary Note of the paper above):</strong></p> <p>The peak artifact measurements were produced by mixing 20 nM of the small dye Alexa Fluor 488 (Thermo Fisher Scientific) with 10 μM of slow, large (100 nm) unilamellar vesicles (LUVs) labelled with DiO in the membrane (0.1 % mol lipid:dye). The corresponding control measurements were 20 nM Alexa Fluor 488 in solution. We prepared the LUVs by the extrusion method: 1−palmitoyl−2−oleoyl−glycero−3−phosphocholine (POPC, Avanti Polar Lipids) was vacuum dried on a glass vial for 1 h and resuspended on phosphate buffered saline to form lipid vesicles. The vesicles were extruded 30 times through 100 nm pore size polycarbonate membranes using a Mini-Extruder (Avanti Polar Lipids). We prepared the samples on #1.5 coverslips mounted on Attofluor Cell Chambers (Thermo Fisher Scientific). We acquired the data on a MicroTime 200 microscope (PicoQuant) equipped with an Olympus UPlanSApo 60× 1.2NA water immersion objective lens and a HydraHarp 400 TCSPC module (PicoQuant). Excitation was achieved with a 488 nm pulsed laser (PicoQuant) with a power of 5 mW measured at the sample plane. We used a 20 nM solution of Alexa Fluor for calibrating the correction collar. One TCSPC measurement had a length of 10 s for AF488 experiments.</p>
Fluorescence correlation spectroscopy time-series data with and without peak artifacts - simulated data
<p>This is a dataset of FCS time-series with and without peak artifacts. It was created by 2D Monte Carlo simulations of diffusing particles. The provenance of the data is recorded in <a href="https://github.com/aseltmann/fluotracify/blob/data/data/exp-201231-clustsim/LabBook-exp-201231-clustsim.org">this file</a> (see a rendered version <a href="https://aseltmann.github.io/fluotracify/data/LabBook-all.html#sec-2-2">here</a>). This parent project (<a href="https://github.com/aseltmann/fluotracify">https://github.com/aseltmann/fluotracify</a>]) also contains examples of how to use this data and related Python code to load it.</p> <p>The following connected paper is currently under review and should be cited together with this dataset: Seltmann, A.; Carravilla, P.; Reglinski, K.; Eggeling, E.; Waithe, D. Neural Network Informed Photon Filtering Reduces Artifacts in Fluorescence Correlation Spectroscopy Data. 2023 (currently under review)</p> Data structure inside each .csv file <table><tbody> <tr> <td><header></td> <td> <p>10 to 12 lines, contains metadata</p> </td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>...</td> <td>...</td> <td> </td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td><source_1></td> <td><target_1a></td> <td><target_1b></td> <td><source_2></td> <td><target_2a></td> <td><target_2b></td> <td>...</td> </tr> <tr> <td> <p>FCS time-series with artifact</p> </td> <td>Artifact time series</td> <td> <p>FCS time series without artifact</p> </td> <td> <p>FCS time series with artifact</p> </td> <td>Artifact time series</td> <td> <p>FCS time-series without artifact</p> </td> <td>...</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p> </p>
ScienceDex guides
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