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Figure 3 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 3 FTIR spectrum of graphene oxide.
Figure 5 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 5 Raman spectrum of graphene oxide.
Figure 4 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 4 FTIR spectrogram of thermally reduced graphene oxide.
Figure 2 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 2 UV-Vis absorption spectrum of tetracycline hydrochloride.
Figure 1 from: Dedic M, Gutic S, Gicevic A, Becic E, Imamovic B, Markovic D, Ziga-Smajic N (2020) Application of membrane filters in determination of the adsorption of tetracycline hydrochloride on graphene oxide. Pharmacia 67(4): 339-345. https://doi.org/10.3897/pharmacia.67.e57242
Figure 1 Structure of graphene in the form of a hexagonal lattice (Garg et al. 2016).
Cellular electrical properties of specific membrane capacitance and cytoplasmic conductivity
<p>The submitted datasets contain the original data of the cell's electronic characteristic parameters of specific membrane capacitance and cytoplasmic conductivity.</p> <p>The cell types include H1299 cells treated with DMSO and 4-Mu, 95C cells treated with DMSO and 4-Mu, A549 control cells and A549 sg-RNA3 cells. The <span>scatter plots and bar graphs demonstrated the differences of electroninc properties were according to these datasets.</span></p>
Genetically Encoded Supramolecular Targeting of Fluorescent Membrane Tension Probes within Live Cells: Precisely Localized Controlled Release by External Chemical Stimulation
<p>Original data. Second part at </p> <pre>https://doi.org/10.5281/zenodo.4322986</pre>
Data from: Energy landscape for the insertion of amphiphilic nanoparticles into lipid membranes: a computational study
Amphiphilic, monolayer-protected gold nanoparticles (NPs) have been shown to enter cells via a non-endocytic, non-disruptive pathway that could be valuable for biomedical applications. The same NPs were also found to insert into a series of model cell membranes as a precursor to cellular uptake, but the insertion mechanism remains unclear. Previous simulations have demonstrated that an amphiphilic NP can insert into a single leaflet of a planar lipid bilayer, but in this configuration all charged end groups are localized to one side of the bilayer and it is unknown if further insertion is thermodynamically favorable. Here, we use atomistic molecular dynamics simulations to show that an amphiphilic NP can reach the bilayer midplane non-disruptively if charged ligands iteratively ``flip'' across the bilayer. Ligand flipping is a favorable process that relaxes bilayer curvature, decreases the nonpolar solvent-accessible surface area of the NP monolayer, and increases attractive ligand-lipid electrostatic interactions. Analysis of end group hydration further indicates that iterative ligand flipping can occur on experimentally relevant timescales. Supported by these results, we present a complete energy landscape for the non-disruptive insertion of amphiphilic NPs into lipid bilayers. These findings will help guide the design of NPs to enhance bilayer insertion and non-endocytic cellular uptake, and also provide physical insight into a possible pathway for the translocation of charged biomacromolecules.
Coarse-grained simulations of the TRAP subunits in a multi-component membrane
<p>to be added</p>
Coarse-grained simulations of the Sec61 and TRAP complexes in a POPC membrane
<p>to be added</p>
HydroFlipper membrane tension probes
<p>Original data</p>
In-operando visualization of redox flow battery in membrane-free microfluidic platform
<p>These data sheets are voltage-capacity characteristics of in-operando visualization in terms of mass-, and charge-transfer. </p> <p> </p>
Coarse-grained simulations of protein partitioning in membranes with varying levels of SDPE
<p>The free energies of transfer between liquid ordered (Lo) and liquid disordered (Ld) domains was calculated for three proteins (Adenosine A2 receptor, A2AR; dopamine D2 receptor, D2R; and glucose transporter (GLUT)) using coarse-grained molecular dynamics simulations together with free energy perturbation. Each of these proteins was embedded in a Lo membrane consisting of DSPC, cholesterol, and varying levels (0–16 mol%) of SDPE. First, the lipids were allowed to reorganize during a 10 microsecond simulations. Then, for each protein and for each SDPE concentration, the DSPC lipids were mutated into DOPC ones and the free energy of this change was calculated. This process was repeated in the absence of the proteins for each SDPE concentration. By substracting the corresponding values for the protein-containing and protein-free cases, the free energy of transfer between Lo and Ld domains was calculated for each of the proteins as a function of SDPE concentration. All simulations were performed using the Martini force field [1] and the GROMACS simulation package [2]. For more details of the setup and the simulations, please see the related paper at DOI: 10.1371/journal.pcbi.1007033.</p> <p>The original equilibration simulations (10 microseconds in the presence of the protein and SDPE and shorter in the absence of either SDPE or the protein). The run input files for these simulations are provided as X_PUFAY_EQ.tpr and the trajectories as X_PUFAY_EQ.xtc, where X stands for the protein (or says 'NOPROT' for the case without protein) and Y for the SDPE concentration.</p> <p>The run input files for the free energy of transfer simulations are provided as the tar files; one tar for each protein and one for the protein-free case. Each of these tars contains a folder for each used SDPE concentration, including the more realistic membrane compositions (with a suffix 'REAL'). For each SDPE concentration, the run input files (tpr) and the simulation input parameter files (mdp) for each window in the free energy perturbation are provided. Here, the windows are numbered from 0 to 26, where 0 corresponds to DSPC and 26 to DOPC. Also, for each SDPE concentration, common topology (top), index (ndx) and start configuration (gro) are provided. The molecule definitions (itp) are provided in the TOP.tar archive.</p> <p>[1] <strong>DOI: </strong>10.1021/jp071097f ; <strong>DOI: </strong>10.1021/ct700324x ; <strong>DOI: </strong>10.1021/ct300646g</p> <p>[2] <strong>DOI: </strong>10.1016/j.softx.2015.06.001</p>
Human gut commensal Alistipes timonensis modulates the host lipidome and delivers anti-inflammatory outer membrane vesicles to suppress colitis in an Il10-deficient mouse model
Open the record for dataset details and reuse information.
Confocal microscopy data associated with "The conserved aphid saliva chemosensory protein effector Mp10 targets plant AMSH deubiquitinases at cellular membranes to suppress pattern-triggered immunity"
<p><strong>Confocal microscopy data as described in "The conserved aphid saliva chemosensory protein effector Mp10 targets plant AMSH deubiquitinases at cellular membranes to suppress pattern-triggered immunity".</strong></p> <p> </p> <p>Data relate to Figure 2 (“Mp10 interacts with AMSH deubiquitinases in yeast and plants”) involving FLIM-FRET imaging data to determine the interaction between eGFP-tagged <em>Myzus persicae </em>Mp10 and mCherry-tagged <em>Nicotiana benthamiana </em>AMSH proteins in plant cells; and Figure 6 (“Mp10 affects the abundance and localisation of cell-surface receptor-like kinases) involving confocal microscopy showing the effect of Mp10-expression on the localisation of the GFP-tagged FLS2 receptor-kinase protein, and it’s colocalization with RFP-tagged markers of the plasma membrane and the tonoplast in plant cells.</p> <p>Constructs encoding fluorescent protein fusions were transformed into <em>Agrobacterium tumefaciens </em>GV3101, and mixed Agrobacterium cultures were infiltrated into mature leaves of <em>N. benthamiana </em>plants to co-express the desired combinations of proteins. All image data was gathered from lower epidermal leaf cells of infiltrated leaves 2-3 days post infiltration.</p> <p> </p> <p><strong>FLIM-FRET assays.</strong></p> <p>eGFP-tagged Mp10, or eGFP-alone, was co-expressed with mCherry-tagged AMSH proteins, or mCherry fused to aquaeorin in <em>N. benthamiana</em> via agroinfiltration as described above. Lower epidermal cells of leaf sections were imaged 2-3 days after infiltration using a Leica Stellaris 8. Images were captured detecting fluorescence from eGFP (WLL laser, ex.488 nm, em 509-534 nm.) mCherry (lWLL aser, ex. 587 nm em 603-625 nm.) and chlorophyll (WLL laser, ex 587 nm. em 687-712 nm.). Regions of cells showing expression of both eGFP- and mCherry- tagged proteins but lacking chlorophyll were selected for FLIM analysis to avoid bleed through of chlorophyll fluorescence into the eGFP chanel. Fluorescence lifetime data of EGFP were collected from these regions in FLIM mode (WLL laser ex. 488nm, em 525-530 nm.), data were collected at 128x128 resolution until 1000 photons per pixel were collected for the most intense regions of the image. Instrument response function was captured using erythrosine on each day of data collection. FLIM data were analysed using Leica LASX FLIM FCS software. Fluorescence lifetime decay curves of free eGFP control samples were modelled as a 2-component exponential function, and all samples from each experimental set were modelled against the fluorescence lifetime from the corresponding control samples to derive values for fluorescent lifetime and %FRET efficiency for each image collected. %FRET efficiency was mapped to the images and phasor plots were generated for regions with the highest and lowest FRET efficiency, showing that the FRET signal was associated with a clockwise shift on the phasor plot consistent with <em>bona-fide </em>FRET. Full experimental metadata for each image set are included within the .lif files.</p> <p> </p> <p><strong>FLS2-GFP localisation experiments.</strong></p> <p>Confocal microscopy analysis was performed on a Leica TCS SP8X confocal DM6 microscope with a 63x water-immersion objective, using Leica Application Suite X (LAS X) software (3.5.7.23225). eGFP and chlorophyll signals were excited by a 488 nm Argon laser with emission, respectively, at 495–545 nm and 690-710 nm. RFP signal was excited by a 590 nm white light laser (WLL) with emission at 605–650 nm. Full experimental metadata for each image set are included within the .lif files.</p> <p> </p> <p>Leica Image Files (.lif) that contain multiple images including metadata associated with image acquisition and processing.</p> <p> </p> <p>FLIM030823.lif</p> <p>FLIM130724.lif</p> <p>FLIM140623.lif</p> <p>FLIM160623.lif</p> <p>FLIM240523.lif</p> <p>FLIM250523.lif</p> <p>Files include FLIM-FRET data as shown in Figure 2 parts D-L.</p> <p>FLIM-data-files.xlsx includes a description of the individual image filenames, and the combinations of fluorophore imaged in each.</p> <p> </p> <p>Fig6cSlFLS2-gfp_Mp10-RFP.lif</p> <p>Fig6c-SlFLS2-GFP_EV-RFP.lif</p> <p>Correspond to Figure 6 C showing co-expression of RFP-tagged Mp10 (or free RFP control) co-expressed with GFP-tagged FLS2</p> <p> </p> <p>20230828_SlFLS2-GFP_Flag-Mp10_Flag-alone_Remorin-RFP-3-3-1x.lif</p> <p>20230828_SlFLS2-GFP_Flag-Mp10_Flag-alone_Remorin-RFP-3-3-3x.lif</p> <p>20230828_SlFLS2-GFP_Flag-Mp10_Flag-alone_Remorin-RFP-4-2-3x.lif</p> <p>0230828_SlFLS2-GFP_Flag-Mp10_Flag-alone_Remorin-RFP4-2-1x.lif</p> <p>Correspond to Figure 6 D showing co-expression of FLAG-tagged Mp10 (or free FLAG control) co-expressed with GFP-tagged FLS2 together with RFP-tagged plasma membrane marker Remorin</p> <p> </p> <p>20230911_SlFLS2-GFP_Flag-Mp10_Flag-alone_StSUC4-RFP-14-5-1x.lif</p> <p>20230911_SlFLS2-GFP_Flag-Mp10_Flag-alone_StSUC4-RFP-14-5-3x.lif</p> <p>20230911_SlFLS2-GFP_Flag-Mp10_Flag-alone_StSUC4-RFP-16-1-1x.lif</p> <p>20230911_SlFLS2-GFP_Flag-Mp10_Flag-alone_StSUC4-RFP-16-1a-3x.lif</p> <p>20230911_SlFLS2-GFP_Flag-Mp10_Flag-alone_StSUC4-RFP-16-1b-1x.lif</p> <p>Correspond to Figure 6 E showing co-expression of FLAG-tagged Mp10 (or free FLAG control) co-expressed with GFP-tagged FLS2 together with RFP-tagged tonoplast marker SUC4.</p> <p> </p> <p>We are grateful to the John Innes Centre (JIC) Bioimaging Platform for training and technical support</p> <p>This work was funded by UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) grants to SAH (BB/V008544/1 and BB/N009169/1), Additional Support was provided by the BBSRC Institute Strategy Programmes (BBS/E/J/000PR9797 and BBS/E/JI/230001B) awarded to the John Innes Centre (JIC). The JIC is grant-aided by the John Innes Foundation.</p> <p> </p>
Data from: The AAA protein Msp1 mediates clearance of excess tail-anchored proteins from the peroxisomal membrane
Msp1 is a conserved AAA ATPase in budding yeast localized to mitochondria where it prevents accumulation of mistargeted tail-anchored (TA) proteins, including the peroxisomal TA protein Pex15. Msp1 also resides on peroxisomes but it remains unknown how native TA proteins on mitochondria and peroxisomes evade Msp1 surveillance. We used live-cell quantitative cell microscopy tools and drug-inducible gene expression to dissect Msp1 function. We found that a small fraction of peroxisomal Pex15, exaggerated by overexpression, is turned over by Msp1. Kinetic measurements guided by theoretical modeling revealed that Pex15 molecules at mitochondria display age-independent Msp1 sensitivity. By contrast, Pex15 molecules at peroxisomes are rapidly converted from an initial Msp1-sensitive to an Msp1-resistant state. Lastly, we show that Pex15 interacts with the peroxisomal membrane protein Pex3, which shields Pex15 from Msp1-dependent turnover. In sum, our work argues that Msp1 selects its substrates on the basis of their solitary membrane existence.
A retrospective 10-year mean follow-up of implants placed in ridges grafted using autogenous mandibular blocks covered with bovine bone mineral and collagen membrane
<p>Abstract<br> Objectives: To report the clinical and radiographic outcomes of patients presenting<br> with edentulous and atrophic ridges and treated with autogenous mandibular bone<br> blocks and rehabilitated with implant-supported prostheses.<br> Materials and methods: From 1997 to 2015, 75 patients presenting with bone defects<br> of the jaws were grafted with autogenous mandibular bone blocks. One-hundred<br> eighty-two implants were placed 4 to 12 months later and loaded 3 to 10 months<br> later: the mean follow-up was 10 years (range: 3–16 years). The following outcomes<br> were recorded: (a) complication rate of the reconstructive procedure; (b) bone graft<br> resorption before implant placement; (c) peri-implant bone resorption; (d) implantrelated<br> complications; and (e) implant survival and success rates.<br> Results: Postoperative recovery was uneventful in the majority of patients. An early<br> dehiscence occurred in three patients, but with no significant bone loss, while 3 experienced<br> temporary paraesthesia. The mean vertical and horizontal bone resorption<br> before implant placement was 0.18 mm (standard deviation [SD] = 0.43) and 0.15 mm<br> (SD = 0.42), respectively. The mean peri-implant bone loss ± standard deviation was<br> 1.06 ± 1.19 (range: 0.00–5.05) at patient level and 1.11 ± 1.26 (range: 0.00–5.20)<br> at implant level. Two implants in 2 patients lost integration and were removed; 10<br> implants in 7 patients developed peri-implantitis, but healed after surgical treatment.<br> The cumulative implant survival and success rates were 98.11% and 85.16%,<br> respectively.<br> Conclusion: Implants placed in areas reconstructed with mandibular bone blocks presented<br> survival rates consistent with those obtained for implants placed in native<br> bone.</p>
Pure POPE membrane simulations with the AMOEBA force field at 310K (OpenMM)
<p>MD simulation data of a pure POPE membrane with the AMOEBA force field generated with the OpenMM simulation engine at 310K.</p> <p>Parameters from https://doi.org/10.1080/00268976.2018.1436201</p>
Pure POPC membrane simulations with the CHARMM-Drude force field (OpenMM 7.5.0)
<p>MD simulation data of a pure POPC membrane with the CHARMM-Drude force field.</p> <p>All the input parameters are available in the *inp file.</p> <p>Total simulation duration is 500 ns. First 100 ns is discarded as equilibration. This data set contains 400 ns data with 40000 frames (saving frequency is 10 ps).</p> <p>In total, 64 POPC lipids in each leaflet, 6400 SWM4-NPD water molecules.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. The correction to the timestamp was done via MDAnalysis.</strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p> </p>
Membrane free-energy landscapes derived from atomistic dynamics explain nonuniversal cholesterol-induced stiffening - Sample files
<p><strong>Initial and final snapshots of atomistic simulations of curved lipid bilayers</strong></p> <p>Each PDB file is named according to lipid composition and number of lipid molecules per leaflet.</p> <p>To reduce file size, solvent molecules were excluded and multiple snapshots of the same system zipped together.</p> <p>Reference: <a href="https://doi.org/10.1101/2023.02.02.525347">https://doi.org/10.1101/2023.02.02.525347</a></p>
ScienceDex guides
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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.