Helmholtz Institute Freiberg for Resource Technology
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Data publication: Nonreciprocal spin-wave dispersion in magnetic bilayers
No full textThe repository contains the experimental BLS data and script files to generate the simulated data with TetraX
Data publication: Resonant defect states of transparent conductive oxide SnO2:Ta revealed by excitation wavelength-dependent Raman spectroscopy and hybrid functional DFT calculations
No full textThe data publication contains the primary data used for the publication. There are three groups of data: Raman data, optical data, and DFT data. The two former are in txt format, the DFT partially as Excel and partially as txt file
Multiphase Python Repository by HZDR
No full textThe python package provides several routines and scripts required to operate the code and cases repositories containing additional code and set-ups for the open-source software released by the OpenFOAM Foundation. This includes among others utilities for pre- and post-processing of simulation cases, utilities to launch virtual environments containing the source code, and utilities to operate the continuous integration and continuous development environment in a self-hosted Gitlab instance
Minterpy - multivariate polynomial interpolation
No full textminterpy is an open-source Python package for a multivariate generalization of the classical Newton and Lagrange interpolation schemes as well as related tasks. It is based on an optimized re-implementation of the multivariate interpolation prototype algorithm (MIP) by Hecht et al.1 and thereby provides software solutions that lift the curse of dimensionality from interpolation tasks. While interpolation occurs as the bottleneck of most computational challenges, minterpy aims to free empirical sciences from their computational limitations
Data publication: Comparative Binding Studies of the Chelators Methylolanthanin and Rhodopetrobactin B to Lanthanides and Ferric Iron
No full textData on which the figures and findings of the article are base
Correlated Widefield-confocal Microscopy Dataset
No full textHow to cite us
Li, R., Della Maggiora, G., Andriasyan, V., Petkidis, A., Yushkevich, A., Deshpande, N., ... & Yakimovich, A. (2024). Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model. Communications Engineering, 3(1), 186.
@article{li2024microscopy,
title={Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model},
author={Li, Rui and Della Maggiora, Gabriel and Andriasyan, Vardan and Petkidis, Anthony and Yushkevich, Artsemi and Deshpande, Nikita and Kudryashev, Mikhail and Yakimovich, Artur},
journal={Communications Engineering},
volume={3},
number={1},
pages={186},
year={2024},
publisher={Nature Publishing Group UK London}
}
Download Timeout Troubleshooting
Use "-C" flag of curl in case you experience timeout of the download:
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Dataset
This dataset contains a sample of 600 fluorescently labelled nuclei of cultured cells imaged using widefield fluorescence microscopy and confocal fluorescence microscopy at different focal planes.
Image preprocessing
Notably, the hardware precision of the sectioning process led to variations in the step size when shifting the focal plane between the two devices. This resulted in distinct z-dimensions between the datasets obtained from the two microscopy techniques. The confocal stacks in raw data comprised 92 focal planes, whereas the widefield stacks consisted of only 40 slices. Each focal plane image had a shape [2048, 2048, 1]. Assuming the central slice of each stack to be the in-focus, we performed z-direction registration by downsampling the confocal stacks from the central slice (46th) to match the 40 slices of the widefield stacks. Due to the instrumental limitations, a slight drift was noticeable between images. To address this, we used the phase cross-correlation algorithm [2] to compensate for the offsets on the x-y plane for the z-dimension registered image stacks. Having completed the registration and alignment along three dimensions, we then partitioned the original images into non-overlapping patches with dimensions of [128, 128, 1] in the xy plane. This partitioned dataset serves as the test dataset for validating our blind-deconvolution model, conducted without the specific Point Spread Function (PSF) parameters [3].
Files description
The Widefield-confocal Microscopy Dataset is stored in the '*.npz' format, encompassing the variables 'c_img' and 'w_img.' These handles respectively denote the confocal images and their corresponding widefield microscopy images. Both types of data undergo registration, alignment, and normalization, with values scaled to range between [0.0, 1.0]. For each category, the data has a shape of [600, 128, 128, 40], where the first dimension denotes the individual field of view and the last dimension signifies the z-dimension representing changes in the focal plane for virtual sectioning. The first dimension corresponds to the patch number, each with a patch size of [128, 128].
Sample preparation and microscopy
A549 lung carcinoma cell line cells were seeded in 96-well imaging plates a night prior to imaging, then fixed with 4% paraformaldehyde (Sigma) and stained for DNA with Hoechst 33342 fluorescent dye (Sigma). Cell culture was maintained similarly to the procedures described in [1]. Next, stained cell nuclei were imaged using ImageXpress Confocal system (Molecular Devices) in either confocal or widefield mode employing Nikon 20X Plan Apo Lambda objective. To obtain 3D information images in both modes were acquired as Z-stacks with 0.3 µm and 0.7 µm for confocal and widefield modes respectively. Confocal z-stack was Nyquist sampled. The excitation wavelength was 405 nm and the emission was 452 nm. Using these settings, we obtained individual stacks for both modalities, with each stack covering 2048 by 2048 pixels or 699 by 699 µm.
References
Yakimovich, Artur, et al. "Plaque2. 0—a high-throughput analysis framework to score virus-cell transmission and clonal cell expansion." PloS one 10.9 (2015): e0138760.
Alink, Mark S. Oude, et al. "Lowering the SNR wall for energy detection using cross-correlation." IEEE transactions on vehicular technology 60.8 (2011): 3748-3757.
Li, Rui, et al. "Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model." arXiv preprint arXiv:2306.02929 (2023)
Data publication: Reversing Lanmodulin's Metal-binding Sequence in Short Peptides Surprisingly Increases the Lanthanide Affinity: Oops I Reversed it again!
No full textData on which the figures and findings of the article are base
Data publication: Comparative Binding Studies of the Chelators Methylolanthanin and Rhodopetrobactin B to Lanthanides and Ferric Iron
No full textData on which the figures and findings of the article are base
Data publication: Novel correlative microscopy approach for nano-bio interface studies of ultrafine particle-induced lung epithelial cell damage
No full textRaw data used in the figures and plot
Minterpy - multivariate polynomial interpolation
No full textminterpy is an open-source Python package for a multivariate generalization of the classical Newton and Lagrange interpolation schemes as well as related tasks. It is based on an optimized re-implementation of the multivariate interpolation prototype algorithm (MIP) by Hecht et al.1 and thereby provides software solutions that lift the curse of dimensionality from interpolation tasks. While interpolation occurs as the bottleneck of most computational challenges, minterpy aims to free empirical sciences from their computational limitations