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2,194 results for “Diffusers”
Utility of standard diffusion-weighted magnetic resonance imaging for the identification of ischemic optic neuropathy in giant cell arteritis
<p><span>Purpose: To assess diffusion abnormalities of the optic nerve (ON) in giant cell arteritis (GCA) patients with acute onset of visual impairment (VI) using diffusion-weighted magnetic resonance imaging (DWI).</span></p> <p><span>Methods: DWI scans of GCA patients with acute VI were evaluated in a case-control study. Two blinded neuroradiologists assessed randomized DWI scans of GCA and controls for ON restricted diffusion. Statistical quality criteria and inter-rater reliability (IRR) were calculated. DWI findings were compared to ophthalmological assessments.</span></p> <p><span>Results: 35 GCA patients (76.2 ± 6.4 years; 37 scans) and 35 controls (75.7±7.6 years; 38 scans) were included. ON restricted diffusion was detected in 81.1% (Reader 1) of GCA scans. Localization of ON restricted diffusion was at the optic nerve head in 80.6%, intraorbital in 11.1% and affecting both segments in 8.3%. DWI discerned affected from unaffected ON with a sensitivity, specificity, positive and negative predictive value of 87%/99%/96%/96%. IRR for ON restricted diffusion was κ<sub>inter </sub>= 0.72 (95% CI 0.59‑0.86). DWI findings challenged ophthalmologic diagnoses in 4 cases (11.4%).</span></p> <p><span>Conclusions: DWI visualizes anterior and posterior ON ischemia in GCA patients with high sensitivity and specificity, as well as substantial inter-rater reliability. DWI may complement the ophthalmological assessment in patients with acute VI.</span></p>
Text-fig. 6. Quercus sp. Red Oak, UF 279-24550. a, b: Wood semi-ring-porous to diffuse-porous; vessels exclusively solitary; thinwalled tyloses in wide vessels; diffuse, diffuse-in-aggregates axial parenchyma; rays of two distinct sizes. TS. c: Rays of two distinct sizes, TLS. d: Vessel-vasicentric tracheid pitting, RLS. e: Vessel-ray parenchyma pitting with reduced borders, vertically elongate, RLS. f: Narrow rays 1-2 cells wide, TLS. g: Ray composed of procumbent cells, RLS. Scale bars: 200 µm in a, b, c; 50 µm in d, f, g; 20 µm in e. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 6. Quercus sp. Red Oak, UF 279-24550. a, b: Wood semi-ring-porous to diffuse-porous; vessels exclusively solitary; thinwalled tyloses in wide vessels; diffuse, diffuse-in-aggregates axial parenchyma; rays of two distinct sizes. TS. c: Rays of two distinct sizes, TLS. d: Vessel-vasicentric tracheid pitting, RLS. e: Vessel-ray parenchyma pitting with reduced borders, vertically elongate, RLS. f: Narrow rays 1-2 cells wide, TLS. g: Ray composed of procumbent cells, RLS. Scale bars: 200 µm in a, b, c; 50 µm in d, f, g; 20 µm in e.
Pyramid finite elements for discontinuous and continuous discretizations of the neutron diffusion equation with applications to reactor physics
<p>Data statement for named paper</p>
DIFFUSION OF NEWS IN THE TOURISM INDUSTRY
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Training Datasets for Digital Twin of Diffusion Experiments
<p>This repository contains the training datasets generated from lattice Boltzmann simulations.</p>
Data for the paper: Deciphering the guanidinium cation: Insights into thermal diffusion
<p>The Excel file contains all data presented in the paper with the doi.org/10.1063/5.0215843</p>
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
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His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
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His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
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His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>part aa of HMU1st</p>
2018-2020 Dataset [7/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 7/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Sample dataset for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains a sample of the input data for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This sample dataset also includes files relative to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a> and unzip this zip file in the data folder.</p>
Models of low-mass helium white dwarfs including gravitational settling, thermal and chemical diffusion, and rotational mixing⋆
<p>MESA inlists, data (from a single run: rotation + diffusion, M1=1.4, M2=1.2, Porb=3.4 days, Z=0.02) and run_star_extras associated with <a href="http://adsabs.harvard.edu/abs/2016A%26A...595A..35I">Istrate et. al 2016</a>. MESA version 7624. The grid of models produced in this paper can be found <a href="http://adsabs.harvard.edu/abs/2016yCat..35950035I">here</a>.</p>
Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration - Synthetic data
<p>Analysis of dMRI of the optic chiasm using CSD revealed unexpectedly high AFD of the intact bundle in the experimental animals. To evaluate whether this finding is an artifact produced by CSD, we used analytic synthetic data. Using the multi-tensor model, two sets of synthetic dMRI data were created consisting of 100 voxels, each with two tensors crossing at right angles. For each voxel, one tensor was modelled as an “intact” fiber bundle, following the DTI metrics observed in our experimental data (λ∥=0.64 and λ⟂=0.12 ×10-3 mm²/s), while the other tensor was modelled as an “injured” fiber bundle (λ∥=0.42 and λ⟂=0.22 ×10-3 mm²/s). Volume fractions were set as f=0.5 for both tensors. The multi-shell scheme to generate the synthetic signals consisted of 20 b=0 s/mm², and 160 DWI volumes, consisting of 80 diffusion gradient orientations, each with two distinct b values, namely b=1000 and 3000 s/mm². Signal-to-noise ratio was set at 20. </p> <p>Diffusion-weighted parameters are shown in the corresponding *protocol.txt files, organized as in the MRtrix gradient scheme (https://mrtrix.readthedocs.io/en/latest/concepts/dw_scheme.html), namely one row per volume, each containing the vector [x y z b], where x, y, and z, correspond to the components of the diffusion gradient direction, and b is the b value in s/mm^2.</p> <p>Refers to:</p> <p>Rojas-Vite, Gilberto, Coronado-Leija, Ricardo, Narvaez-Delgado, Omar, Ramirez-Manzanares, Alonso, Marroquin, Jose-Luis, Noguez-Imm, Ramses, Aranda, Marcos L, Scherrer, Benoit, Larriva-Sahd, Jorge, Concha, Luis: Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration , Available at bioRxiv, doi.org/10.1101/571539, 2019.</p>
Pretrained-Guided Conditional Diffusion Models for Microbiome Data Denoising
<p>All datasets and corresponding metadata used in mbVDiT.</p>
Fig. 3 in Microstructural Impact of Ischemia and Bone Marrow-Derived Cell Therapy Revealed With Diffusion Tensor Magnetic Resonance Imaging Tractography of the Heart In Vivo
Fig. 3. Amblyomma variegatum female before engorgement
Dynamic evaluation of corneal cross-linking and osmotic diffusion effects using optical coherence elastography
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Data_Effects_and_correctability_of_pile_up_distortion_using_established_figures_of_merit_in_time_domain_diffuse_optics_at_extreme_photon_rates
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WRF met-data for generative diffusion model post-processing precipitation map
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Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generation - Data and Samples
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.