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568 results for “condensate”
Exhaled breath condensate samples (mzML files) analysed by uHPLC-ESI-OrbitrapMS
<p>LC-MS raw data files<strong> </strong>were converted to mzML using MSConvert (Version: 3.0.20279, ProteoWizard)</p> <p>- QC_pos_xxx: QC samples analysed in positive polarity (11 files)<br> - ACOS_XXX_pos_01 to 03: Asthmatic patients (3 replicates) randomly injected (15 files)<br> - Ctrl_XXX_pos_01 to 03: Control (15 files)<br> - DPOC_XXX_pos_01 to 03: COPD (15 files)</p> <p> </p> <p><strong>EBC Samples and Clinical Assessment</strong></p> <p>EBC samples were collected from 15 individuals randomly selected (five controls, five with asthma medical diagnosis, and five with COPD medical diagnosis, as assessed by the OLDER Study – Obstructive Lung Diseases in Elders). The Ethics Committee of Nova Medical School approved this study.</p> <p>Asthma was assigned when the patient reported respiratory symptoms, was a nonsmoker, and presented a positive reversibility test (FEV1 > 12% and 200 mL). COPD disease was attributed to those who also reported being current smokers, had a post-bronchodilator FEV1/FVC < 0.70, and had a negative reversibility test.</p> <p><strong>LC–MS Analysis</strong></p> <p>Samples were analyzed in triplicate by LC–MS using an Orbitrap Q Exactive Focus (Thermo Scientific) coupled to an Ultimate 3000 UHPLC (Thermo Scientific). A pooled quality control (QC) sample was used to compensate for any possible time-dependent batch effects. The QC samples were created using a small aliquot from each sample. The QC samples were reinjected at regular intervals to bracket the samples. The separation was performed using a Waters XBridge column C18 (2.1 × 150 mm, 3.5 μm particle size, P/N 186003023). The mobile phase A was water with 0.1% formic acid (v/v), and mobile phase B was acetonitrile with 0.1% formic acid (v/v) (Optima LC–MS Grade, Fisher Scientific). The gradient program was as follows: 1 min at 1% B; 1–13 min from 1 to 99% B, 13–15 min at 99% B, 15–16 min from 99 to 1% B, and 4 min at 1% B. The column temperature was maintained at 30 °C, and a flow rate of 400 μL/min was used.</p> <p>The Q Exactive Focus MS method consisted of several cycles of full MS scan (<em>R</em> = 70000) followed by three ddMS2 scans (<em>R</em> = 17500), with a (N)CE of 30 and in positive mode. External calibration was performed using LTQ ESI Positive Ion Calibration Solution (Thermo Scientific) and the lock mass enabled internal calibration. Data were obtained using the Xcalibur software v.4.0.27.19 (Thermo Scientific). The raw MS files, as recorded by the instrument, are available from the corresponding author upon reasonable request.</p>
Tracking the photomineralization mechanism in irradiated lab-generated and field-collected brown carbon samples and its effect on cloud condensation nuclei abilities
<p>Data set of the data presented in figures and tables in our manuscript on the photomineralization of brown carbon samples: ammonium sulfate-methylglyoxal solutions, Suwannee River fulvic acid isolates, firewood smoke and ambient aerosols from Padua, Italy.</p>
Exploring Bifurcations in Bose-Einstein Condensates via Phase Field Crystal Models
<p>Supplementary data for the following paper: Alina Barbara Steinberg, Fabian Maucher, Svetlana Gurevich, Uwe Thiele, "Exploring Bifurcations in Bose-Einstein Condensates via Phase Field Crystal Models"</p>
Data for the publication: Bursting of condensates
<p>This entry includes the raw data and the analysed data for the publication "Bursting of condensates". The original publication was published in Communications Physics: https://doi.org/10.1038/s42005-024-01650-5</p> <p>The "Analysed_Data.zip" folder contains 5 subfolders with the name of the proteins. Each subfolder contains the analysed data from the videos as .txt files. The type of data is specified in the name: aspiration, retraction, bursting_radius, curvilinear_coordinate. The "Video_protein_name.zip" folders contain the videos in the .tif format obtained with optical microscopy. The applied pressure (Pa) as well as the fps are indicated in the file name. The folder "Stability_coalescence_data.zip" contains the analysed coalescence data as .avi and the images of the time stability and coalescence analysed for CBM-AQ12-CBM. The folder "Microscopy_data.zip" contains the microscopy images. Please find more information in the read_me files uploaded.</p>
Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface
<h3>Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface</h3> <p>We ran simulations for four different systems:</p> <ul> <li>WT: A1-LCD WT (N=137), wild-type (WT) sequence of the low-complexity domain (LCD) of the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), with electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T260: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T290: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=290K</li> <li>HP: homopolymer consisting of prolines (N=137), at temperature T=550</li> </ul> <p>For every system, we ran five independent simulations over 5µs (1000 frames) and used the last 900 frames (4.5µs) for our analysis.</p> <p>This data repository consists of<br> (1) folders containing the data for every seperate run (*_i, i=1,2,3,4,5) in simulation units<br> (2) folders containing the averaged data of all five runs (*_AVG), converted to SI units<br> (3) a droplet folder, containing the data (square radius of gyration and asphericity) for the whole droplet (for all four systems, all five runs)<br>Units are also clarified in each file's header.</p> <p>The simulation units can be converted to SI units via:</p> <ul> <li>Distance: D = 0.45nm</li> <li>Mass: M = 57.05amu</li> <li>Energy: epsilon = 0.2 kcal/mol</li> </ul> <p> </p> <p>Details for (1) and (2):<br>Each folder (*_i, i=1,2,3,4,5, and *_AVG) contains the following subfolders and files:</p> <p><strong>Ree:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ_{ee}) of the whole chains, where θ_{ee} is the angle between the polymer's center r_c and the chain’s end-to-end vector Ree [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_{ee,s}) of segment seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_{ee,s}) of all segments seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos.dat: distribution of the polymer end positions (whole chains) [Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos_segment_i.dat: distribution of the polymer segment end positions of seg_i</li> <li>distribPolymerEndPos_segment_all.dat: distribution of the polymer segment end positions of all segments</li> <li>distribPolymerRee2_all.dat: distribution of Ree^2 (whole chains), binned via polymer center position r_c</li> <li>distribPolymerRee_segment_i.dat: distribution of Ree^2 of segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerRee_segments_all.dat: distribution of Ree^2 of all segments, binned via segment center position r_{c,s}</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all polymer segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>Rg:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ) of the whole chains, where θ is the angle between the polymer's center r_c and the eigenvector belonging to the largest eigenvalue of the chain’s gyration tensor [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_s) of segment seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_s) of all segments seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribMonomerRg_all.dat: distribution of monomer weighted Rg^2 (whole chains), referred to as R_{g,mono}^2 (following Farag et. al) [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_all.dat: distribution of Rg^2 (whole chains), binned via polymer center position r_c [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_segment_i.dat: distribution of Rg^2 of segment seg_i, referred to as R_{g,s}^2, binned via segment center position r_{c,s} [Fig. S4a, Fig. S7a, Fig. S10a, Fig. S13a]</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>resDist:</strong></p> <ul> <li>distribPolymerRee2_base_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRee2_center_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRg2_base_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> </ul> <p>distribPolymerRg2_center_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14] </p> <p> </p> <p>Details for (3):<br>The folder '<strong>droplet</strong>' contains four system folders (HP, WT, WT_noEL_T260, WT_noEL_T290). Each of those folders contains the following files:</p> <ul> <li>runX_cluster_Rg2_Rg2Normal_kappa2.dat: for every run X, one finds the time evolution (in simulation units, with 1e8 timesteps = 1µs) of the square radius of gyration Rg2 of the full droplet, its x-, y- and z-components, its three eigenvalues and the droplet asphericity A (referred to as kappa2 in the header) [Fig.S1c, Fig.S1d]</li> <li>AVG_cluster_Rg2_Rg2Normal_kappa2.dat: average of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> <li>STD_cluster_Rg2_Rg2Normal_kappa2.dat: standard deviation of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> </ul>
Experimental CCN properties of 6 pollenkitts and two mixtures with ammonium sulfate reported in the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical dry particle size Dp<sub>50</sub> measured for supersaturations 0.1–1.4% with a DMT CCN Counter (CCN-100). Size and composition resolved, and size-averaged hygroscopicity values calculated using the method presented by Rose et al. (2010), Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China - Part 1: Size-resolved measurements and implications for the modeling of aerosol particle hygroscopicity and CCN activity, <em>Atmospheric Chemistry and Physics</em>, <em>10</em>, 3365–3383.</p>
Experimental surface tensions of aqueous poplar and ragweed pollenkitts reported in the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Surface tensions measured with a ramé-hart goniometer (Model 250) for aqueous solutions of varying concentrations comprising poplar and ragweed pollenkitts and their mixtures with ammonium sulfate</p>
Critical droplet properties for particles containing 6 pollenkitts evaluated with 4 Köhler models for the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical droplet supersaturation and surface tension, calculated with 3 thermodynamic Köhler models presented in Prisle et al., <em>Surfactants in cloud droplet activation: mixed organic-inorganic particles</em>, Atmospheric Chemistry and Physics, 10, 5663-5683, doi:10.5194/acp-10-5663-2010, and with the model presented in Prisle et al., <em>A simple representation of surface active organic aerosol in cloud droplet formation</em>, Atmospheric Chemistry and Physics, 11, 4073-4083, doi: 10.5194/acp-11-4073-2011, for particles of varying size comprising 6 pollenkitts and mixtures with ammonium sulfate.</p>
Proteomic characterization of human exhaled breath condensate.
<p>datasets from 3 studies, for In-depth proteomics characterization of exhaled breath condensate (EBC).</p> <p>1) Lacombe M. et al, 2018</p> <p>2) Muccilli V. et al, 2015</p> <p>3) Bredberg A. et al, 2012</p>
Data for the publication "Impact of Isolated Atmospheric Aging processes on the Cloud Condensation Nucleiactivation of Soot Particles"
<p>The repository contains the data for the paper:</p> <p>Friebel, F., Lobo, P., Neubauer, D., Lohmann, U., Drossaart van Dusseldorp, S., Mühlhofer, E., and Mensah, A. A.: Impact of isolated atmospheric aging processes on the cloud condensation nuclei activation of soot particles, Atmos. Chem. Phys., 19, 15545–15567, https://doi.org/10.5194/acp-19-15545-2019, 2019.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3452036)</p>
Terrestrial Lidar Point Cloud Data for: Evaporation and condensation dynamics within saturated epiphyte communities in a Quercus virginiana forest
<p><span>Terrestrial lidar scans were captured using a BLK360 scanner (Leica Geosystems, Norcross, GA, USA) which has a range of 0.5 – 45 m and measurement rate up to 680,000 points s<sup>−1</sup> at the high-resolution setting. A georeferenced, 3-D point cloud of the study site was generated from 12 scans, approximately 50 m apart in both horizontal directions. Scans were performed in orientations intended to maximize branch exposure to the scanner and to scan during optimal weather conditions to minimize occlusion of features due to noise or movement generated by wind. Scan co-registration was done in Leica Geosystem’s Cyclone Register 360 software using its Visual Simultaneous Localization and Mapping algorithm (Visual SLAM) and resulted in relatively low overall co-registration error ranging from 0.005-0.009 m. From this study site point cloud, manual straight-line measurements from the ground to the sensors were made using Leica’s Cyclone Register 360 software.</span></p>
Measurement of solubility product reveals the interplay of oligomerization and self-association for defining condensate formation
<p>This data set includes the raw confocal microscopy images, DLS, and SEC-MALS data used for Chattaraj, Baltaci, et al. <em>Mol. Biol. Cell.</em> 2024. </p>
Dataset for "On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles"
<p>Data for Kulmala et al. (2024 )"On the potential of the Cluster Ion Counter (CIC) to observe local new particle formation, condensation sink and growth rate of newly formed particles" (https://doi.org/10.5194/ar-2024-14).</p> <p>Included in the file are number concentrations of sub-2 nm ions and 2-2.3 nm ions measured with Cluster Ion Counter (CIC) and Neutral cluster and Air Ion Spectrometer (NAIS) at SMEAR II station in Hyytiälä, Finland. Concentrations of 1-2 nm ions measured with the NAIS are also included. Sub-2 nm (2-2.3 nm) ion concentrations measured with CIC are refered as Channel 1 (Channel 2-Channel 3) in the .csv file.</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Raw data for Microwave-assisted Condensation Approach for Vanadium Silicate Microspheres and Their Catalytic Activity in Cyclohexene Epoxidation and Ethyl Lactate Oxidation
<p><strong>Specification of affiliations:</strong></p> <ul> <li>David Skoda - Centre of Polymer Systems</li> <li>Kamila Kuzelova - Centre of Polymer Systems</li> <li>Rajendran Blessy Pricilla - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Ales Styskalik - Department of Chemistry, Faculty of Science</li> <li>Tomas Pokorny - Department of Chemistry, Faculty of Science</li> <li>Iaroslav Doroshenko - Department of Chemistry, Faculty of Science</li> <li>Lucie Simonikova - Department of Chemistry, Faculty of Science</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript.</p>
Observation of fragmentation of a spinor Bose-Einstein condensate
<p>Data and theory curve for the figures of the publication "Observation of fragmentation of a spinor Bose-Einstein condensate".</p>
Data for the article "Evidence for spin current driven Bose-Einstein condensation of magnons"
<p>Data for the article "Evidence for spin current driven Bose-Einstein condensation of magnons"</p>
The effects of condensed tannins on behaviour and performance of a specialist aphid on Aspen
<p>Data and Rscripts used to generate the results in Díez Rodríguez, Kloth and Albrectsen: The effects of condensed tannins on behaviour and performance of a specialist aphid on Aspen.</p>
Mp4-Version of the supplementary Movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"
<p>Videos in 'mp4'-format of the 8 supplementary movies for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation"</p>
Data for: Quantifying Quantum Coherence in Polariton Condensates
<p>Data for the publication C. Lüders, M. Pukrop, E. Rozas, C. Schneider, S. Höfling, J. Sperling, S. Schumacher, and M. Aßmann, Quantifying Quantum Coherence in Polariton Condensates, PRX Quantum, 2(3), 030320 (2021).</p> <p>We theoretically and experimentally investigate quantum features of an interacting light-matter system from a multidisciplinary perspective, combining approaches from semiconductor physics, quantum optics, and quantum-information science. To this end, we quantify the amount of quantum coherence that results from the quantum superposition of Fock states, constituting a measure of the resourcefulness of the produced state for modern quantum protocols. This notion of quantum coherence from quantum-information theory is distinct from other quantifiers of nonclassicality that have previously been applied to condensed-matter systems. As an archetypal example of a hybrid light-matter interface, we study a polariton condensate and implement a numerical model to predict its properties. Our simulation is confirmed by our proof-of-concept experiment in which we measure and analyze the phase-space distributions of the emitted light. Specifically, we drive a polariton microcavity across the condensation threshold and observe the transition from an incoherent thermal state to a coherent state in the emission, thus confirming the buildup of quantum coherence in the condensate itself.</p> <p>You can use 7-zip for extracting the .7z file. (https://www.7-zip.org/)</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB-Geschäftszeichen TRR142/3-2022 – Projektnummer 231447078, Project A04. A grant for computing time at the Paderborn Center for Parallel Computing (PC<sup>2</sup>) is gratefully acknowledged.</p>
Data for: Tracking quantum coherence in polariton condensates with time-resolved tomography
<p>Data for publication C. Lüders, M. Pukrop, F. Barkhausen, E. Rozas, C. Schneider, S. Höfling, J. Sperling, S. Schumacher, and M. Aßmann, Tracking quantum coherence in polariton condensates with time-resolved tomography, submitted. arXiv preprint arXiv:2209.07129 (2022)</p> <p>Long-term quantum coherence constitutes one of the main challenges when engineering quantum devices. However, easily accessible means to quantify complex decoherence mechanisms are not readily available, nor are sufficiently stable systems. We harness novel phase-space methods - expressed through non-Gaussian convolutions of highly singular Glauber-Sudarshan quasiprobabilities - to dynamically monitor quantum coherence in polariton condensates with significantly enhanced coherence times. Via intensity- and time-resolved reconstructions of such phase-space functions from homodyne detection data, we probe the systems's resourcefulness for quantum information processing up to the nanosecond regime. Our experimental findings are confirmed through numerical simulations for which we develop an approach that renders established algorithms compatible with our methodology. In contrast to commonly applied phase-space functions, our distributions can be directly sampled from measured data, including uncertainties, and yield a simple operational measure of quantum coherence via the distribution's variance in phase. Therefore, we present a broadly applicable framework and a platform to explore time-dependent quantum phenomena and resources.</p> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – SFB-Geschäftszeichen TRR142/3-2022 – Projektnummer 231447078, Projects A04 and C10. A grant for computing time at the Paderborn Center for Parallel Computing (PC<sup>2</sup>) is gratefully acknowledged.</p> <p> </p> <p> </p> <p>You can use 7-zip for extracting the .7z file. (https://www.7-zip.org/)</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.