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5,371 results for “Flow”
Improved Vanadium Flow Battery Performance through a Pulsating Electrolyte Flow Regime
<p><span>The folder contains the processed data of figures 1, 2, 3, 4, S. 7, S. 8, S. 9 and S. 10 that appear in: </span></p> <p><span>R. De Wolf, A. Rossen, M. De Rop, L.F. Arenas, T. Breugelmans, J. Hereijgers, Improved Vanadium Flow Battery Performance through a Pulsating Electrolyte Flow Regime, Batter. Supercaps. 01 (2023) 1–23.</span></p> <p><span><a href="https://doi.org/10.1002/batt.202300382">https://doi.org/10.1002/batt.202300382</a></span></p>
Rigid fiber transport in a periodic non-homogeneous geophysical turbulent flow
<p>From anthropogenic litter carried by ocean currents to plant stems traveling through the atmosphere, geophysical flows are often seeded with elongated, fiber-like particles. In this study, we used a large-scale laboratory model of a tidal current -- representative of a widespread class of geophysical flows -- to investigate the tumbling motion of long, slender, and floating fibers in the complex turbulence generated by flow interactions with a tidal inlet. Despite the non-stationary, non-homogeneous, and anisotropic nature of this turbulence, we find that long fibers statistically rotate at the same frequency as eddies of similar size, a phenomenon called \textit{scale selection}, which is known to occur in ideal turbulence. Furthermore, we report that the signal of the instantaneous transverse velocity difference between the fiber ends differs significantly from the signal produced by the flow in the fiber surroundings, although the two are statistically equivalent. These observations have twofold implications. On the one hand, they confirm the reliability of using the end-to-end velocity signal of rigid fibers to probe the two-point transverse statistics of the flow, even under realistic conditions: oceanographers could exploit this observation to measure transverse velocity differences through elongated floats in the field, where superdiffusion complicates collecting sufficient data to probe two-point turbulence statistics at a fixed separation effectively. On the other hand, by addressing the dynamics of inertial range particles floating in the coastal zone, these observations are crucial to improving our ability to predict the fate of meso- and macro-litter, a size class currently understudied.</p>
THEORETICAL STUDY OF THE MOVEMENT OF THE COTTON FLOW IN THE INCLINED PLANE UNDER THE INFLUENCE OF BLADES.
Open the record for dataset details and reuse information.
MSci Report: Automated catalytic reductive aminations in Flow
<p>Information supporting the MSci report: Automated catalytic reductive aminations in Flow.</p>
Supplementary data: "Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI"
<p>This repository contains processed data and result files for the paper <a href="https://arxiv.org/abs/2204.10727">Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI</a>. The code for producing the processed data and the results is <a href="https://github.com/sebastianptz/XAI-for-HVDC">available at github</a>.</p> <p><strong>Data</strong></p> <p>The data folder contains the feature and target data used to train the ML model:</p> <ul> <li>stability_input: A Folder containing training and test sets for the stability model for each area.</li> <li>flow_input: A Folder containing training and test sets for the flow model for each link.</li> <li><em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features.</li> <li><em>input_forecast.h5 </em>and<em> input_actual.h5: </em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features.</li> <li><em>indicators.h5 </em>: The grid frequency stability indicators.</li> <li><em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation.</li> <li>HVDClinks: A Folder containing preprocessed time series for scheduled and unscheduled HVDC flows</li> </ul> <p><strong>Data sources</strong></p> <p>Most of the data is derived from ENTSO-E Transparency Platform data [1]. The grid stability indicators (indicators<em>.h5</em>) are based on publicly available data from the German Transmission System Operators (TSOs) [2].</p> <p><strong>Results</strong></p> <p>The stability_results and the flow_results folder contain the results of hyperparameter optimization, model prediction and interpretation via SHAP for the respective models.</p> <ul> <li><em>cv_results_gtb_full.csv</em> : Performance results for each combination in the hyperparameter optimization.</li> <li><em>cv_best_params_gtb_full.csv</em> : Hyperparameters used in the final (optimized) model.</li> <li><em>shap_values_gtb_full.npy</em> : First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set.</li> <li><em>y_pred.h5/y_pred_links.h5</em> : Predictions of daily profile predictor and Machine Learning models.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The data might be subject to copyright or related rights. Please consult the primary data owner.</p> <p> </p> <p> </p> <div> </div>
The non-local reactive flow simulation dataset
<h1><strong>The non-local reactive flow simulation dataset</strong></h1> <ol> <li>Animation <ul> <li>propagation rate_0.1u: the animations of wormholing with fracture propagation rate = 0.1*u0</li> <li>propagation rate_1u: the animations of wormholing with fracture propagation rate = 1*u0</li> <li>propagation rate_5u: the animations of wormholing with fracture propagation rate = 5*u0</li> </ul> </li> <li>Dataset: the drawing data of Figs. 3-6 in main text</li> </ol>
Beyond Average Characterisation: Exploring Lipid Nanoparticle Properties Using Online Asymmetric Flow Field-Flow Fractionation with Small Angle X-Ray Scattering
<p>Astra files from AF4-fractogram for LNP samples</p> <p>LC-CAD raw data (Lipid standards and collected fractions)</p> <p>Cryo-TEM images (SampleA = LNP-A, SampleE = LNP-E)</p> <p> </p>
Table S1: Human-specific oligonucleotide primers; Table S2: Antibodies and their final dilutions used for immunofluorescence analysis and flow cytometry.
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A Nonlinear Flow Model and Criterion for 3D Natural Fracture Intersections: Effects of Surface Roughness and Mechanical Aperture
<p>The present repository includes the raw data of flow rate ~ presure presented in the manuscript submitted for publication in Water Resources Research entitled <em>A Nonlinear Flow Model and Criterion for 3D Natural Fracture Intersections: Effects of Surface Roughness and Mechanical Aperture.</em></p> <p>The repository is presented as TXT files,and all other results including the linear and nonlinear coefficients in Forchheimer equation and permeability of fracture intersections can be calculated employing this basic data.</p> <p> </p>
Fig. 5 a 95 in Using haplotype networks, estimation of gene flow and phenotypic characters to understand species delimitation in fungi of a predominantly Antarctic Usnea group (Ascomycota, Parmeliaceae)
Fig. 5 a 95% probability haplotype network for the U. sphacelata group based on a combined dataset of ribosomal IGS and ITS and protein-coding RPB1 sequences. The 3-step nesting level is shown. Haplotypes are represented by colored circles according to the sample localities. The circle size is proportional to the number of sequences sharing a haplotype, except for haplotype H5 comprising too many
Strategies for Flow Cytometric Profiling of BCR Immunoglobulin Heavy Chain Isotypes: A Comparative Assessment of Fc Receptor Blocking Agents
<p>This study aims to assess the impact of FcR blocking reagents on detection of BCR IgH isotypes IgM, IgD, IgA1-2, and IgG1-4. </p> <p>Blood samples were collected from healthy volunteers in lithium heparin tubes, followed by the isolation of PBMCs utilizing BioColl density gradient centrifugation. The blood samples were diluted at a ratio of 1:1 in PBS with a pH of 7.4 and then centrifuged at 800 g for 20 minutes at RT. The PBMC interphases were harvested and washed three times with PBS containing 2 mM EDTA. Subsequently, PBMCs were resuspended in cRPMI medium. PBMCs were treated with five different FcR blocking reagents prior to staining. For each donor, PBMCs were divided into two groups, with each group containing five samples. One group was stained directly with an immunophenotyping panel without being washed following the application of FCR blocking reagent. The other group was applied washing before staining.<br>Ultimately, the samples were incubated with an 8-color immunophenotyping panel targeting B-cell markers:</p> <table> <tbody> <tr> <td> <p><strong><span>Target</span></strong></p> </td> <td> <p><strong><span>Fluorochrome</span></strong></p> </td> <td> <p><strong><span>Producer</span></strong></p> </td> <td> <p><strong><span>Clone</span></strong></p> </td> <td> <p><strong><span>Isotype</span></strong></p> </td> <td> <p><strong><span>Dilution </span></strong></p> </td> </tr> <tr> <td> <p><span>CD19</span></p> </td> <td> <p><span>APC-eFluor780</span></p> </td> <td> <p><span>eBioscience</span></p> </td> <td> <p><span>HIB19</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> <tr> <td> <p><span>IgM</span></p> </td> <td> <p><span>PerCP/Cy5.5</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>MHM-88</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> <tr> <td> <p><span>IgD</span></p> </td> <td> <p><span>AF700</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>IA6-2</span></p> </td> <td> <p><span>Mouse IgG2a, kappa</span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> <tr> <td> <p><span>IgA</span></p> </td> <td> <p><span>VioBlue</span></p> </td> <td> <p><span>Miltenyi</span></p> </td> <td> <p><span>IS11-8E10</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:200</span></p> </td> </tr> <tr> <td> <p><span>IgA2</span></p> </td> <td> <p><span>PE</span></p> </td> <td> <p><span>Miltenyi</span></p> </td> <td> <p><span>IS11-21E11</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> <tr> <td> <p><span>IgG1</span></p> </td> <td> <p><span>AF488</span></p> </td> <td> <p><span>AF488/Lumiprobe</span></p> </td> <td> <p><span>MH161-1</span></p> </td> <td> <p><span>Mouse, IgG2b, kappa </span></p> </td> <td> <p><span>1:600</span></p> </td> </tr> <tr> <td> <p><span>IgG1</span></p> </td> <td> <p><span>Dylight550</span></p> </td> <td> <p><span>Dylight/Innova Bio</span></p> </td> <td> <p><span>MH161-1</span></p> </td> <td> <p><span>Mouse, IgG2b, kappa</span></p> </td> <td> <p><span>1:500</span></p> </td> </tr> <tr> <td> <p><span>IgG2</span></p> </td> <td> <p><span>Dylight550</span></p> </td> <td> <p><span>Dylight/Innova Bio</span></p> </td> <td> <p><span>HP6002</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> <tr> <td> <p><span>IgG3</span></p> </td> <td> <p><span>AF488</span></p> </td> <td> <p><span>AF488/Lumiprobe</span></p> </td> <td> <p><span>MH163-1 (HP6095)</span></p> </td> <td> <p><span>Mouse, IgG2b, kappa</span></p> </td> <td> <p><span>1:400</span></p> </td> </tr> <tr> <td> <p><span>IgG4</span></p> </td> <td> <p><span>APC</span></p> </td> <td> <p><span>Cytognos</span></p> </td> <td> <p><span>SAG4</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:400</span></p> </td> </tr> <tr> <td> <p><span>CD3</span></p> </td> <td> <p><span>BV605</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>OKT3</span></p> </td> <td> <p><span>Mouse IgG2a, kappa</span></p> </td> <td> <p><span>1:200</span></p> </td> </tr> <tr> <td> <p><span>CD14</span></p> </td> <td> <p><span>BV605</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>63D3</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:200</span></p> </td> </tr> <tr> <td> <p><span>CD16</span></p> </td> <td> <p><span>BV605</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>3G8</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:200</span></p> </td> </tr> <tr> <td> <p><span>Viability dye</span></p> </td> <td> <p><span>Zombie yellow</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span> </span></p> </td> <td> <p><span> </span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> </tbody> </table> <p>Cells were analyzed on a FACS Aria IIIu (Becton, Dickinson and Company, Franklin Lakes, NJ). B cells were gated as live ZombieYellow-CD3-CD14-CD16-CD19+ lymphocyte. IgM and IgD expression was used to differentiate class-switched (IgM-IgD-) and non-switched (IgM+IgD+) B cells. Next, the IgA+ population, gated from switched B cells, was segregated into IgA1+ (gated as IgA+IgA2-) and IgA2+ (IgA+IgA2+) B cells. The IgA- population was segregated into IgG1+, IgG2+, IgG3+ and IgG4+ B cells. Gates were set based on fluorescence minus one (FMO) controls.<br>To evaluate the effectiveness of the different blocking reagents used in our study on preventing non-specific binding of mouse monoclonal antibodies (mAbs), PBMCs were incubated with isotype control antibodies. Mouse IgG1, IgG2a, and IgG2b antibodies, all conjugated with PE, were evaluated for their non-specific binding to CD14+ monocytes. Prior to staining, the cells were divided into two groups and were treated with FcR blocking reagent as mentioned above. The details concerning the reagents are below:</p> <table> <tbody> <tr> <td> <p><strong><span>Target</span></strong></p> </td> <td> <p><strong><span>Fluorochrome</span></strong></p> </td> <td> <p><strong><span>Brand</span></strong></p> </td> <td> <p><strong><span>Clone</span></strong></p> </td> <td> <p><strong><span>Isotype</span></strong></p> </td> <td> <p><strong><span>Dilution</span></strong></p> </td> </tr> <tr> <td> <p><span>IgG1</span></p> </td> <td> <p><span>PE</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>MOPC-21</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> <tr> <td> <p><span>IgG2a</span></p> </td> <td> <p><span>PE</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>MOPC-173</span></p> </td> <td> <p><span>Mouse IgG2a, kappa</span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> <tr> <td> <p><span>IgGb</span></p> </td> <td> <p><span>PE</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>MPC-11</span></p> </td> <td> <p><span>Mouse, IgG2b, kappa</span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> <tr> <td> <p><span>CD14</span></p> </td> <td> <p><span>BV605</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span>63D3</span></p> </td> <td> <p><span>Mouse, IgG1, kappa</span></p> </td> <td> <p><span>1:200</span></p> </td> </tr> <tr> <td> <p><span>Viability dye</span></p> </td> <td> <p><span>Zombie green</span></p> </td> <td> <p><span>Biolegend</span></p> </td> <td> <p><span> </span></p> </td> <td> <p><span> </span></p> </td> <td> <p><span>1:100</span></p> </td> </tr> </tbody> </table> <p>The analysis of all flow cytometry data was performed using FlowJo v10 software (BD Biosciences). Statistical analysis was conducted using GraphPad Prism 9 software (GraphPad, La Jolla, USA). </p>
Effects of different grass distribution patterns and coverage combinations on overland flow resistance mechanisms
<p>To clarify the relationship between grass cover and water flow resistance, laboratory-simulated rainfall experiments were conducted. Five rainfall intensities and three degrees of slope were applied to six degrees of grass cover and six distribution patterns to elucidate the response of overland resistance to the experimental design factors and hydrodynamic parameters and to establish a predictive model. The results indicated that as grass coverage increases, the resistance coefficient also increases; however, with increases in the rainfall intensity and slope, the resistance coefficient decreases, with no apparent critical slope or rainfall intensity. Overland resistance was higher for the horizontal vegetation pattern than the vertical pattern. The flow Reynolds and Froude numbers were negatively correlated with the resistance coefficient. The water flow path index was useful for characterizing overland resistance, reflecting the influence of different grass cover types, and was positively correlated with the resistance coefficient. Based on a dimensional analysis and the π theorem, a model of overland flow resistance was developed. This model effectively predicted overland resistance and quantified the contribution of each factor to the resistance coefficient. The contributions of the slope, rainfall Reynolds number, flow Reynolds number, Froude number, and water flow path index to overland resistance were 12.78%, 6.02%, 34 9.77%, 15.04%, and 56.39%, respectively, with grass cover playing a key role. Simulated rainfall experiments with different degrees of grass coverage and distribution patterns revealed the dynamic mechanisms of overland hydraulic erosion.</p>
Separating the relative roles of climate and forest disturbance in high and low flows in snow-dominated watersheds of Southern British Columbia
<p>1. Historical hydrological data in watershed 241, Camp, Greata, and Trepanier watersheds, including daily hydrometeorological records and aggregated hydrometeorological metrics. </p> <p>2. Relative contributions of climate metrics and forest disturbance to multiple hydrological signatures (i.e., high and low flows) in watershed 241, Camp, Greata, and Trepanier watersheds.</p>
CyTOF and flow cytometry fcs files from BMMCs of patients with myeloid neoplasms (AML, MDS, CMML)
<p>This dataset contains all the flow cytometry and mass cytometry (CyTOF - CD45+ immune cells) fcs files , associated with Tasis <em>et al</em>. Preprint doi: https://doi.org/10.1101/2023.12.30.23300608</p> <p> </p>
Figure 1. A in Asymmetric acoustic signal recognition led to asymmetric gene flow between two parapatric frogs
Figure 1. A, sample localities across the east and the west contact zones. B, molecular phylogeny, and (C) population structure of Buergeria otai and B. choui using RAD-seq data. The phylogeny was constructed by RAXML using 877 SNPs. STRUCTURE plot of two species and populations of B. otai were constructed with 824 and 4954 RAD loci, respectively. Bootstrap values correspond to phylogenetic analyses with SNPs and concatenated RAD-loci sequences.
Figure 4 in The Zagros Mountains acting as a natural barrier to gene flow in the Middle East: more evidence from the evolutionary history of spiny-tailed lizards (Uromasticinae: Saara)
Figure 4. Chronogram of diversification events based on two mtDNA (Cytb and 16S) and one nDNA (ACM4) genes for Uromasticinae, including the genera Saara and Uromastyx. Blue bars show 95% highest posterior density (HPD) intervals of the estimated node ages; values indicated on branches are mean node ages (Mya).
Generation of Current-wave-supported Sediment Gravity Flow Promoted by Wave-induced Liquefaction on an Abandoned Estuary Delta
<p>This dataset includes sediment concentrations,velocities, and residual pore pressure data measured at two sites in the abandoned Yellow River Delta of the North Yellow Sea, China.</p>
Data for "Flow adaptation to velocity changes in transitional clay suspension flows"
<p>This dataset details experimental results of unsteady clay-laden suspension open-channel flows, focussing on the flow adaptation to changes in velocity within these transitional clay flows. </p> <p><br> </p>
Resistance Breakdown of a Membraneless Hydrogen–Bromine Redox Flow Battery
<p>Raw data for the publication plot figures (Fig. 3, 4, 5, 6)</p>
Figure 2 in Rare genetic admixture and unidirectional gene flow between Vipera aspis and Vipera berus at their contact zone in western France
Figure 2. NewHybrids results using Jeffreys priors of V. aspis and V. berus samples collected in the Loire Atlantique department (France). Morphologically intermediate individuals include individual 4.14 which was genetically assigned to V. aspis. The hybridization level of individuals 14 and 71 could not be clearly assigned. BcVa = F1 × Vipera aspis; BcVb = F1 × Vipera berus.
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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.