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opencc-by-4.0Dec 2023View details →

Molecular dynamics simulation of MFSD1 in apo, His-Ala, Lys-Ala and Leu-Ala bound

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.  Substrates were fitted into the binding site based on non-protein density observed in the outward-open Cryo-EM structure of GLMP-MFSD1+HisAla.  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.  The following ligands were used for the molecular dynamics simulations: <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>

opencc-by-4.0Mar 2024View details →

Data and scripts for: Airborne DNA reveals predictable spatial and seasonal dynamics of fungi

Fungi are among the most diverse and ecologically important kingdoms of life. However, the distributional ranges of fungi remain largely unknown, as do the ecological mechanisms that shape their distributions. To provide an integrated view of the spatial and seasonal dynamics of fungi, we implemented a globally distributed standardised aerial sampling of fungal spores. The vast majority of OTUs were detected only within one climatic zone, and the spatio-temporal patterns of species richness and community composition were mostly explained by annual mean air temperature. Tropical regions hosted the highest fungal diversity except for lichenized, ericoid mycorrhizal, and ectomycorrhizal fungi, which reached their peak diversity in temperate regions. The sensitivity in climatic responses was associated with phylogenetic relatedness, suggesting that large-scale distributions of some fungal groups are partially constrained by their ancestral niche. There was a strong phylogenetic signal in seasonal sensitivity, suggesting that some groups of fungi have retained their ancestral trait of sporulating only for a short period. Overall, our results show that the hyperdiverse kingdom of fungi follows globally highly predictable spatial and temporal dynamics, with seasonality in both species richness and community composition increasing with latitude. Our study reports patterns resembling those described for other major groups of organisms, thus making a major contribution to the long-standing debate on whether organisms with microbial lifestyles follow the global biodiversity paradigms known for macro-organisms. The analyses presented in the paper can be reproduced with the R-script pipeline provided here. The starting point for the scripts is the datafile allData.RData that was published originally by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). The datafile allData.RData is provided also here for convenience, and it includes the following three objects: metadata, taxonomy, and otu.table (see Ovaskainen et al. for details). The script pipeline consists of the following elements (for deltails, see the Methods of the paper): <ul> <li>Scripts S01: data preparation <ul> <li>S01.1_download_clim_data.R. This script downloads daily climatic data for the entire world.</li> <li>S01.2_select_and_preprocess_clim_data.R. This script selects the data relevant for the study locations and preprocesses it.</li> <li>S01.3_add_climatic_data_to_metadata.R. This script adds the preprocessed climatic data to the metadata.</li> <li>S01.4_otu_guild_assignment.R. This script performs the guild assignment to the OTUs. It utilizes the datafiles Fung_LifeStyle_Data.RDS and funguild_db.rds provided here, and it utilizes the taxonomy of ProtaxFungi provided by Ovaskainen et al. Data from: Global Spore Sampling Project: A global standardized dataset of airborne fungal DNA. https://doi.org/10.5281/zenodo.10435615 (2024). Note that while the paper presents analyses and results only for the trait database of Aguilar-Trugueros et al., the scipts repeat the trait analyses also for the FunGuild database. The reason for not showing the results for the FunGuild database in the paper was that the database of Aguilar-Trugueros et al. contains FunGuild as one of the data sources, and that the results were highly coherent between the two databases.</li> <li>S01.5_add_trait_data_to_taxonomy_and_metadata.R. This script adds the guild data and spore size data to taxonomy (taxon-specific traits) as well as to metadata (community-weighted mean traits). It utilizes the datafile Spore_data_12Nov21.RDS provided here. This script can also be used to generated simulated contamination to the OTU table by setting contaminate=TRUE.</li> </ul> </li> <li>Scripts S02: exploratory analyses <ul> <li>S02.1_show_descriptive_statistics.R. This script outputs basic desriptive statistics from the data.</li> <li>S02.2_make_study_design_maps.R. This script plots the study design map shown in the paper.</li> <li>S02.3_compute_site_and_biome_profiles.R. This script computes site_profiles (needed in ordinations) and biome_profiles (needed to create Venn diagrams).</li> <li>S02.4_make_venns.R. This script produces Venn diagrams.</li> </ul> </li> <li>Scripts S03: ordination analyses <ul> <li>S03.1_make_ordination_maps.R. This script makes the ordination analyses.</li> </ul> </li> <li>Scripts S04: univariate analyses <ul> <li>S04.1_conceptualize_univariate_models.R. This script produces a figure that illustrates conceptually the differenent model variants. </li> <li>S04.2_make_univariate_analysis.R. This script implements the univariate analyses.</li> <li>S04.3_show_univariate_results.R. This script summarizes the results of the univariate analyses by producing tables of AIC and R2.</li> <li>S04.4_plot_univariate_results.R. This script plots the univariate models.</li> <li>S04.5_compute_temporal_turnover.R. This script computes site-specific indices of temporal turnover.</li> <li>S04.6_show_temporal_turnover.R. This script generates a plot illustrating temporal turnover.</li> </ul> </li> <li>Scripts S05: Hmsc analyses <ul> <li>S05.1_define_Hmsc_models.R. This script defines the Hmsc models. It utilizes the R-function as.phylo.formula provided here.</li> <li>S05.2_export_Hmsc_models_for_fitting.R. This script exports the unfitted Hmsc-models for fitting with Hmsc-HPC that operates on python/tensorflow.</li> <li>S05.3_import_fitted_Hmsc_models.R. This script imports the fitted Hmsc-models back to Hmsc-R.</li> <li>S05.4_postprocess_Hmsc_results.R. This script postprocesses the results of the fitted Hmsc model.</li> <li>S05.5_show_Hmsc_results.R. This script generates a plot that illustrates the postprocessed results.</li> </ul> </li> </ul>

dryad36/100

Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest

Increasing hurricane frequency and intensity with climate change is likely to affect soil organic carbon (C) stocks in tropical forests. We examined the cycling of C between soil pools and with depth at the Luquillo Experimental Forest in Puerto Rico in soils over a 30-year period that spanned repeated hurricanes. We used a non-linear matrix model of soil C pools and fluxes ("soilR") and constrained the parameters with soil and litter survey data. Soil chemistry and stable and radiocarbon isotopes were measured from three soil depths across a topographic gradient in 1988 and 2018. Our results suggest that pulses and subsequent reduction of inputs caused by severe hurricanes in 1989, 1998, and two in 2017 led to faster mean transit times and younger mean ages of soil C in the particulate, occluded, and mineral-associated soil organic matter pools at 0–10 cm and 35–60 cm depths relative to a modeled control soil with constant inputs over the thirty years. Between 1988 and 2018, the occluded C stock increased, and d13C in all pools decreased, while changes in particulate and mineral-associated C were undetectable. The differences between 1988 and 2018 suggest that hurricane disturbance results in a dilution of the occluded light C pool with an influx of young, debris-deposited C, and possible microbial scavenging of old and young C in the particulate and mineral-associated pools. These effects led to a younger total soil C pool with faster mean transit times. Our results suggest that increasing frequency of intense hurricanes will speed up rates of C cycling in tropical forests, and eventually lead to net losses of C from tropical forest soils.

opencc-zeroMar 2024View details →

opencc-by-nc-4.0Mar 2024View details →

ToneTwist AFx Dataset: Custom Dynamic Fuzz

<div> <div> <h3>Settings</h3> <table> <tbody> <tr> <td>Gain</td> <td>Sensitivity</td> <td>Attack</td> <td>Release</td> <td>Volume</td> </tr> <tr> <td>5</td> <td>10</td> <td>1ms</td> <td>2500ms</td> <td>10</td> </tr> </tbody> </table> <h3>Dry with markers</h3> </div> </div> Dry inputs are a selection of clean guitar and bass recordings from different sources: <ul> <li><a href="https://www.idmt.fraunhofer.de/en/publications/datasets/guitar.html" target="_blank" rel="noopener">IDMT-SMT-GUITAR</a> - dataset 2 (7:23 min)</li> <li><a href="https://www.idmt.fraunhofer.de/en/publications/datasets/guitar.html" target="_blank" rel="noopener">IDMT-SMT-GUITAR</a> - dataset 4 - Career SG (6:08 min)</li> <li><a href="https://www.idmt.fraunhofer.de/en/publications/datasets/guitar.html" target="_blank" rel="noopener">IDMT-SMT-GUITAR</a> - dataset 4 - Ibanez 2820 (5:14 min)</li> <li><a href="https://www.idmt.fraunhofer.de/en/publications/datasets/bass_lines.html" target="_blank" rel="noopener">IDMT-SMT-Bass-Single-Track</a> - (5:58 min)</li> <li><a href="https://github.com/sdatkinson/neural-amp-modeler?tab=readme-ov-file#download-audio-files" target="_blank" rel="noopener">NAM: Neural Amp Modeler</a> - (3:11 min)</li> <li>Private Guitar Data - (5:19 min)</li> <li>YouTube Bass Recordings - (10:09 min)</li> </ul> Pre-processing: <ul> <li>All: <ul> <li>synchronization markers (2 impulses) added at start and end of every file</li> </ul> </li> <li>IDMT-SMT-GUITAR - dataset 2: <ul> <li>peak normalized to -6dBFS</li> </ul> </li> <li>NAM: <ul> <li>no pre-processing</li> </ul> </li> <li>Others: <ul> <li>peak normalized to -0.1dBFS</li> <li>signal multiplied by random number every 5 seconds (uniform distribution [0.1, 1.0] = [-20dB, 0dB])</li> </ul> </li> </ul> <div> <h3>Authors</h3> <a href="https://mcomunita.github.io/" target="_blank" rel="noopener">Marco Comunità</a> - <a href="http://c4dm.eecs.qmul.ac.uk/" target="_blank" rel="noopener">Centre for Digital Music</a>, Queen Mary University of London <h3>Github</h3> <a href="https://github.com/mcomunita/tonetwist-afx-dataset" target="_blank" rel="noopener">https://github.com/mcomunita/tonetwist-afx-dataset</a> <h3>Reference</h3> If you make use of this dataset, please cite the following publications: <pre><code>@inproceedings{comunita2023modelling, title={Modelling black-box audio effects with time-varying feature modulation}, author={Comunit{\`a}, Marco and Steinmetz, Christian J and Phan, Huy and Reiss, Joshua D}, booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, pages={1--5}, year={2023}, organization={IEEE} }</code></pre> <pre> </pre> <pre><code>@misc{comunità2025nablafxframeworkdifferentiableblackbox, title={NablAFx: A Framework for Differentiable Black-box and Gray-box Modeling of Audio Effects}, author={Marco Comunità and Christian J. Steinmetz and Joshua D. Reiss}, year={2025}, eprint={2502.11668}, archivePrefix={arXiv}, primaryClass={cs.SD}, url={https://arxiv.org/abs/2502.11668}, }</code></pre> </div>

03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  # The forders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  # The folders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, run_adaptiveMD.py : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

00_Caver.tar.gz - Contains CAVER (https://caver.cz/fil/download/manual/caver_userguide.pdf) input and output files used for identification of transport pathways in LinB86(PDB ID: 5LKA).  final_clustering ├── tunnel_custers # contains caver output files for individual tunnels clusters  │   ├── ... ├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters  │   ├── ... 01_CaverDock_Tunnels_Profile.tar.gz - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained.  # Each folder (tun_cluster_p1a, tun_cluster_p1b, tun_cluster_p2, tun_cluster_p3) contains input raw files used for CaverDock calculations for individual snapshots of the respective tunnel clusters named as stripped_system*. The details of those files are: - calculations/*/caverdock.conf :  The config file input for caverdock calculation.  - calculations/*/DBE.pdbqt : Input file for the substrate DBE. - calculations/*/stripped_system*.pdbqt : Input file for the Protein/Receptor - calculations/*/stripped_system*.dsd : Tunnel discretization file. Notes:  - calculations/*/stripped_system*.pdb : PDB file for the tunnel.  02_Minimization_and_Equilibration.tar.gz -  Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations. 03_HTMD_Bulk - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes.  04_HTMD_Cavity - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. 05_HTMD_Cavity_Bulk - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes.  06_HTMD_Tunnels - separate Zenodo repository, see below for the link. Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes.  07_MD-Analysis.tar.gz - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K.  # Each folder contains input raw files used to calculate epochs convergence, distances, RMSD, RMSF, kinetics, percentage, sample proportions and tunnel lengths. The details of those files are: - Epoch_Convergence/epochs_dist_counts.csv :  Contains the counts of DBE distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å) for the 30 epochs.  -  Distances/*/dist_s_r*.csv : Contains .csv file for the  frames wise for all studied schemes. The analysis were performed using the cpptraj program (https://amber-md.github.io/cpptraj/CPPTRAJ.xhtml). The following columns are present: D107_OD1_DBE_C1    D107_OD2_DBE_C1    D107_OD1_DBE_C2   D107_OD2_DBE_C2    N37_ND2_DBE_Br1   N37_ND2_DBE_Br2   W108_NE1_DBE_Br1   W108_NE1_DBE_Br2  D107_COM_DBE_COM    W108_COM_DBE_COM     N37_COM_DBE_COM    catal_COM_p1aCOM     catal_COM_p1bCOM    catal_COM_p2COM     catal_COM_p3COM     p1aCOM_DBE_COM     p1bCOM_DBE_COM     p2COM_DBE_COM     p3COM_DBE_COM   catal_COM_DBE_COM     p1aCOM_p1bCOM    p1aCOM_p2COM   p1aCOM_p3COM   p1bCOM_p2COM  p1bCOM_p3COM  p2COM_p3COM  - RMSD_RMSF/*/*.csv : Contains .csv files with RMSD and RMSF from the protein residues. For RMSF 1st row are residue number (1-295) and 2nd row are RMSF. For RMSD, 1st column are frame no. (0.1ns) and 2nd column are RMSD values respectively. The calcualtion were performed using pytraj (https://amber-md.github.io/pytraj/latest/index.html) program.             Example input: pytraj.rmsd(traj, mask='1-295@CA')             Example input: pytraj.rmsf(traj, mask=':1-295', options='byres') - COM_RMSF/.xlsx : Contains the center of mass (COM) distances calculated using the bottleneck residues for catalytic residues (N38, D108, W109), p1a (D147, F151, and V173), p1b (D147, W177, and L248), p2 (L211 and L248), and p3 (L143, F151, and I213) - Kinetics/kinetics.txt : Contains .csv file with kinetic information from studied scheme: Cavity, Cavity&Bulk and Tunnels for all the three replicates and calculated average kon, koff, koff/kon rates. - Percentages/.csv : Contains csv files for the percentages of DBE localization and distances from the active-site (0-5 Å), tunnel (5-19 Å), and bulk (>19 Å). - Tunnels_lengths/.csv : Contains tunnel_lengths.csv, Summary of tunnel lengths.xlsx files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in tunnel_lengths.csv and summary of respective tunnel clusters generated from Caver output (for more details please check https://www.caver.cz/fil/download/manual/caver_userguide.pdf with keywork "summary.txt") in the Summary of tunnel lengths.xlsx file.  - Sample_proportions/.csv : Contains sample_proportions.csv file with proportions or fraction individual metastable states while performing the transition pathway analysis. For more details please check https://software.acellera.com/htmd/htmd.kinetics.html  or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path  - *.py : Python scripts used to build and analysis Markov state models with use case and distances of ligand. - Generated_models/ : Contains models_rep[].dat files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below: ├── Cavity │   ├── model_rep1.dat │   ├── model_rep2.dat │   └── model_rep3.dat ├── Cavity_Bulk │   ├── model_rep1.dat │   ├── model_rep2.dat │   └── model_rep3.dat └── Tunnels     ├── model_rep1.dat     ├── model_rep2.dat     └── model_rep3.dat  08_TransportTools.tar.gz - Contains TransportTools (TT) analysis output, log and summary files for Cavity, Cavity&Bulk and Tunnels schemes. For more details on the workflow of TT, please visit https://github.com/labbit-eu/transport_tools results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels. ├── data │   ├── super_clusters ├──  _internal │   ├── ... ├── statistics │   ├── ... - event.csv file contains the aggregated summary of events inferred from the 4-filtered_events_statistics.txt files of each results of respective schemes 09_MSM_states.tar.gz - Contains the Markov state models (MSM) output files for Cavity, Cavity&Bulk and Tunnels schemes and three replicates. The MSMs were generated using the pyEMMA program and HTMD framework, for further details please follow https://software.acellera.com/htmd/documentation.html. The directories looks as below:  ├── Bulk │   ├── rep1 # MSM states for replicate 1 │   ├── rep2 # MSM states for replicate 2 │   ├── rep3 # MSM states for replicate 3 ├── Cavity │   ├── rep1  │   ├── rep2  │   ├── rep3  ├── Cavity&Bulk │   ├── rep1  │   ├── rep2 │   ├── rep3 ├── Tunnels │   ├── rep1  │   ├── rep2 │   ├── rep3 10_MSM_fingerprints.tar.gz - Contains the Markov state models (MSMs) distances generated from repository dir 09_MSM_states consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package. - MSM_Distances/*/rep*/*.csv : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files: D107_OD1_DBE_C1    D107_OD2_DBE_C1    D107_OD1_DBE_C2   D107_OD2_DBE_C2    N37_ND2_DBE_Br1   N37_ND2_DBE_Br2   W108_NE1_DBE_Br1   W108_NE1_DBE_Br2  D107_COM_DBE_COM    W108_COM_DBE_COM     N37_COM_DBE_COM    catal_COM_p1aCOM     catal_COM_p1bCOM    catal_COM_p2COM     catal_COM_p3COM     p1aCOM_DBE_COM     p1bCOM_DBE_COM     p2COM_DBE_COM     p3COM_DBE_COM   catal_COM_DBE_COM     p1aCOM_p1bCOM    p1aCOM_p2COM   p1aCOM_p3COM   p1bCOM_p2COM  p1bCOM_p3COM  p2COM_p3COM  11_ULS_clustering_and_transition_assignments.tar.gz - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files: 1. for the transition detection of DBE and the classification in  Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and  2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown. # the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels: ├── average_tunnel_utilization_per_scheme.png ├── average_tunnel_utilization.png ├── Bulk │   ├── Bulk_run_htmd_0_combined_df.csv │   ├── Bulk_run_htmd_0_transitions_counts.csv │   ├── Bulk_run_htmd_1_combined_df.csv │   ├── Bulk_run_htmd_1_transitions_counts.csv │   ├── Bulk_run_htmd_2_combined_df.csv │   └── Bulk_run_htmd_2_transitions_counts.csv ├── Bulk&Cavity │   ├── Cavity&Bulk_run_htmd_0_combined_df.csv │   ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv │   ├── Cavity&Bulk_run_htmd_1_combined_df.csv │   ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv │   ├── Cavity&Bulk_run_htmd_2_combined_df.csv │   └── Cavity&Bulk_run_htmd_2_transitions_counts.csv ├── Cavity │   ├── Cavity_run_htmd_0_combined_df.csv │   ├── Cavity_run_htmd_0_transitions_counts.csv │   ├── Cavity_run_htmd_1_combined_df.csv │   ├── Cavity_run_htmd_1_transitions_counts.csv │   ├── Cavity_run_htmd_2_combined_df.csv │   └── Cavity_run_htmd_2_transitions_counts.csv ├── parse_distances_msm.py ├── schemes_comparison_piechart_per_scheme.png └── Tunnels     ├── Tunnels_run_htmd_0_combined_df.csv     ├── Tunnels_run_htmd_0_transitions_counts.csv     ├── Tunnels_run_htmd_1_combined_df.csv     ├── Tunnels_run_htmd_1_transitions_counts.csv     ├── Tunnels_run_htmd_2_combined_df.csv     └── Tunnels_run_htmd_2_transitions_counts.csv

05_HTMD_Cavity_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes.  # The folders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, *run_adaptiveMD.py* : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

# Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes.  # The forders are organized as: Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, **run_adaptiveMD.py** : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE. rep1/ └── adaptive_data/     ├── generators/ # Contains the initial generator files provided by the user     │   ├── ../structure.parm7     │   ├── ../input.ncrst     │   └── ...     ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)     │   ├── ../equil1.log     │   ├── ../input.ncrst     │   └── ... └──rep2/ ... ...

dryad36/100

Data and code for: The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system

Understanding indirect interspecific effects (IIEs) on population dynamics is key for predicting community dynamics. Yet, empirically teasing apart IIEs from other interactions and population drivers is data-demanding. We used stochastic population models parameterized with long-term vital rate time series to simulate population trajectories and examine IIEs in a high-arctic vertebrate trophic chain: Svalbard reindeer, its scavenger (Arctic fox), and a migratory fox prey (barnacle goose). Reindeer carcass supply shaped fox abundance fluctuations, subsequently affecting goose fluctuations. Yet reindeer and goose population growth rates were only weakly correlated, probably due to stochasticity, density dependence, and life history traits. However, by isolating the effects of individual processes within our simulation model, we demonstrate the presence of strong IIEs on goose population fluctuations and extinction probability. Thus, we highlight the long-term impact of species interactions, including IIEs, on species coexistence and communities, beyond immediate effects and short-term fluctuations.

opencc-by-4.0Mar 2024View details →

Dataset for Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches

Data sets of the work "Evapotranspiration dynamics and partitioning in a grassed vineyard: ecophysiological and computational modelling approaches". You will find all data files needed for this work, organised by the figures of the paper. For the codes, refer to Flavio Bastos Campos. (2024). flaviobastoscampos/ET_dynamics_and_partitioning_vineyard: v2024.1 (v2024.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.10864169" target="_blank" rel="nofollow noopener">https://doi.org/10.5281/zenodo.10864169</a>.

Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy (J. Roy Soc INTERFACE, to be published, march 2024)

data and matlab code  from paper "Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy"  (paper submitted to J. Roy Soc INTERFACE, to be published, march 2024) Dynamics of Centipede Locomotion Revealed by Large-Scale Traction Force Microscopy  J.P. Rieu1, *, H. Delanoë-Ayari1, C. Barentin1, T. Nakagaki2 and S. Kuroda3 ,* 1 Institut Lumière Matière, University of Lyon, Université Claude Bernard Lyon 1, CNRS, F-69622, Villeurbanne, France 2 Research Institute for Electronic Science, Hokkaido University, N20W10 Kita-ku, Sapporo Hokkaido 001-0020, Japan 3 Faculty of Software and Information Technology, Aomori University, Koubata 2-3-1, Aomori, 030-0943, Japan * Authors for correspondence:  Jean-Paul Rieu (e-mail: jean-paul.rieu@univ-lyon1.fr) and Shigeru Kuroda (shigeru-kuroda@aomori-u.ac.jp)   Abstract We present a novel approach to traction force microscopy (TFM) for studying the locomotion of 10cm-long walking centipedes on soft substrates. Leveraging the remarkable elasticity and ductility of kudzu starch gels, we utilize them as a deformable gel substrate, providing resilience against the centipedes' sharp leg tips. Through optimizing fiducial marker size and density and fine-tuning imaging conditions, we enhance measurement accuracy. Our TFM investigation reveals traction forces along the centipede's longitudinal axis that effectively counterbalance inertial forces within the 0-10mN range, providing the first report of non-vanishing inertia forces in TFM studies. Interestingly, we observe waves of forces propagating from the head to the tail of the centipede, corresponding to its locomotion speed. Furthermore, we discover a characteristic cycle of leg clusters engaging with the substrate: forward force (friction) upon leg tip contact, backward force (traction) as the leg pulls the substrate while stationary, and subsequent forward force as the leg tip detaches to reposition itself in the anterior direction. This work opens perspectives for TFM applications in ethology, tribology, and robotics.

dryad36/100

Data from: forest stand dynamics of a short-stature tree species: ecological knowledge for sustainable forest management

This data set is a forest inventory of Nothofagus antarctica in southern Chile. More than 20 sites were selected. In each site, parallel transects were set and every 30 m a sampling point was set where the distance to the nearest four trees (in accordance with the cardinal points) were measured.

On the prediction of the time-varying behaviour of dynamic systems by interpolating state-space models

In this article, a local Linear Parameter Varying (LPV) model identification approach is exploited to analyze the dynamic behaviour of a structure whose dynamics varies over time. This structure is composed by two aluminum crosses connected by a rubber mount. To observe time-dependent variations on the dynamics of this assembly, it is placed in a climate chamber and submitted to a six minute temperature run-up. During this run-up the structure is continuously excited by a shaker. The load provided by this device is measured by a load cell, while six accelerometers are measuring the responses of the system. The temperatures of the air inside the climate chamber and at the surface of the mount are also continuously measured. It is found that during the performed temperature run-up, the rubber mount temperature increased from, roughly, 14℃ to, approximately, 35.2℃. By using the measured load provided by the shaker and the measured accelerations, Frequency Response Functions (FRFs) at five different rubber mount temperatures are computed. From each of these sets of FRFs, state-space models are estimated. Afterwards, these models are used to define an interpolating LPV model, which enables the computation of interpolated state-space models representative of the dynamics of the system at each time sample. It is found that by feeding the interpolated state-space models with the measured load, an accurate simulation of the measured accelerations is obtained. Moreover, by exploiting a joint input state estimation algorithm with the interpolated state-space models and with the measured accelerations, a very good prediction of the applied load can be obtained. It is also shown that if the time dependency of the dynamics of the system is ignored, the results are less accurate.

opencc-by-4.0Jan 2024View details →

Multifaceted Activity of Fabimycin: Insights from Molecular Dynamics Studies on Bacterial Membrane models

This dataset presents a comprehensive collection of input data for Molecular Dynamics (MD) simulations performed using the GROMACS simulation software. The included systems cover various membrane environments, each with distinctive properties. The systems consist of: <ol> <li>IM (Inner Membrane): Simulations involving the bacteral mimicking inner membrane environment.</li> <li>IM_OM (Inner Membrane and Outer Membrane Complex): Complex systems encompassing both inner and outer bacterial membrane models.</li> <li>OM_D (Double Symmetric Outer Membrane): Simulations featuring a symmetric outer membrane structure.</li> <li>OM (Asymmetric Outer Membrane): Simulations with an asymmetric outer membrane configuration.</li> <li>PC Membrane (Phosphatidylcholine Membrane): Simulations involving membranes composed of phosphatidylcholine.</li> </ol> For each membrane type, the dataset provides three replicas. The dataset includes initial and final structures (.gro files), simulation parameter files (.mdp), index files (.ndx), and topology files (.itp and .top) applicable to all systems.

DArTseq genetic dataset associated with the article "Hybrids as mirrors of the past: genomic footprints reveal spatio-temporal dynamics and extinction risk of alpine extremophytes in the mountains of Central Asia"

Description: This file stores genetic information on the single nucleotide polymorphism markers (SNPs) in the examined alkali grasses (Poaceae: Puccinellia). The dataset was generetad by Genome-Wide Restriction Fragment Analysis via the DArTseq platform (Diversity Arrays Technology Pty Ltd, Canberra, Australia), which combines complexity reduction methods, fragment size selection, and high-throughput sequencing, optimised for a target organism. The file contains raw data.   Usage notes: We used R (version 4.2.2, 2022-10-31; https://www.R-project.org/) and RStudio (version 2022.07.2+576 "Spotted Wakerobin" Release (e7373ef832b49b2a9b88162cfe7eac5f22c40b34, 2022-09-06; http://www.rstudio.com/) on Windows 8.1 to handle this file. We used the dartR R-package (version 2.7.2) with necessary dependencies to import, proccess and analyse this data file as an object of a class genlight (dartR) in the R environment. You may also handle the file as an object of a class genlight using the adegenet and ade4 R-packages. To learn more about installation procedure and how to use of the R-packages visit: https://cran.r-project.org/web/packages/available_packages_by_name.html.

opencc-by-4.0Jun 2023View details →

Dynamics of magnetization at infinite temperature in a Heisenberg spin chain

Transferred magnetization data presented in arxiv.org/abs/2306.09333.  The files are named according to the parameters mu, which describes the initial magnetization imbalance, and Delta, which describes the relative strengths of XX+YY and ZZ in the Hamiltonian. Each file contains a dictionary with the following keys (see manuscript for further details): n: Number of qubits mu: Initial magnetization imbalance parameter theta: fSim-gate parameter controlling XX+YY coupling phi: fSim-gate parameter controlling ZZ coupling mean: Mean magnetization transfer as a function of circuit depth d_mean: Uncertainty in mean magnetization transfer moments: n-th central moments for as a function of circuit depth, starting with n=0 d_moments: Uncertainty in central moments skewness: Skewness of distribution of transferred magnetization as a function of circuit depth d_skewness: Uncertainty in skewness as a function of circuit depth kurtosis: Kurtosis of distribution of transferred magnetization as a function of circuit depth d_kurtosis: Uncertainty in kurtosis as a function of circuit depth Z: Expectation value of Z for each initial state, qubit and circuit depth nTrials: Number of different initial states investigated In addition, the file `numerical_simulation_results.zip` contains the results of classical statevector simulations of our experiment, up to cycle 18.

Data for the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'

The deposited data were employed to generate the figures concerning single-cell RNA (scRNA) and spatial transcriptomic analyses in the paper titled 'Dynamic Molecular Atlas for Cardiac Fibrosis at Single-Cell and Spatial Resolution: CD248 in Orchestrating Fibroblast-Immune Interaction'.

opencc-by-4.0Mar 2024View details →

Supplementary data: Bayesian multi-exposure image fusion for robust high dynamic range ptychography

Accompanying supplementary data for the paper. To download this data automatically and use the software, please refer to the details in the README of the linked github repository.  Github URL: <a href="https://github.com/microscopic-image-analysis/bayes-mef">https://github.com/microscopic-image-analysis/bayes-mef</a>

Data: Exploring the Drivers and Dynamics of Urban Waters: A Case Study of Wuhan from 1980 to 2060

The dataset comprises three main folders: "lulc," "factors," and "results." 1. lulc (Land Use/Land Cover): This folder contains raster data spanning from 1980 to 2020, representing land use and land cover dynamics. The data is stored in TIFF format, with values ranging from 1 to 6 denoting different land cover classes: farmland (1), woodland (2), grassland (3), waters (4), settlements (5), and unused land (6). 2. factors: This folder includes various driving factors influencing water dynamics. It encompasses eleven raster datasets, including Digital Elevation Models (DEM), annual precipitation data, and other relevant variables. The temporal coverage of these factors spans from 1980 to 2020. Additionally, demographic factors such as population data and economic indicators like GDP are available, covering the period from the 1990s to 2020. All datasets are stored in TIFF format with a spatial resolution of 30 meters. 3. results: This folder contains the outcomes of the analysis conducted using the PLUS (Patch Landscape and Urban Growth Model) framework. The results include outputs from the Landscape Ecological Assessment Protocol (LEAP) and the Cellular Automata for Regional Simulation (CARS) modeling. These results offer insights into the spatial and temporal patterns of water dynamics in the study area. The data structure follows a systematic organization to facilitate access and analysis. Each folder contains raster datasets stored in TIFF format, ensuring compatibility and ease of use with common GIS software. The temporal consistency of the datasets allows for longitudinal analysis of water dynamics, while the inclusion of various driving factors provides a comprehensive understanding of the underlying processes. Overall, this dataset offers valuable insights into land-water interactions and their implications for urban planning and water resource management strategies.