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2,194 results for “Diffusers”
DFT Dilute Solute Diffusion Database in Mg
<p>This is a database of first-principles results for dilute solute diffusion in HCP magnesium. The diffusivities were computed using the eight-frequency HCP diffusion model, where all the frequencies have been calculated using density functional theory. All eight frequencies for all solutes are reported as well as the diffusion coefficient (D0) and diffusion activation energy (Q).</p>
Involvement of the optic nerve in mutated CSF1R-induced hereditary diffuse leukoencephalopathy with axonal spheroids
<p><strong>Figure 1:</strong> Family pedigree. The arrow indicates the proband (present patient). Her mother developed a motor disorder at 40 years of age and died at 60 years of age. Her grandparents, father, brothers, sisters, and daughters were not affected.</p> <p><strong>Figure 2: Brain MRI, DWI, DTI, and MRS images. T2/Flair showed multifocal periventricular white matter lesions (A, B, and C), without enhancement (D). DWI shows high-signal intensities in periventricular white matters and corpus callosum (E, F). DTI shows decreased numbers of corpus callosum fibers, while subcortical arcuate fibers are spared (G). MRS shows increased Cho levels, while NAA levels are decreased in the white matter lesions (H, I).</strong></p> <p><strong>Figure 3: </strong>Optic nerves on MRI, showing that bilateral optic nerves are injured (red arrows). </p> <p><strong>Figure 4: </strong>OCT shows that the right peripapillary retinal nerve fiber layer (pRNFL) is atrophic in the temporal quadrant, and the left pRNFL is thinning in the temporal superior quadrants. Green represents pRNFL thickness, which is within normal limits; yellow represents pRNFL thickness, which is below borderline; red represents pRNFL thickness, which is below normal limits.</p> <p><strong>Figure 5:</strong> VEP shows reduced bilateral P100 amplitudes, although P100 latencies are normal in both eyes.</p> <p><strong>Figure 6:</strong> Visual fields in the right eye are partially missing in the upper right, lower right, and lower left quadrants, especially in the lower right quadrant. Visual fields in the left eye are partially missing in the four quadrants, especially in the upper left and lower right quadrants.</p> <p><strong>Figure 7:</strong> Gene analysis of <em>CSF1R</em>. The sequencing result from exon 18 of <em>CSF1R</em> (NM_005211.3) indicates a heterozygous c.2345 G>A (p.782Arg>His) substitution in the patient.</p>
Freely diffusing 100 nm polystyrene nanoparticles captured with nanoparticle tracking analysis
<p>100nm polystyrene NP (Kisker-Biotech, PPs-0.1) which were collected with a NanoSight<sup>TM</sup> LM10 system equipped with a LM14 green (535 nm) laser module and a cooled Andor camera (Andor-DL-658-OEM).</p> <p>FPS: 30</p> <p>Pixelsize: 166</p>
Computing Eddy-Driven Effective Diffusivity Using Lagrangian Particles Dataset
<p>This archive contains netCDF files corresponding to datasets used in the<br> Ocean Modelling paper entitled<br> "Computing Eddy-Driven Effective Diffusivity Using Lagrangian Particles" by<br> Phillip J. Wolfram and Todd D. Ringler.</p> <p>Archives are presented for particle reset times of 3, 5, 10, and 15 days,<br> corresponding to data sources used to produce figures in the manuscript. </p> <p>The data is in standard netCDF file format, which is readily readable using<br> standard netCDF tools or via the python libraries netCDF4 and xarray.</p> <p>Please see the README.md (with updates at https://gist.github.com/pwolfram/c9f3d8b6c37385732bd1b105b5267e6a).</p> <p> </p>
Raw data sets for: A simple calculation algorithm to seperate high-resolution CH4 flux measurements into ebullition- and diffusion derived components (AMT)
<p>Raw data sets for the research article "A simple calculation algorithm to seperate high-resolution CH4 flux measurements into ebullition- and diffusion derived components", published in "Atmospheric Measurment Techniques" (AMT). Data sets include raw data sets for the field and laboratory study, as well as calculated CH4 fluxes (field).</p>
Supporting data for Lin et al. "Diffuse Electron Precipitation Effects on Subauroral Polarization Streams"
<p>This dataset contains the necessary data supporting the paper titled "The role of diffuse electron precipitation in the formation of subauroral polarization streams", submitted by Lin et al., 2021. The dataset includes 1. the solar wind/IMF and SYMH indices used to drive the Multiscale Atmosphere-Geospace Environment (MAGE) model; 2. Electron precipitation energy flux (EnFlux), cross track ion drift, field-aligned current (FAC), and Pedersen conductance (SigmaP) sampled along three trajectories of DMSP F18 satellite during 17 March 2013 (08:28-08:38 UT, 11:18-11:28 UT, 13:00-13:10 UT), which are used to compare with the DMSP measurements; 3. Plasma pressure distribution in the magnetospheric equatorial plane, and EnFlux, FAC, westward ion drifts, at 06 UT, 07 UT, 09 UT, and 11 UT from the baseline run of MAGE simulation; 4. Two-dimensional distributions of EnFlux, FAC, zonal ion drifts and their latitudinal samplings at magnetic local time of 20 h from two MAGE simulation runs, one being the same from above mentioned, the other being a controlled run in which diffuse electron precipitation was turned off; 5. Zonal ion drifts, magnetospheric equatorial plane plasma density, and boundary locations of the EnFlux and downward FAC in both the ionosphere and magnetosphere; 6. Two-dimensional distributions of EnFlux, FAC, zonal ion drifts and their latitudinal samplings at magnetic local time of 20 h from another two MAGE simulation runs, one using the Fedder precipitation model, the other using the RCM+Zhang precipitation model with a loss rate of one-tenth.</p>
The role of dynamics in heterogeneous catalysis: surface diffusivity and N2 decomposition on Fe(111)
<p>Data related to the molecular dynamics simulations reported in the manuscript "The role of dynamics in heterogeneous catalysis: surface diffusivity and N2 decomposition on Fe(111)"</p><p>- Inputs of the MD simulations of surface morphology and dynamics (LAMMPS)</p><p>- Inputs of the OPES simulations for N2 adsorption and dissociation (LAMMPS+PLUMED)</p><p>- MD outputs: trajectory files</p><p>- Code for analysis and post-processed data</p><p>- Jupyter notebook to reproduce the pictures reported in the manuscript</p>
Supplementary data supporting "3D diffusion of water in melt inclusion-bearing olivine phenocrysts"
<p>Supplementary_DataS1</p><p>Secondary Ion Mass Spectrometry (SIMS) data of water concentrations in olivine crystals from the 1977 Seguam eruption used in this study. Electron probe microanalyser (EPMA) data of major elements and crystal orientations from electron backscatter diffraction (EBSD) are also included.</p><p>Supplementary_DataS2</p><p>Model inputs and outputs for the Monte Carlo diffusion modelling used in this study for water loss from a melt inclusion during magma decompression. Spreadsheet includes water loss estimated by 3D, 2D and 1D numerical models in addition to the anisotropic analytical solution. It also includes the inverted decompression rates from the 2D, 1D and analytical models.</p>
Anisotropic molecular diffusion in confinement II (Dataset)
<p>This archive contains the simulation settings for the bulk and S-L-V system configurations used in the associated paper "Anisotropic molecular diffusion in confinement II: A model for structurally complex particles applied to transport in thin ionic liquid films" (DOI: 10.5281/zenodo.7446070).</p><p>We here provide two different archives: </p><ul><li>One with the gromacs simulation configuration used for the simulation ("il_sim_settings_data.zip")</li><li>One with the raw extracted diffusion (parallel+perpendicular) and interface-normal density data ("graph_raw_data.zip"), which is the data underlying all plots in our associated publication.</li></ul><p>In "il_sim_settings_data.zip" there are folders for the bulk- and S-L-V- systems respectively, each containing folder for the systems with n=2,4,6,8.10 carbon atoms in the cation alkyl chain. These folders are labeled c<n> (i.e., for example: c2). Within these folders you will find the respective top and tpr files for the simulation.</p><p>In "graph_raw_data.zip" you can also find a folder for the bulk and the slv systems in the top level directory.</p><p>In "bulk", you can again find the folders c2,c4,c6,c8,c10 labeled as described above. Each folder contains a folder "diffusion_msd", which contains the gromacs (gmx msd) results for cations and anions in xvg format respectively. Also it contains a folder "diffusion_epm", which contains the epm model analysis results in these systems. Each file contains a header explaining the detailed data format. The file name depicts whether the data pertains to cations or anions within the respective system.</p><p>In "slv", you first find the c2,c4,c6,c8,c10 level of folders and within each folder two files describing the perpendicular diffusivity according to EPM analysis for cations and anions respectively in that respective SLV system ("D_perp_anion.txt"/"D_perp_cation.txt") the results of the parallel MSD-based analysis ("D_parallel_anion.txt"/"D_parallel_cation.txt") as well as the interface-normal number density profiles ("innd_anion.txt"/"innd_cation.txt")</p>
Reproducibility of "Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs"
<p>This file is the source data to ensure reproducibility of the paper "Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs". It contains the logs and results of all DFT calculations associated with transition states generated using the model proposed in the paper. It also includes code to reproduce the core findings of the paper, which can be done by running reproduce.sh. To accurately reproduce the results of the paper, use the v1.0.0 virtual environment from "https://github.com/seonghann/tsdiff".</p>
Dataset from : Unsteady Analysis of a Pulsating Alternate Flow Pattern in a Radial Vaned Diffuser.
Open the record for dataset details and reuse information.
Synthetic Out-Of-Vocabulary IAM dataset with Latent Diffusion Models
<p>SyntheticHTR: Handwritten Text Image Synthesis based on Latent Diffusion Models</p>
Model checkpoints for "SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models"
<p>Checkpoints for all SEEDS models in the paper <a href="https://arxiv.org/abs/2306.14066" rel="nofollow">https://arxiv.org/abs/2306.14066</a>, including all SEEDS-GEE and SEEDS-GPP models in the main manuscript, and the additional models trained in the Supplemental Material.</p> <p>Checkpoint naming convention:</p> <ul> <li><code>gee_c2_s7</code>: SEEDS-GEE trained conditioning on 2 seeds for 7-day lead time.</li> <li><code>gpp_c2_s7_g3_r4</code>: SEEDS-GPP trained conditioning on 2 seeds for 7-day leadtime, where the label mixture is 3 GEFS members and 4 ERA reanalyses.</li> </ul>
Stimuli from: AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures
<p>Stimuli generated using a Diffusion Model from the paper "AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures".</p> <p>Please cite as:<br><span>Ciupinska, K.; Marchesi, S.; Abbo, G. A.; Belpaeme, T. and Wykowska, A. (2024). <strong>AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures</strong>. In <em>Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: AWAI</em>; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 1436-1443. DOI: 10.5220/0012596400003636</span></p>
Diffusely charged polymeric zwitterions as loosely hydrated marine antifouling coatings
<p>This dataset accompanies the article "Diffusely Charged Polymeric Zwitterions as Loosely Hydrated Marine Antifouling Coatings" by Shawn D. Mengel, Wen Guo, Guangyao Wu, John A. Finlay, Peter Allen, Anthony S. Clare, Riddhiman Medhi, Zhan Chen, Christopher K. Ober, and Rachel A. Segalman in Langmuir in 2023 (LINK). The article demonstrates that more diffusely charged polymeric zwitterions have improved surface hydration, which correlates with improved fouling resistance to <em>Ulva linza</em>. This dataset contains the necessary experimental data to reproduce the main text and supporting figures. Nuclear magnetic resonance spectra, vibrational sum frequency generation spectra, marine fouling assays, and contact angle measurements are included.</p>
Nuclear induction lineshape: Non-Markovian diffusion with boundaries
<div> <div> <div> <div> <p>The dynamics of viscoelastic fluids are governed by a memory function, which is critical yet computationally intensive to determine, particularly when diffusion is restricted by boundaries. We introduce a computational method that effectively captures the memory effects by analyzing the time-correlation function of the pressure tensor, an indicator of viscosity, through the analytic continuation of the Stokes-Einstein equation to the Laplace domain. This equation is integrated with molecular dynamics (MD) simulations to obtain necessary parameters. Our method computes NMR lineshapes by employing a generalized diffusion coefficient that incorporates the influences of temperature and confinement geometry. This approach establishes a direct link between the memory function and thermal transport parameters, enabling precise computation of NMR signals for non-Markovian fluids in confined geometries.</p> </div> </div> </div> </div>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Dataset of "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy"
<p>Dataset for the article entitled "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy" </p> <p> </p> <p>Abstract:</p> <p> </p> <p>Thermal Therapies treat tumors by means of heat, greatly reducing pain, post-operation complications, and cost as compared to traditional methods. Yet, effective tools to avoid under- or over-treatment are mostly needed, to guide surgeons in laparoscopic interventions.<br>In this work, we investigated the temperature-dependent optical signatures of ex-vivo calf brain, lung, and heart tissues, based on the reduced scattering and absorption coefficients in the near-infrared spectral range (657 to 1107 nm). These spectra were measured by time domain diffuse optics, applying a step-like spatially homogeneous thermal treatment at 43 °C, 60 °C, and 80 °C.<br>We found three main increases in scattering spectra, possibly due to the denaturation of collagen, myosin, and proteins secondary structure.<br>After 75 °C, we found the rise of two new peaks at 770 and 830 nm in the absorption spectra due to the formation of a new chromophore, possibly related to hemoglobin or myoglobin.<br>This research marks a significant step forward in controlling thermal therapies with diffuse optical techniques by identifying several key markers of thermal damage. This could enhance the ability to monitor and adjust treatment in real-time, promising improved outcomes in tumor therapy.</p> <p> </p> <p> </p> <p>Authors:</p> <p>ALESSANDRO BOSSI , LEONARDO BIANCHI , PAOLA SACCOMANDI , AND ANTONIO PIFFERI<br>Politecnico di Milano</p> <p> </p> <p> </p> <p><a href="https://opg.optica.org/boe/fulltext.cfm?uri=boe-15-4-2481&id=548108" target="_blank" rel="noopener">Link to the article</a></p>
Data from: Does stomatal patterning in amphistomatous leaves minimize the CO2 diffusion path length within leaves?
<div> <div> <div> <p>Photosynthesis is co-limited by multiple factors depending on the plant and its environment. These include biochemical rate limitations, internal and external water potentials, temperature, irradiance, and carbon dioxide (CO<sub>2</sub>). Amphistomatous leaves have stomata on both abaxial and adaxial leaf surfaces. This feature is considered an adaptation to alleviate CO<sub>2</sub> diffusion limitations in productive environments as the diffusion path length from stomate to chloroplast is effectively halved in amphistomatous leaves. Plants may also reduce CO<sub>2</sub> limitations through other aspects of optimal stomatal anatomy: stomatal density, distribution, patterning, and size. A number of studies have demonstrated that stomata are overdispersed compared to a random distribution on a single leaf surface; however, despite their prevelance in nature and near ubiquity among crop species, much less is known about stomatal anatomy in amphistomatous leaves, especially the coordination between leaf surfaces. Here we use novel spatial statistics based on simulations and photosynthesis modeling to test hypotheses about how amphistomatous plants may optimize CO<sub>2</sub> diffusion in the model angiosperm <em>Arabidopsis thaliana</em> grown in different light environments. We find that 1) stomata are overdispersed, but not ideally dispersed, on both leaf surfaces across all light treatments; 2) the patterning of stomata on abaxial and adaxial leaf surfaces is independent; and 3) the theoretical improvements to photosynthesis from abaxial-adaxial stomatal coordination are miniscule (≪ 1%) across the range of feasible parameter space. However, we also find that 4) stomatal size is correlated with the mesophyll volume that it supplies with CO<sub>2</sub>, suggesting that plants may optimize CO<sub>2</sub> diffusion limitations through alternative pathways other than ideal, uniform stomatal spacing. We discuss the developmental, physical, and evolutionary constraits which may prohibit plants from reaching this theoretical adaptive peak of uniform stomatal spacing and inter-surface stomatal coordination. These findings contribute to our understanding of variation in the anatomy of amphistomatous leaves.</p> </div> </div> </div>
Experimentally probing the effect of confinement geometry on lipid diffusion
<p>Dataset accompanying the J. Phys. Chem. B paper: "<span>Experimentally probing the effect of confinement geometry on lipid diffusion". Contains raw and processed FCS data, processed FRAP data, and VCell simulations, and Jupyter notebooks to generate the figures in the associated manuscript.</span></p>
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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.