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2,216 results for “membrane”
Simulation files for POPC lipid membrane with Slipids-VIS force field for Gromacs MD simulation engine
<p>The tar.gz archive contains simulation input files that were used in the publication Transmembrane potential modeling: Comparison between methods of constant electric field and ion imbalance.</p> <p>http://pubs.acs.org/doi/abs/10.1021/acs.jctc.5b01202</p> <p>The files are meant to be used with <strong>Gromacs</strong> simulation package (gromacs.org).</p> <p>A modified Slipids force field, <strong>Slipids-VIS</strong>, is introduced. It uses Virtual Interaction sites in order to speed up simulation. The technique is described in the aforementioned work. The archive contains working topology for <strong>POPC</strong> lipid molecules and 6fs timestep without any significant loss of accuracy.</p>
Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications - PCCP
<p>The dataset uploaded herein is associated with the paper published under the title "<i>Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications</i>" with the Royal Society of Chemistry - Physical Chemistry Chemical Physics Journal. This dataset includes the .vasp files for all modeled structures, an example dftb_in.hsd file, which is the instructional file for geometry optimization with DFTB+, an Excel spreadsheet with atom number densities and total energy values for all modeled geometries, and, finally, a Python script that was used to calculate the Enthalpy of Formation and Cohesive Energies for all structures. This data has been made available to the scientific community in the interest of open-source and accessible data. The authors request that you please cite the associated paper and Zenodo dataset if used.</p><p><strong>Abstract</strong></p><p>Two-dimensional covalent organic frameworks (2D-COFs) exhibit characteristics ideal for membrane applications, such as high stability, tunability and porosity along with well-ordered nanopores. However, one of the many challenges with fabricating these materials into membranes is that membrane wetting can result in layer swelling. This allows molecules that would be excluded based on pore size to flow around the layers of the COF, resulting reduced separation. Cross-linking between these layers inhibits swelling to improve the selectivity of these membranes. In this work, computational models were generated for a quinoxaline-based COF cross-linked with oxalyl chloride (OC) and hexafluoroglutaryl chloride (HFG). Enthalpy of formation and cohesive energy calculations from these models show that formation of these COFs is thermodynamically favorable and the resulting materials are stable. The cross-linked COF with HFG was synthesized and characterized with Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis with differential scanning calorimetry (TGA-DSC), and water contact angles. Additionally, these frameworks were fabricated into membranes for permeance testing. The experimental data supports the presence of cross-linking and demonstrates that varying the amount of HFG used in the reaction does not change the amount of cross-linking present. Computational models indicate that the effect of varying cross-linking concentration on the framework stability is negligible and less cross-linking still results in stable materials. This work sheds light on the nature of the cross-linking in these 2D-COFs and their application in membrane separations.</p>
Fig 6b - FWHM vs distance - broken membrane
<p>The temperature profile on a broken patterned silicon membrane. (b) The line width versus the relative distance between the moving heating laser and the DBT-Ac nanocrystal. The error bars correspond to repeated laser scans, as explained in Sec. III A from the related publication. (c) The temperature profile as a function of the relative distance between the moving heating laser and the DBT-Ac nanocrystal. The red circles denote temperatures estimated from experimental measurements in (b) considering a calibration curve as described in Sec. III A. The solid lines represent simulated temperature profiles assuming different power laws of dependence of the thermal conductivity on the temperature, as well as the presence or lack thereof of the tear, as described in the text.</p>
Polymer Electrolyte Membrane Water Electrolyzer Oxygen Bubble Evolution Optical Video Recording For Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli
<p>Dataset used for the training of the segmentation model employed in the work "Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer" by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli. This dataset consists in 35 images and the corresponding manual annotated masks of diverse bubbly scenarios extracted from the optical video recording of a PEMWE with a transparent flow field.</p>
Aquaporins Embedded in a Cell Membrane with O2 Diffusion
<p>This is an illustration of an aquaporin embedded in a cell membrane with O2 diffusion as well. </p>
Human Colorectal Tissue OCT Dataset: Neoplastic and Non-Neoplastic Samples from Chorioallantoic Membrane (CAM) Assays
<p>A commercial OCT system (Telesto II-1325 LR spectral domain OCT (SD-OCT)) was used to imaging two colon cell lines implanted in a chick embryo chorioallantoic membrane (CAM) assay. This is a high-performance imaging system designed for in vivo and ex vivo imaging of biological tissues.</p> <p>The CAM is a highly vascularized extra-embryonic membrane connected to the developing embryo through an easily accessible circulatory system that allows the successful engraftment of a variety of foreign tissues, such as tumor explants or cancer cell lines. Neoplastic and non-neoplastic tumors were developed from RKO and NCM460 cell lines, respectively.</p> <p> </p> <p>OCT B-scan images were collected from 30 CAM models, 15 with RKO-cells and 15 with NCM460-cells. The collected images are avalilable in format <strong>OCT </strong>format (<strong>Neoplastic_OCT.zip/Non-neoplastic_OCT.zip</strong>), <strong>TIFF </strong>format (<strong>Neoplastic_tiff.zip/Non-neoplastic_tiff.zip</strong>), and <strong>MAT </strong>format (<strong>Neoplastic_mat.zip/Non-neoplastic_mat.zip</strong>).</p> <p> </p> <p>Since, the attenuation of near-infrared light in biological samples has proven to be a powerful tool for tissue characterization the <strong>attenuation coefficient of light</strong> was calculated for each image. This data is available in the <strong>Neoplastic_processed.zip</strong> and <strong>Non-neoplastic_processed.zip</strong> folders.</p>
Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?
<p>Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?:</p> <p><br>This data set contains all the experimental raw data, analysis and source files for the final figures reported in the manuscript: "Can calmodulin bind to lipids of the cytosolic leaflet of plasma membranes?". It is divided into five (1-5) zipped folders, named as the technique used to obtain the data. Each of them, where applicable, consists of three different subfolders (raw data, analysed data, final graph). Read below for more details. </p> <p>1) ConfocalMicroscopy</p> <p> 1a) Raw_Data: the raw images are reported as .dat and .tif formats, divided into folders (according to date first yymmdd, and within the same day according to composition). Each folder contains a .txt file reporting the experimental details </p> <p> 1b) GUVs_Statistics<br> - GUVs_Statistics.txt explains how we generated the bar plot shown in Fig. 1E</p> <p> 1c) Final_Graph<br> - Figure_1B_1D.png is the figure representing figure 1B and 1D<br> - Figure1E_%ofGUVswithCaMAdsorbptions.csv is the source file x-y of the bar plot shown in figure 1E (% of GUVs which showed adsorption of CaM over the total amount of measured GUVs) <br> - Where_To_Find_Representative_Images.txt states the folders where the raw images chosen for figure 1 can be found </p> <p>2) FCS<br> <br> 2a) Raw_Data: <br> - 1_points: .ptu files <br> - 2_points: .ht3 files <br> - Raw_Data_Description.docx which compositions and conditions correspond to which point in the two data sets<br> <br> 2b) Final_Graphs:<br> - Figure_2A.xlsx contains the x-y source file for figure 2A</p> <p> 2c) Analysis: <br> - FCS_Fits.xlsx outcome of the global fitting procedure described in the .docx below (each group of points represents a certain composition and calcium concentration, read the Raw_Data_Description.docx in the FCS > Raw_Data)<br> - Notes_for_FCS_Analysis.docx contains a brief description of the analysis of the autocorrelation curves</p> <p>3) GPLaurdan<br> <br> 3a) Raw Data: all the spectra are stored in folders named by date (yymmdd_lipidcomposition_Laurdan) and are in both .FS and .txt formats </p> <p> 3b) GP calculations: contains all the .xlsx files calculating the GP values from the raw emission and excitation spectra</p> <p> 3c) Final_Graphs<br> - Data_Processing_For_Fig_2D.csv contains the data processing from the GP values calculated from the spectra to the DeltaGP (GP with- GP without CaM) reported in fig. 2D<br> - Figure_2C_2D.xlsx contains the x-y source file for the figure 2C and 2D</p> <p>4) LiveCellsImaging </p> <p> 3a) Intensity_Protrusions_vs_Cell_Body: <br> - contains all the .xlsx files calculating the intensity of the various images. File renamed by date (yymmdd) <br> - All data in all excel sheets gathered in another Excel file to create a final graph </p> <p> 3b) Final_Graphs<br> - Figure_S2B.xlsx contains the x-y source file for the figure S2B</p> <p>5) LiveCellImaging_Raw_Data: it contains some of the images, which are given in .tif. They are divided by date (yymmdd) and each contains subfolders renamed by sample name, concentration of ionomycin. Within the subfolders, the images are divided into folders distinguishing the data acquired before and after the ionomycin treatment and the incubation time.</p> <p> </p> <p>6) 211124_BioCev_Imaging_1 folder has the .jpg files of the time laps, these are shown in fig 1A and S2.</p> <p>7) 211124_BioCev_Imaging_2 and 8) 211124_BioCev_Imaging_3 contain the images of HeLa cells expressing EGFP-CaM after treatment with ionomycin 200 nM (A1) and 1 uM (A2), respectively. </p> <p><br>9) SPR</p> <p> 9a) Raw Data: <br> - SPR_Raw_Data.xlsx x/y exported sensorgrams <br> - the .jpg files of the software are also reported and named by lipid composition</p> <p> 9b) Final_Graph: <br> - Fig.2B.xlsx contains the x-y source file for the figure 2B</p> <p> 9c) Analysis<br> - SPR_Analysis.xlsx: excel file containing step-by-step (sheet by sheet) how we processed the raw data to obtain the final figure (details explained in the .docx below)<br> - Analysis of SPR data_notes.docx: read me for detailed explanation</p>
Data from: Sorting states of environmental DNA: Effects of isolation method and water matrix on recovery of membrane-bound, dissolved, and adsorbed states of eDNA
<p>Environmental DNA (eDNA) once shed can exist in numerous states with varying behaviors including degradation rates and transport potential. In this study we consider three states of eDNA: 1) a membrane-bound state referring to DNA enveloped in a cellular or organellar membrane, 2) a dissolved state defined as the extracellular DNA molecule in the environment without any interaction with other particles, and 3) an adsorbed state defined as extracellular DNA adsorbed to a particle surface in the environment. Capturing, isolating, and analyzing a target state of eDNA provides utility for better interpretation of eDNA degradation rates and transport potential. While methods for separating different states of DNA have been developed, they remain poorly evaluated due to the lack of state-controlled experimentation. We evaluated the methods for separating states of eDNA from a single sample by spiking DNA from three different species to represent the three states of eDNA as state-specific controls. We used chicken DNA to represent the dissolved state, cultured mouse cells for the membrane-bound state, and salmon DNA adsorbed to clay particles as the adsorbed state. We performed the separation in three water matrices, two environmental and one synthetic, spiked with the three eDNA states. The membrane-bound state was the only state that was isolated with minimal contamination from non-target states. The membrane-bound state also had the highest recovery (54.11 ± 19.24 %), followed by the adsorbed state (5.08 ± 2.28 %), and the dissolved state had the lowest total recovery (2.21 ± 2.36 %). This study highlights the potential to sort the states of eDNA from a single sample and independently analyze them for more informed biodiversity assessments. However, further method development is needed to improve recovery and reduce cross-contamination.</p>
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini. in Nodularia vladivostokensis (Bivalvia: Unionidae) from Razdolnaya River (Primorye, Russia)
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini.
Faa1 membrane binding drives positive feedback in autophagosome biogenesis via fatty acid activation
<p>Autophagy serves as a stress response pathway by mediating the degradation of cellular material within lysosomes. In autophagy, this material is encapsulated in double-membrane vesicles termed autophagosomes, which form from precursors referred to as phagophores. Phagophores grow by lipid influx from the endoplasmic reticulum into Atg9-positive compartments and local lipid synthesis provides lipids for their expansion. How phagophore nucleation and expansion are coordinated with lipid synthesis is unclear. Here, we show that Faa1, an enzyme activating fatty acids, is recruited to Atg9 vesicles by directly binding to negatively charged membranes with a preference for phosphoinositides such as PI3P and PI4P. We define the membrane-binding surface of Faa1 and show that its direct interaction with the membrane is required for its recruitment to phagophores. Furthermore, the physiological localization of Faa1 is key for its efficient catalysis and promotes phagophore expansion. Our results suggest a positive feedback loop coupling phagophore nucleation and expansion to lipid synthesis.</p>
DeepBacs – Artificial labeling of E. coli membranes dataset and fnet/CARE models
<p>Training and test images of <em>E. coli </em>cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes.</p> <p>Additional information can be found on this <a href="https://github.com/HenriquesLab/DeepBacs/wiki">github wiki</a>.</p> <p>Example image shows an <em>E. coli</em> bright field image and PAINT membrane image predicted by the neural network (scale bar is 1 µm).</p> <p> </p> <p><strong>Training and testing dataset</strong></p> <p><strong>Data type</strong>: Paired bright field and super-resolution images</p> <p><strong>Microscopy data type</strong>: Bright field and fluorescence microscopy (widefield and point accumulation for imaging in nanoscale topography (PAINT) images)</p> <p><strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective</p> <p><strong>Cell type</strong>: <em>E. coli </em>K12 strain derivatives</p> <p><strong>File format</strong>: .tif (8-bit)</p> <p><strong>Image size</strong>: 512x512 px<sup>2</sup> with different pixel sizes:</p> <p>1x tube lens: 158 nm (raw) and 19.75 nm (8x upscaled for PAINT images)</p> <p>1.5x tube lens: 106 nm (raw) (widefield fluorescence only)</p> <p> </p> <p><strong>fnet model (PAINT membrane images)</strong></p> <p>The fnet 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 200,000 steps on 33 paired images (image dimensions: (512 x 512 px²), patch size: (128 x 128 px²)) with a batch size of 4, a learning rate of 0.0004, 10% validation split and 4x data augmentation (flipping and rotation).</p> <p>Model weights can be used with the ZeroCostDL4Mic fnet 2D notebook.</p> <p> </p> <p><strong>CARE model (PAINT membrane images):</strong></p> <p>The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 300 epochs (100 steps/epoch) on 33 paired images (image dimensions: 512 x 512 px², patch size: 256 x 256 px²) with a batch size of 4, a learning rate of 0.0004, 90/10% train/validation split and 4x data augmentation (flipping and rotation).</p> <p>Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin.</p> <p><br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup></p> <p><strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de</p> <p> </p> <p><strong>Affiliation(s)</strong>: </p> <p>1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany</p> <p>2) ORCID: 0000-0001-9886-2263 </p> <p>3) ORCID: 0000-0002-9821-3578</p>
Reference data and analysis software for "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane"
<p>Reference data set for the single molecule co-tracking analysis presented in "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane". Corresponding author for further inquiries:</p> <p>Prof. Dr. Jacob Piehler</p> <p>University of Osnabrück, Department of Biology/Chemistry, Division of Biophysics, Barbarastr. 11, 49076 Osnabrück, Germany</p> <p>https://www.biophysik.uni-osnabrueck.de/</p>
A role for myosin II cluster and membrane energy in cortex rupture for Dictyostelium discoideum cells
<p>Blebs, pressure driven protrusions of the cell membrane, facilitate the movement of eukaryotic cells such as the soil amoeba <em>Dictyostelium discoideum</em>, white blood cells and cancer cells. Blebs initiate when the cell membrane separates from the underlying cortex. A local rupture of the cortex, has been suggested as a mechanism by which blebs are initiated. However, much clarity is still needed about how cells inherently regulate rupture of the cortex in locations where blebs are expected to form. In this work, we examine the role of membrane energy and the motor protein myosin II (myosin) in facilitating the cell driven rupture of the cortex. We perform under-agarose chemotaxis experiments, using <em>Dictyostelium discoideum</em> cells, to visualize the dynamics of myosin and calculate changes in membrane energy in the blebbing region. To facilitate a rapid detection of blebs and analysis of the energy and myosin distribution at the cell front, we introduce an autonomous bleb detection algorithm that takes in discrete cell boundaries and returns the coordinate location of blebs with its shape characteristics. We are able to identify by microscopy naturally occurring gaps in the cortex prior to membrane detachment at sites of bleb nucleation. These gaps form at positions calculated to have high membrane energy, and are associated with areas of myosin enrichment. Myosin is also shown to accumulate in the cortex prior to bleb initiation and just before the complete disassembly of the cortex. Together our findings provide direct spatial and temporal evidence to support cortex rupture as an intrinsic bleb initiation mechanism and suggests that myosin clusters are associated with regions of high membrane energy where its contractile activity leads to a rupture of the cortex at points of maximal energy.</p>
Fig. 2 in Comparative Aspects Of The Morphogenesis And Morphology Of The Wing Membranes Of Bats (Сhiroptera) And Flying Lemurs (Dermoptera)
Fig. 2. Hand and wing membrane of embryo Cynocephalus variegatus, stage 20. Longitudinal sections. The right forearm. А, D, E, F — x400; B, C — x1000: А — longitudinal (below) and cross-section (from above) of the propatagium skin; B, C — the muscle tubes in the plagiopatagium skin; D — the two row of muscle tubes in the plagiopatagium skin; E, F — the chiropatagium skin. Epidermis (ЕPD), undifferentiated mesenchyme (М), blood vessel and blood capillaries (V and CAP), muscle tubes (MT), muscles (MUS), rudiments of digits I (I) and II (II); IV (IV) and V (V).Stained with Mallory's trichrome.
Fig. 1 in Comparative Aspects Of The Morphogenesis And Morphology Of The Wing Membranes Of Bats (Сhiroptera) And Flying Lemurs (Dermoptera)
Fig. 1. Hand and wing membrane of bats embryos. А, B, C — x400; D — x1000: А — embryo Myotis blythii stage 18. Longitudinal section. The left forelimb bud with metacarpals rudiments: mesenchymal condensations of metacarpal rudiments (Mc), undifferentiated mesenchime (M), epidermis (EPD). Stained with Ehrlich's hematoxylin and eosin; В — embryo Rhinolophus hipposideros stage 20. Cross-section. The wing membrain (uropatagium). The centre of hemopoiesis (G), epidermis (ЕPD), undifferentiated mesenchyme (М), blood vessels (V). Stained with Mallory's trichrome; C — embryo Myotis blythii stage 19. Longitudinal section. The right forearm. Metacarpal rudiments (Mc), digits rudiments (II III, IV, V), epidermis (ЕPD), undifferentiated mesenchyme (М), blood vessels (V). Stained with Ehrlich's hematoxylin and eosin; D — embryo Myotis blythii stage 22. Cross-section of the plagiopatagium skin. The centre of hemopoiesis (G), mesenchyme (М), epidermis (ЕPD). Stained with Ehrlich's hematoxylin and eosin.
To what extent naringenin binding and membrane depolarization shape mitoBK channel gating - a machine learning approach (code and dataset)
<p>The dataset consists of dwell-time series (sampling frequency 100 kHz) of the mitoBK ion channel activation modulated by the naringenin binding and membrane<br> depolarization. It also contains the code written in Python, with the use of tslearn and scikit-learn packages, classifying the dwell-time subseries into right categories.</p> <p>The dataset is organized as follows. The mitoBK_ML.zip directory consists of two directories:</p> <ol> <li><strong>dwell times </strong>containing 5 subdirectories comprising groups of dwell-time subseries obtained at different pipette potentials and naringenin concentration. First number in the name od directory stands for the applied voltage in mV, whilst the second one denotes the naringenin concentration in µmol. For instance, directory named 20_3 means that the obtained dwell-times series were obtained at 20 mV (value of pipette potential) and 3 µmol (concentration of naringenin). These subdirectories are named as follows:</li> </ol> <ul> <li><strong>1group </strong>comprising dwell time series <strong>20_3, 40_1, 60_0</strong></li> <li><strong>2group</strong> comprising dwell-time series <strong>20_10, 60_1</strong></li> <li><strong>3group</strong> comprising dwell-time series <strong>40_10</strong>, <strong>60_3</strong></li> <li><strong>naringenina</strong> comprising dwell-time series <strong>60_0, 60_10</strong></li> <li><strong>voltage</strong> comprising dwell-time series <strong>20_10, 60_10</strong></li> </ul> <p><strong>1group, 2group and 3group</strong> contain the dwell-time series with approximately the same value of open-state probability of the ion channel.</p> <p>The <strong>naringenina</strong> contains the dwell-time series with the same value of potential (60 mV) and different values of naringenin concentration (0 µmol and 10 µmol). </p> <p>The <strong>voltage </strong>contains the dwell-time series with the same value of naringenin concentration (10 µmol) and different values of applied voltage (20 mV and 60 mV).</p> <p> 2. <strong>rslt </strong>is organized analogously to <strong>dwell times. </strong>The subdirectories are empty, but they will be filled with the results after launching the Python scripts placed in the <strong>knn_ion_channel.ipynb</strong> or <strong>shapelet_ion_channel.ipynb </strong>files.</p> <p>The Python code is placed in two files:</p> <ol> <li><strong>knn_ion_channel.ipynb </strong>containing kNN (<em>k-Nearest Neighbors</em>) algorithm classifying dwell-time series belonging to one of 5 different categories enumerated above: <strong>1group, 2group, 3group, naringenina, voltage</strong>. More detailed description of the code can be found inside uploaded Jupyter notebook.</li> <li><strong>shapelet_ion_channel.ipynb </strong>containing <em>shapelet-learning algorithm</em> classifying dwell-time series belonging to one of 5 different categories enumerated above. <strong>1group, 2group, 3group, naringenina, voltage. </strong>More detailed description of the code can be found inside uploaded Jupyter notebook.</li> </ol> <p> </p> <p> </p> <p> </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>
Hippocampal CA1 pyramidal cell membrane voltage recorded in response to noise stimuli at two temperatures.
<p>Electrophysiological recording of the membrane voltage (whole-cell patch-clamp) of three hippocampal CA1 pyramidal cells. Cells are stimulated with a current step chosen to ensure a firing rate around 5-10Hz (amplitude of the current step is given in the filenames) and a noise stimulus with zero mean (Ornstein-Uhlenbeck process with 4ms timescale). Each CSV file contains three columns, the timepoints (saved at 10000Hz), the noise stimulus, and the voltage trace recorded in response to the given noise stimulus. Voltages are recorded at low temperatures (around 32 degrees Celsius) and at high temperatures (around 37 degrees Celsius for cell 1, around 40 degrees Celsius for cells 2 and 3), exact temperatures are given in the filenames. The file metadata.csv contains additional information.</p>
A partnership between the lipid scramblase XK and the lipid transfer protein VPS13A at the plasma membrane
<p>This upload contains files documented in a preprint and a publication.</p> <p>Preprint: https://doi.org/10.1101/2022.03.30.486314</p> <p>Publication: <a href="https://doi.org/10.1073/pnas.2205425119">https://doi.org/10.1073/pnas.2205425119</a></p> <p>The files uploaded here are:</p> <p>- Alphafold predictions for VPS13A N-term (a.a. 1-2100) and C-term (a.a. 1021-3174). The .pse file is the pymol structure alignment of the two predicted VPS13A portions, join at aminoacid position D14 with the different representations presented throughout the paper stored as pymol "scenes". </p> <p>- AlphaFold-Multimer prediction for the interaction between XK and the C-term region of VPS13A is also included.</p> <p>- An excel file containing the tabular data for the graphs in Figures 1G, S2E and 4D.</p>
Dataset to "Bulk thermodynamics determines surface hydrogen concentrations in membranes"
<p>Dataset to "Bulk thermodynamics determines surface hydrogen concentrations in membranes" as published in Advanced Materials Interfaces</p>
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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.