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773 results for “bounds”
Supplementary codes and datasets for "Efficient numerical method for reliable upper and lower bounds on homogenized parameters"
<p>This repository supports L. Gaynutdinova, M. Ladecký, A. Nekvinda, I. Pultarová, and J. Zeman, <em>Efficient numerical method for reliable upper and lower bounds on homogenized parameters</em> (first announced as the arXiv preprint <a href="http://arxiv.org/abs/2208.09940">2208.09940</a>).</p> <p>In particular, it contains MATLAB source files for reproducing the results presented in Examples 1 and 2 of the manuscript for discretizations with <span class="math-tex">\(N_1 = N_2 = N_3 = 6, 12, 24\)</span>. For other parameters, the code needs to be modified manually.</p> <p>The most recent version of the codes is available in the <a href="https://gitlab.com/ul_bounds_homog_CTU/3d-fem-homogenized-parameters">GitLab repository</a>.</p>
Supplementary materials for "Spanish lower and upper bounded change of state verbs: Focusing on transitive experiencer object verbs", published in Linguistics: An Interdisciplinary Journal of the Language Sciences
<p>Supplementary files are as follows:</p> <ul> <li><strong>R-code_Scalarity.Rmd</strong> contains the R-code in a markdown version of the statistical analysis of the study.</li> <li><strong>R-code_Scalarity.html</strong> displays the R-code_Scalarity.Rmd in an html format.</li> <li><strong>Results_Scalarity.csv</strong> contains the data of the study.</li> </ul> <p>For more details on the description of the data, see the publication: "Spanish lower and upper bounded change of state verbs: Focusing on transitive experiencer object verbs", Linguistics.</p>
Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited
<p>Reproduction Package for the Paper "Tip of the Red Giant Branch Bounds on the Neutrino Magnetic Dipole Moment Revisited".</p> <p><strong>File Organization</strong></p> <ul> <li>MESA: MESA modifications to include losses due to the neutrino magnetic dipole moment, scripts to run the grid of models, and post processing pipeline scripts including the Worthey \& Lee bolometric correction code.</li> <li>ML_models: Machine learning code to train and use the models as well as the models themselves.</li> <li>analysis: Plotting code to create figures for papers and presentations.</li> <li>makeGrids: Scripts to create the different input grid files to run MESA on.</li> <li>mcmc: Scripts and plots for the MCMC analysis.</li> <li>mesa_data: All MESA models generated in this project.</li> <li>environment.yml: Conda environment for analysis and the mcmc. </li> </ul> <p>More details can be found in the README files within each directory.</p> <p><strong>Citation Policy</strong><br> If you use any part of this reproduction package for independent work, we recommend you cite the following papers:</p> <ul> <li>This paper</li> <li>https://arxiv.org/abs/2303.12069</li> <li>https://arxiv.org/abs/2305.03113</li> <li>Astrophys. J. Suppl. 192, 3 (2011)</li> <li>Astrophys. J. Suppl. 208, 4 (2013)</li> <li>Astrophys. J. Suppl. 234, 34 (2018)</li> <li>Astrophys. J. Suppl. 243, 10 (2019)</li> </ul> <p><strong>Software</strong></p> <p>Python version 3.8, NumPy version 1.22.3, Pandas version 1.4.3, Matplotlib version 3.5.1, Seaborn version 0.11.2, Tensorflow version 2.4.1, corner version 2.2.1, emcee version 3.1.2, MESA version 12778, MESASDK version x86_64-linux-20.3.2.</p>
Sandy seeds: Armor or invisibility cloak? Mucilage-bound sand physically protects seeds from rodents and invertebrates
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Data from: Memory-bound k-mer selection for large and evolutionary diverse reference libraries
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Data from: Sorting states of environmental DNA: Effects of isolation method and water matrix on recovery of membrane-bound, dissolved, and adsorbed states of eDNA
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Molecular dynamics simulation data of designed cyclic peptide - ligand 4 (receptor-ligand bound)
<p>Trajectories of receptor-ligand bound simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. This dataset contains simulations of ligand 4. Due to the file size limitation, ligand 1-3 data and simulation set-up files can be found here: http://doi.org/10.5281/zenodo.3780463<br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Molecular dynamics simulation data of designed cyclic peptide - ligand 1-3 (receptor-ligand bound)
<p>Trajectories of receptor-ligand bound simulation and simulation set-up files of designed cyclic peptide as MDM2 binders. This dataset contains simulations of ligand 1-3. Ligand 4 data can be found here: http://doi.org/10.5281/zenodo.3782629<br> The original paper of these designed cyclic peptide: Danelius, E., Pettersson, M., Bred, M., Min, J., Waddell, M. B., Guy, R. K., et al. (2016). Flexibility is important for inhibition of the MDM2/p53 protein–protein interaction by cyclic β-hairpins. <em>Org. Biomol. Chem.</em>, <em>14</em>(44), 10386–10393. http://doi.org/10.1039/C6OB01510G</p>
Energetic limits: Defining the bounds and trade-offs of successful energy management in a capital breeder
<p>1. Judicious management of energy can be invaluable for animal survival and reproductive success. Capital breeding mammals typically transfer energy to their young at extremely high rates while undergoing prolonged fasting, making lactation a tremendously energy demanding period. Effective management of the competing demands of the mother's energy needs and those of her offspring is presumably fundamental to maximising lifetime reproductive success.</p> <p>2. How does the mother maximise her chances of successfully rearing her pup, by ensuring that both her pup and herself have sufficient energy during this 'energetic fast'? While energy management models were first discussed in the 1990s, application of this analytical technique is still very much in its infancy. Recent work suggests that a broad range of species exhibit 'energy compensation'; during periods when they expend more energy on activity, their bodies partially compensate by reducing background (basal) metabolic rate as an adaptation to limit overall energy expenditure. However, the value of energy management models in understanding animal ecology is presently unclear.</p> <p>3. We investigate whether energy management models provide insights into the breeding strategy of phocid seals. Not only do we expect lactating seals to display energy compensation because of their breeding strategy of high energy transfer while fasting, but we anticipate that mothers exhibiting a lack of energy compensation are less likely to rear offspring successfully.</p> <p>4. On the Isle of May in Scotland, we collected heart rate data as a proxy for energy expenditure in 52 known individual grey seal (Halichoerus grypus) mothers, repeatedly across three years of breeding. We provide evidence that grey seal mothers typically exhibit energy compensation during lactation by down-regulating their background metabolic rate to limit daily energy expenditure during periods when other energy costs are relatively high. However, individuals that fail to energy compensate during the lactation period are more likely to end lactation earlier than expected.</p> <p>5. Our study is the first to demonstrate the importance of energy compensation to an animal's reproductive expenditure. Moreover, our multi-seasonal data indicate that environmental stressors may reduce the capacity of some individuals to follow the energy compensation strategy. </p>
Atomic resolution X-ray crystal structure of cisplatin bound to hen egg white lysozyme stored for 5 years ‘on the shelf’
<p>These are the raw diffraction images for crystals 1 and 2 underpinning PDBe code 5LXW.</p>
Proper modelling of ligand binding requires an ensemble of bound and unbound states
<p>Crystallographic data for structures described in the manuscript "Proper modelling of ligand binding requires an ensemble of bound and unbound states".</p>
TensorBoard runs for reinforcement learning on automated conjectured bounds on Laplacian spectral radius of graphs
<p>The zip archive contains separate folders with TensorBoard event files for the runs of our reinforcement learning implementation on the conjectured upper bounds for the Laplacian spectral radius, which are listed in Appendix B of the forthcoming paper: S. Al-Yakoob, M. Ghebleh, A. Kanso, D. Stevanović, Reinforcement learning for graph theory, I. Reimplementation of Wagner’s approach, Art Discrete Appl. Math. (2024).</p> <p>To view the contained graphs and the evolution of rewards, unzip the archive in a folder of your choice, and run in the terminal the command "tensorboard --logdir runs" from the parent folder of the unzipped "runs" folder.</p>
Molecular dynamic simulations of histamine-bound H4R
<p>MD simulations data for Histamine-bound H4R</p>
Molecular Dynamics Trajectories for the D3 receptor (D3R) complexes bound with a GαOβγ heterotrimer and 1) FOB02-04A bitopic agonist; 2) pramipexole.
<p>Molecular Dynamics Data for publication "Structure of the dopamine D3 receptor bound to a bitopic agonist reveals a new specificity site in an expanded allosteric pocket". Sandra Arroyo-Urea, Antonina L. Nazarova, Ángela Carrión-Antolí et al., Nat. Comm., revision (2024). https://doi.org/10.21203/rs.3.rs-3433207/v1</p> <p><br>This directory includes the PDB (Protein Data Bank) format file detailing the topology and the XTC (eXtended Trajectory) format file outlining the trajectories for two distinct complexes: 1) the FOB02-04A-bound structure of D3 receptor (D3R) complexes in association with a GαOβγ heterotrimer, and 2) the pramipexole-bound structures of D3R complexes also coupled to a GαOβγ heterotrimer. The data is organized in a strided trajectory with a timestep of 0.5 nanoseconds per frame. To reapply periodic boundary conditions, users can employ the standard periodic boundary condition commands available in Visual Molecular Dynamics (VMD) package.</p> <p> </p> <p>The MD trajectory data for this study was acquired as well as uploaded by Antonina L. Nazarova.</p>
Mechanistic principles of hydrogen evolution in the membrane-bound hydrogenase
<p>Optimized coordinates of DFT models of the [NiFe] active-site from the membrane-bound hydrogenase</p> <p>Table of contents<br>1. Ni-SIa state<br>2. Ni-L and Ni-C state<br>3. Ni-R state<br>4. Ni-SIa state (with His75+)<br>5. Ni-L and Ni-C state (with His75+)<br>6. Ni-R state (with His75+)<br>7. [NiFe] active-site from DvMF in the Ni-R state (Geometries optimized using various DFT functional)</p>
The existence of optimal (v,4,1) optical orthogonal codes achieving the Johnson bound
<p>This is a program for checking Lemma 3.1 of the paper ``The existence of optimal (v,4,1) optical orthogonal codes achieving the Johnson bound'' .</p>
X-ray diffraction data for CNPase bound to nanobody 8C
<p>X-ray diffraction dataset for complex between mouse CNPase catalytic domain and anti-CNPase nanobody 8C</p>
Molecular dynamics simulation of MFSD1 in apo, His-Ala, Lys-Ala and Leu-Ala bound
<p>The MFSD1 structures were placed in a heterogenous bilayer composed of POPE (20%), 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC, 30%), Cholesterol (30%), and N-Palmitoyl-sphingomyelin (SPM, 20%) using CHARMM-GUI scripts and all simulations were performed using GROMACS 2021.3. </p> <p>Substrates were fitted into the binding site based on non-protein density observed in the outward-open Cryo-EM structure of GLMP-MFSD1+HisAla. </p> <p>Here, inital structures and 500 ns simulations for each replicate (Rep1-3) with the respective ligands and its starting conformation (Conf1 or Conf2) are given in PDB-format. </p> <p>The following ligands were used for the molecular dynamics simulations:</p> <ul> <li>LA - Leucyl-alanine dipeptide: both termini are charged</li> <li>KA - Lysyl-alanine dipeptide: both termini are charged, side chain of lysine is positively charged</li> <li>H0A - Histidyl-alanine dipeptide: both termini are charged, side chain of histidine is neutral</li> <li>HA - Histidyl-alanine dipeptide: both termini are charged, side chain of histidine is positively charged</li> </ul>
Impact of Andreev Bound States within the Leads of a Quantum Dot Josephson Junction
<p>This repository contains all the raw data and the code used to generate the figures of the article "Impact of Andreev Bound States within the Leads of a Quantum Dot Josephson Junction".</p> <ul> <li>The plotting.ipynb notebook creates the majority of the figures. To run it you need to install proplot. Create a fresh python environment for it, since you might need to downgrade numpy to the 1.19.5 version and matplotlib to the 3.4.3 one. To install the relevant python packages and open the notebook you can download Anaconda and use the following terminal instructions:<br> <pre><code>conda create -n andreev-trimer python=3.10 conda activate andreev-trimer conda install proplot conda install jupyterlab xarray netcdf4 tqdm jupyter lab</code></pre> extraction-3D.ipynb extracts the charge degeneracy points from 3D charge stability diagram measurements. To run it you need to install scikit-image. If you want to export additional .gif images install imageio as well:<br> <pre><code>conda install scikit-image imageio</code></pre> </li> <li>To visualize the 3D charge stability diagrams (Figure 5) you can use plotting-3D.ipynb. To run it you need to install pyvista. A dedicated environment is recommended. To install pyvista and run it with JupyterLab use the following instructions:<br> <pre><code>conda create -n pyvista python=3.9 conda activate pyvista conda install nodejs pip install jupyter pyvista trame jupyter lab</code></pre> </li> <li>Finally, simulations.ipynb computes all the theoretical simulations. To run it you need cython and you can install it in a dedicated environment using the following instructions:<br> <pre><code>conda create -n theory conda activate theory conda install jupyterlab matplotlib cython scipy jupyter lab</code></pre> </li> </ul>
Geometric deep learning improves generalizability of MHC-bound peptide predictions
<p>Full dataset and trained models from the manuscript "<strong>Geometric deep learning improves generalizability of MHC-bound peptide predictions</strong>".</p> <p>"outputs_and-BA_data.zip" contains the networks' outputs for each cross-validation experiment and a "full_dataset.csv" containing the initial BA data.<br>Note: this file has been updated (2024/11/26) due to errors in generating some of the previous csvs. In the earlier version, both MLP and CNN outputs reported were wrong. The correct values are now reported in the updated csvs.</p> <p>"trained_models.zip" contains all the trained models parameters</p> <p>"propedia_ssl.zip" contains all the 3D models from propedia used to train the 3D-SSL</p> <p>"pdb.zip" contains 3D models generated in PANDORA and used to train CNN, GNN and EGNN. It amounts to 145665 .pdb files, one for each human binding affinity entry from the initial dataset from O'Donnell et al. The list of entries used to actually train networks after filtering can be found in outputs_and-BA_data.zip", in the "full_dataset.csv" file. </p> <p> </p> <p>CHANGELOG v4:</p> <p>- In outputs_and-BA-data.zip, updated CNN_AlleleClustered_test_crossval.csv and CNN_shuffled_test_crossval.csv. These file had the wrong IDs paired with the network outputs.The IDs and labels are now consistent with the outputs.</p> <p>- Updated reference from the preprint to the published article. </p> <p> </p>
ScienceDex guides
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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.