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2,194 results for “Diffusers”
Surrogate Modeling Benchmark - One-dimensional diffusion model
<p>This dataset is related to the One-dimensional diffusion model benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-one-dimensional-diffusion-model" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-one-dimensional-diffusion-model</a>.</p> <p>The experimental designs include datasets with 200, 400, 600, 800, and 1000 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.</p> <h2>Description of the dataset file</h2> <p>The dataset file includes two variables:</p> <ul> <li><em>ExpDesigns</em>, and</li> <li><em>ValidationSet</em>.</li> </ul> <p>Both variables are Matlab structures with fields <em>X</em>, <em>Y</em>, and <em>nSamples</em>. Variable <em>ExpDesigns</em> is a non-scalar structure sized according to the number of experimental design groups. Each field of <em>X</em> for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the <em>ValidationSet</em> variable, except it contains only one dataset per benchmark case.</p> <p>The structure can be summarized as follows:</p> <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> <p>The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.</p> <p>For <strong>Polynomial Chaos Expansions (PCE)</strong>, we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).</p> <p><strong>Polynomial Chaos Kriging (PCK)</strong> employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.</p> <p>We benchmarked both linear and ordinary <strong>Kriging</strong>, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.</p> <p>For further details on the settings, please refer to the competitors.m file and UQLab user manuals:</p> <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> <p>The results file contains one variable: <em>Metrics</em>. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:</p> <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l)<br> <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> <p>The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>.</p> <h2>Additional files</h2> <p>We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called <em>working_with_datafiles.*</em> (the extension depends on the selected language).</p> <h2>Acknowledgment</h2> <p>This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.</p>
Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms Dataset
<p>This is the official data repository for the paper: "Efficient Semantic Diffusion Architectures for Model Training on Synthetic Echocardiograms", available at:<a href="https://www.arxiv.org/abs/2409.19371"> https://www.arxiv.org/abs/2409.19371</a>. The corresponding code is available at: <a href="https://github.com/david-stojanovski/EDMLX">https://github.com/david-stojanovski/echo_from_noise</a></p> <p> </p> <p>The synthetic data is produced using a variety of generative architectures, including the <strong>Elucidating Diffusion Model (EDM), Variance Exploding (VE), Variance Preserving (VP)</strong>, and our novel models, <strong>EDM-L64</strong> and <strong>EDM-L128</strong>, which employ <strong>latent diffusion</strong> strategies to significantly reduce computational cost. By incorporating <strong>spatially adaptive normalization (SPADE) blocks</strong> and <strong>Γ-distribution-based Variational Autoencoders (Γ-VAE)</strong>, these datasets ensure that the generated images preserve the essential semantic features required for training deep learning models.</p> <p> </p> <p>All pretrained classification and segmentation models can be found within the <strong>trained_models </strong>file.</p> <p>All generated images can be found within the <strong>generated_data </strong>file. Included is the <strong>CAMUS</strong> and original <strong>Semantic Diffusion Model (SDM) </strong>data, as well as a folder labelled <strong>easy_inference</strong> designed to contain all relevant labelmaps in a convenient folder for generating replicas of the dataset (detailed at codebase).</p>
Diffusion Coefficient Analysis by Dynamic Light Scattering Enables Determination of Critical Micelle Concentration
<p>This upload contains dynamic light scattering data files obtained from the work described in the manuscript that is published by Lena Nielinger and co-workers in ChemPlusChem (<a href="https://doi.org/10.1002/cplu.202400645">https://doi.org/10.1002/cplu.202400645</a>) (WILEY). The files in this repository contain dynamic light scattering data obtained from the analysis of different detergents series and can be downloaded and analysed with a Zetasizer software according to the instructions procied in the manuscript. For information on how to obtain the the Zetasizer software, we refer to the customer support and/or website of the company Malvern Panalytical.</p>
Dataset of "Transient current responses of organic electrochemical transistors by vertical ionic diffusion and electrolyte resistance"
<p>This dataset supports the article </p> <p>"<span>Transient current responses of organic electrochemical transistors by vertical ionic diffusion and electrolyte resistance</span>"</p> <p> </p> <p>Raw data for the article "<span>Transient current responses of organic electrochemical transistors by vertical ionic diffusion and electrolyte resistance</span>". For further details see the readme.txt file.</p>
Science ready spectra, their best-fitting models and results of Jeans axisymmetric modelling described in the research paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al.
<p>This package contains data presented in the paper "Transforming gas-rich low-mass discy galaxies into ultra-diffuse galaxies by ram pressure" by Grishin, Chilingarian, Afanasiev et al. (2021 Nature Astronomy in press). The dataset can be used to reproduce Figures 3 and 4 from the main manuscript and Extended Data Figures 1, 2, 4, 5 from the Supplementary Information.</p> <p>(1) Python scripts and data points required to reproduce Figure 4 in the manuscript and Extended Data Figure 5 from the Supplementary Information. The data and scripts are presented in a combined .zip archive for both figures.</p> <p>(2) One-dimensional spectra extracted within 1 half-light radius from long-slit Binospec spectra and multi-wavelength far-UV-to-near-IR broadband spectral energy distributions (SEDs) assembled from the photometric measurements extracted within the same aperture for 11 galaxies from the main sample (9 in the Coma cluster and 2 in the Abell 2147 cluster) and 5 galaxies from the supplementary (auxiliary) list. The spectra and SEDs are accompanied with their best-fitting stellar population models and parameters determined by the NBursts+phot algorithm: radial velocity, velocity dispersion, truncation age, final stellar metallicity. The templates are MILES-based models with self-consistent chemical evolution presented in Grishin et al. 2019 (https://ui.adsabs.harvard.edu/abs/2019arXiv190913460G/abstract). The filenames contain the coefficient for galactic winds and the mass fraction of stars in the final starburst, e.g. _l15_60 means lambda=1.5, SSP_frac=60 per cent. The files are presented as binary FITS tables with the fields annotated using unified content descriptors (UCDs) from the list established by the International Virtual Observatory Alliance and physical units where applicable.</p> <p>(3) Two-dimensional profiles of internal kinematics (radial velocity and velocity dispersion) and stellar population properties (truncation age and final stellar metallicity) derived from the analysis of long-slit Binospec spectra for 12 galaxies after adaptive binning, 11 from the main sample and GMP3016 in the Coma cluster from the supplementary sample; best-fitting Jeans axisymmetric models without adaptive binning, i.e. full profiles along the slit. The data are presented in binary FITS tables in the two FITS extensions, one for the profiles derived from the corresponding spectra and the second one for dynamical models.</p>
Dataset: Quantitative Evaluation of Enhanced multi-plane clinical fetal diffusion MRI with a crossing-fiber phantom
<p>This dataset provides MRI acquisitions of a customized crossing phantom for fetal brain. It contains:<br> <br> 1) High Resolution acquisitions of 1.5 mm<sup>3</sup> isotropic and 61 directions (with b-vectors/b-values)</p> <p>2) Six low resolution acquisitions of 1x1x4 mm<sup>3</sup> and 9-16-25 directions (with respective b-vectors/b-values)</p> <p>3) A structural T2-w acquisition</p>
A groupwise registration and tractography framework for cardiac myofiber architecture description by diffusion MRI : an application to the ventriclar junctions
<p>Data and materials regarding the submission of the paper. See Data Avaibility section and https://github.com/valeryozenne/Cardiac-Structure-Database</p> <p> </p>
Text-fig. 2. A – First small excavation trench in the newly discovered fossiliferous outcrop area of the Ploužnice lake horizon, locality "Small Ravine" south of the village Ploužnice on the slope of road No. 286. Typical are the red, violet and varicoloured tuffaceous siltstones. Semily Formation, Late Stephanian, Krkonoše Piedmont Basin. B – Basal part of fossiliferous 12 cm to14 cm thick reddish to purple-red tuffaceous siltstone bed from the trench in Text-fig. 2A. Internal bedding is expressed as horizontal but with diffuse changes in grain size from fine silt to coarse, fine sandy silt. Intercalated in places are 3 to 5 mm thick layers (indicated by arrows) with patchy concentrations of isolated fish remains, especially in the first 3 centimetres of this siltstone layer. The large white spot at 2.5 cm is a coprolite fragment. in Fossil Fauna And Flora Of A Re-Discovered Locality In The Late Carboniferous Ploužnice Horizon Of The Krkonoše Piedmont Basin, Bohemian Massif
Text-fig. 2. A – First small excavation trench in the newly discovered fossiliferous outcrop area of the Ploužnice lake horizon, locality "Small Ravine" south of the village Ploužnice on the slope of road No. 286. Typical are the red, violet and varicoloured tuffaceous siltstones. Semily Formation, Late Stephanian, Krkonoše Piedmont Basin. B – Basal part of fossiliferous 12 cm to14 cm thick reddish to purple-red tuffaceous siltstone bed from the trench in Text-fig. 2A. Internal bedding is expressed as horizontal but with diffuse changes in grain size from fine silt to coarse, fine sandy silt. Intercalated in places are 3 to 5 mm thick layers (indicated by arrows) with patchy concentrations of isolated fish remains, especially in the first 3 centimetres of this siltstone layer. The large white spot at 2.5 cm is a coprolite fragment.
Origin and Structure of Electromagnetic Generator Regions at the Edge of the Electron Diffusion Region
<p>The data included here is for an article submission to the Physics of Plasmas: MMS special Issue. There are electric field, magnetic field, and particle data files from MMS (cdf files). There are files from Particle-in-Cell simulations (xdmf files, h5 files, and a log file with run info). Included here is the python notebook (ipynb file) used to analyze the simulation data, and two IDL files (generator_fig.pro and Energy_Flux.pro) that were used to produce the panel plots of MMS data in the article.</p>
Revisiting the diffuse layer polarization of a spherical grain in electrolytes with numerical solutions of Nernst-Planck-Poisson equations
<p>These datasets contain the Comsol files that were used in the publication "Revisiting the diffuse layer polarization of a spherical grain in electrolytes with numerical solutions of Nernst-Planck-Poisson equations", which is submitted to the Journal of Geophysical Research: Solid Earth. </p> <p>Note:</p> <ol> <li>file parallel plate_static.mph is the model used to generate numerical results in Figure 2</li> <li>file parallel plate_dynamic.mph is the model used to generate numerical results in Figure 3</li> <li>file grain_mechanism.mph is the model used to generate numerical results in Figures 4, 5, 6, 7, and 8</li> <li>file grain_salinity.mph is the model used to generate numerical results in Figures 9, 10, 11, and 12</li> <li>the solutions in the mph files are cleared to reduce file size. The solutions can be reproduced by conducting "solve" </li> </ol>
Data underpinning "Localisation and Sub-Diffusive Transport in Quantum Spin Chains With Dilute Disorder"
<p>It is widely believed that many-body localisation in one dimension is fragile and can be easily destroyed by thermal inclusions, however there are still many open questions regarding the stability of the localised phase and under what conditions it breaks down. Here I construct models with dilute disorder, which interpolate between translationally invariant and fully random models, in order to study the breakdown of localisation. This opens up the possibility to controllably increase the density of thermal regions and examine the breakdown of localisation as this density is increased. At strong disorder, the numerical results are consistent with commonly-used diagnostics for localisation even when the concentration of thermalising regions is high. At moderate disorder, I present evidence for slow dynamics and sub-diffusive transport across a large region of the phase diagram, suggestive of a `bad metal' phase. This suggests that dilute disorder may be a useful effective model for studying Griffiths effects in many-body localisation, and perhaps also in a wider class of disordered systems.</p>
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
spindle cell variant diffuse large B-cell lymphoma (NGS annotation file; high confidence calls) hematolrep-2136295
<p>Diffuse large B-cell lymphoma with spindle cell morphology is a rare variant. We present the case of a 74-year-old male who initially presented with a right supraclavicular (lymph) node enlargement. Histological analysis showed a proliferation of spindle-shaped cells with narrow cytoplasms. An immunohistochemical panel was used to exclude other tumors, such as melanoma, carcinoma, and sarcoma. The lymphoma was characterized by a cell-of-origin subtype of germinal center B-cell-like (GCB) based on Hans’ classifier (CD10-negative, BCL6-positive, and MUM1-negative); EBER negativity, and the absence of BCL2, BCL6, and MYC rearrangements. Mutational profiling using a custom panel of 168 genes associated with aggressive B-cell lymphomas confirmed mutations in ACTB, ARID1B, DUSP2, DTX1, HLA-B, PTEN, and TNFRSF14. Based on the LymphGen 1.0 classification tool, this case had an ST2 subtype prediction. The immune microenvironment was characterized by moderate infiltration of M2-like tumor-associated macrophages (TMAs) with positivity of CD163, CSF1R, CD85A (LILRB3), and PD-L1; moderate PD-1 positive T cells, and low FOXP3 regulatory T lymphocytes (Tregs). Immunohistochemical expression of PTX3 and TNFRSF14 was absent. Interestingly, the lymphoma cells were positive for HLA-DP-DR, IL-10, and RGS1, which are markers associated with poor prognosis in DLBCL. The patient was treated with R-CHOP therapy, and achieved a metabolically complete response.</p> <p>Carreras J, Kikuti YY, Miyaoka M, Hiraiwa S, Tomita S, Ikoma H, Kondo Y, Ito A, Nagase S, Miura H, Roncador G, Colomo L, Hamoudi R, Campo E, Nakamura N. Mutational Profile and Pathological Features of a Case of Interleukin-10 and RGS1-Positive Spindle Cell Variant Diffuse Large B-Cell Lymphoma. <em>Hematology Reports</em>. 2023; 15(1):188-200. https://doi.org/10.3390/hematolrep15010020</p>
spindle cell variant diffuse large B-cell lymphoma (hematolrep-2136295)
<p>Diffuse large B-cell lymphoma with spindle cell morphology is a rare variant. We present the case of a 74-year-old male who initially presented with a right supraclavicular (lymph) node enlargement. Histological analysis showed a proliferation of spindle-shaped cells with narrow cytoplasms. An immunohistochemical panel was used to exclude other tumors, such as melanoma, carcinoma, and sarcoma. The lymphoma was characterized by a cell-of-origin subtype of germinal center B-cell-like (GCB) based on Hans’ classifier (CD10-negative, BCL6-positive, and MUM1-negative); EBER negativity, and the absence of BCL2, BCL6, and MYC rearrangements. Mutational profiling using a custom panel of 168 genes associated with aggressive B-cell lymphomas confirmed mutations in ACTB, ARID1B, DUSP2, DTX1, HLA-B, PTEN, and TNFRSF14. Based on the LymphGen 1.0 classification tool, this case had an ST2 subtype prediction. The immune microenvironment was characterized by moderate infiltration of M2-like tumor-associated macrophages (TMAs) with positivity of CD163, CSF1R, CD85A (LILRB3), and PD-L1; moderate PD-1 positive T cells, and low FOXP3 regulatory T lymphocytes (Tregs). Immunohistochemical expression of PTX3 and TNFRSF14 was absent. Interestingly, the lymphoma cells were positive for HLA-DP-DR, IL-10, and RGS1, which are markers associated with poor prognosis in DLBCL. The patient was treated with R-CHOP therapy, and achieved a metabolically complete response.</p> <p>Carreras J, Kikuti YY, Miyaoka M, Hiraiwa S, Tomita S, Ikoma H, Kondo Y, Ito A, Nagase S, Miura H, Roncador G, Colomo L, Hamoudi R, Campo E, Nakamura N. Mutational Profile and Pathological Features of a Case of Interleukin-10 and RGS1-Positive Spindle Cell Variant Diffuse Large B-Cell Lymphoma. <em>Hematology Reports</em>. 2023; 15(1):188-200. https://doi.org/10.3390/hematolrep15010020</p>
(new version data) Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions
<p><strong>Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is the key to understand their remarkable catalytical, optical and electronic properties. Here, we probe the three-dimensional atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level using atomic resolution electron tomography. We successfully quantify the rich structural variety of core-shell nanoparticles with heteroepitaxy in 3D at atomic resolution. Instead of forming an atomically-sharp boundary, the core-shell interface is atomically diffuse with an average thickness of 4.2 Å, irrespective of the particle's morphology or crystallographic texture. We observed dissolved free Pd and Pt single atoms and sub-nanometer clusters using cryogenic electron microscopy. The high concentration of Pd in the diffusive interface is highly related to the free Pd atoms dissolved from the Pd seeds. These results advance our understanding of core-shell structures at the fundamental level, providing potential strategies into precise nanomaterial manipulation and chemical property regulation.</strong></p> <p> </p> <p>The data and source codes for the paper "Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions" are posted below.</p> <p><strong># Repositary Contents</strong></p> <p><strong>### 1. Experiment Data</strong></p> <p>Folder: [Measured_data](./1_Measured_data)</p> <p>This folder contains denoised and aligned ADF-STEM projections and corresponding finalized tilt angles for three Pd@Pt core-shell nanoparticles. Three particles are named PB (pentagonal bipyramid shaped), EPB (elongated pentagonal bipyramid shaped) and TO (truncated octahedron shaped), respectively.</p> <p><strong>### 2. Reconstructed 3D Volume</strong></p> <p>Folder: [Final_reconstruction_volume](./2_Final_reconstruction_volume)</p> <p>This folder contains 3D tomographic reconstruction volumes of three particles. For the source code of RESIRE algorithm used in these reconstructions, please see the [source code](https://github.com/AET-MetallicGlass/Supplementary-Data-Codes/tree/master/2_RESIRE_package) of Yao Yang's paper on github.</p> <p><strong>### 3. Atom Tracing and Classification</strong></p> <p>Folder: [Tracing_and_classification](./3_Tracing_and_classification)</p> <p>This folder contains the source code to trace and classify atoms in the 3D volume.</p> <p><strong>### 4. Experimental Atomic Models</strong></p> <p>Folder: [Final_coordinates](./4_Final_coordinates)</p> <p>This folder contains the final coordinates of three nanoparticles.</p> <p><strong>### 5. Analysis of core-shell interface and others</strong></p> <p>Folder: [Analysis_of_interface](./5_Analysis_of_interface)</p> <p>This folder contains the codes to analyse the pair distribution function (PDF), the core-shell interface, the local coordination structure (PTM), the displacement and strain map of three nanoparticles.</p>
Diffusion MRI/ PET Simulation
<p>These data are designed to simulate diffusion-weighted images using Fibrefox, analytical PET simulation with ASIM, and PET image reconstruction with STIR.</p> <p> </p> <p>file description </p> <p>/MRI<br> Fibers.fib: the ground-truth fiber tracks file from Tractography Challenge ISMRM 2015 Data<br> bvals, bvecs: the encoding scheme files for DWI simulation (similar to HCP protocol)<br> t1.mif: the fabricated T1 image<br> dwi.mif: the example DWI file simulated</p> <p>/PET<br> AAL_ROI_upsample: a template phantom file with 3 lesions <br> asim_pipeline.sh: batch code for ASIM simulation<br> AAL_ROI_noisy_2: normalised PET sinogram 1e+08 total count, ready to be reconstructed<br> AAL_ROI_noisy.yaff: normalised PET sinogram 1e+07 total count, ready to be reconstructed<br> AAL_ROI_noisy_2.yhdr: ASIM header file<br> OSEM_m962,par hdr_E_m962.hs: header files for STIR reconstruction with OSMAPOSL</p>
Horizontal surface eddy diffusion implementation in CESM/MOM6
<p>The source code, input files, main results, and plotting/analysis scripts that were used in Marques et a., 2023 submitted to JAMES.</p> <p>"Representing eddy diffusion in the surface boundary layer of ocean models with general vertical coordinates"</p>
Global clinopyroxene phenocryst with label of hydrogen diffusion
<p>Each row of the "clinopyroxene phenocryst" dataset represents one sample. The dataset includes the content of major elements (apfu), the content of water (ppm), and the label of whether hydrogen diffusion has occurred in the sample. The dataset also includes reference and geological background information.</p> <p> </p>
Echo from noise: synthetically generated cardiac ultrasound data using semantic diffusion models
<p>This is the data repository for the paper: "Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation", available at: https://arxiv.org/abs/2305.05424. The corresponding code is available at: https://github.com/david-stojanovski/echo_from_noise</p> <p> </p> <p>This is the first work to utilize Denoising Diffusion Probabilistic Models (DDPMs) for generating medical images using semantic label maps as a source image for conditioning the generated image.</p> <p>Each of the 400+50 CAMUS patients contributes with 4 labelled frames (ED and ES for 2 chamber and 4 chamber), totalling 1800 initial semantic maps, to which we added the sector label. These semantic maps then had five random deformations applied (a combination of random affine and elastic deformation) to produce, 9000 transformed semantic maps (8000 for training and 1000 for validation). </p> <p>Affine transformation ranges for rotation degrees, translate, scale and shear were: (-5, 5), (0, 0.05), (0.8, 1.05) and 5 respectively. This was implemented using the torchvision python package. Elastic deformation was implemented using the TorchIO package. The settings for number of control points and max displacement were (10, 10, 4) and (0, 30, 30) respectively.</p> <p>Using these 9000 semantic maps as input to the generative models, we produced 9000 synthetic ultrasound images.</p> <p>Each echo view folder contains 3 folders:</p> <p>1) annotations: augmented labels, with no sector label and no clipping due to sector</p> <p>2) images: semantic diffusion model inferenced images</p> <p>3) sector_annotations: label maps which contain ultrasound cone sector, which were used to generate corresponding semantic diffusion model images</p> <p>ema_0.9999_050000_2ch_ed_256.pt and ema_0.9999_050000_4ch_ed_256.pt are the saved checkpoints for the 2 and 4 chamber diffusion models respectively.</p> <p>The pretrained segmentation networks are provided within the <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/final_models.zip">final_models.zip</a> file.</p> <p>A diagram of image numbers is shown in <a href="https://zenodo.org/api/files/0af4e6a3-234d-40a3-8351-c91261628982/Data%20diagram.png">Data diagram.png</a></p>
Datasets for manuscript - Dirichlet diffusion score model for biological sequence generation.
<p>This repository holds the trained Dirichlet Diffusion Score models for various datasets.</p> <p><strong>best_models.tar.gz</strong></p> <p>It also contains all input data required to train your own models with scripts provided via <a href="https://github.com/jzhoulab/ddsm">github repository</a>.</p> <p><strong>data.tar.gz</strong></p> <p>This archive contains the following folders: </p> <ul> <li><strong>satnet_sudoku </strong>contains dataset with sudoku examples which we used for evaluation of sudoku model.</li> <li><strong>promoter_design</strong> contains dataset used for training promoter design model as well as Sei model weights. Please, read provided readme file before using it for training scripts. </li> </ul>
ScienceDex guides
Understand access before you commit
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.