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2,271 results for “transporte”
Decoupling of uptake and transport-related traits in absorptive roots across coexisting herbaceous species in alpine meadows
<div>The anatomical structure of roots determines their function. Coexisting species complementarily forage nutrients by roots themselves (e.g., root strategy) and their fungal partners (e.g., mycorrhizal strategy), leading to a tradeoff between root strategy and mycorrhizal strategy. However, few studies have specifically evaluated whether and how the root anatomical structures are involved in this tradeoff, especially for species in alpine ecosystems limited by extreme climate.Here, absorptive root anatomical and chemical traits and three key root traits commonly associated with nutrient foraging strategies, i.e., root strategy indicated by first-order root length and root branching intensity and mycorrhizal strategy indicated by arbuscular mycorrhizal fungal colonization, were examined across 68 herbaceous species in alpine meadows of the Tibetan Plateau.We observed that absorptive roots with higher branching intensity had more protoxylem poles, thinner cortices and smaller cortical cells, whereas absorptive roots with higher mycorrhizal colonization and longer first-order roots consistently had thicker cortices and larger cortical cells. Unexpectedly, root cortical traits responsible for nutrient uptake were decoupled from stelar traits specialized in water and nutrient transportation. The decoupling may be related to the non-coordinated changes in soil water and nutrient availability in the meadows of the Tibetan Plateau. In addition, we found that root cortical thickness and stelar radius increased at a similar rate rather than well-reported different rates with increasing root diameter. Our results demonstrate that root internal makeup plays an integral role in forming the diverse nutrient foraging strategies in belowground. These findings provide new insights into our understanding of plant coexistence and responses of alpine meadows to climate change on the Tibetan Plateau.</div>
MC particle transport simulations for the 2022 LET-measurements at DCPT: v1.0.0
Open the record for dataset details and reuse information.
Data for 'Simulating Photodynamic Therapy for the Treatment of Glioblastoma using Monte Carlo Radiative Transport'
<p>Files added include the data used to make each data plot within the paper.</p> <p>Figure 5:</p> <p>pen_depth_plot.py</p> <p>pen_depth_plot_y.py</p> <p>pen_depth_plot_z.py</p> <p>jmean_run2NB.dat</p> <p>o21_full_run2NB.dat</p> <p>rhokap_run2NB.dat</p> <p>tumour.dat</p> <p> </p> <p>Figure 6:</p> <p>tumour_percentage.py</p> <p>tumkill_full_994.dat</p> <p>tumkill_full_1.dat</p> <p> </p> <p>Figure 7:</p> <p>temp_run2NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p> <p>Figure 8:</p> <p>power_run2NB.dat</p> <p>s0_slice_run2NB.dat</p> <p>o23_slice_run2NB.dat</p> <p>o21_slice_run2NB.dat</p> <p>temp_slice_run2NB.dat</p> <p> </p> <p>Figure 9:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run11NB.dat</p> <p>percent_left_run12NB.dat</p> <p>percent_left_run13NB.dat</p> <p> </p> <p>Figure 10:</p> <p>percent_left_run1NB.dat</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run3NB.dat</p> <p>percent_left_run4NB.dat</p> <p> </p> <p>Figure 11:</p> <p>max_temp_time_run1NB.dat</p> <p>max_temp_timeNB.dat</p> <p>percent_left_run1NB.dat</p> <p>percent_left_run2NB.dat</p> <p> </p> <p>Figure 12:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run10NB.dat</p> <p>max_temp_time_run10NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p> <p>Figure 13:</p> <p>percent_left_run2NB.dat</p> <p>max_temp_timeNB.dat</p> <p>percent_left_run9NB.dat</p> <p>max_temp_time_run9NB.dat</p> <p> </p> <p>Figure 14:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run14NB.dat</p> <p>percent_left_run15NB.dat</p> <p> </p> <p>Figure 15:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run56NB.dat</p> <p>percent_left_run62NB.dat</p> <p>max_temp_time_run56NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p>
Effect of Long-Range Transported Fire Aerosols on Cloud Condensation Nuclei Concentrations and Cloud Properties at High Latitudes
<p>The tab-delimited files contain Aerosol chemical composition data, size distribution data measured by AMS, ACSM, MAAP, and SMPS, and DMPS at the SMEAR IV station (Puijo) and the Zepplein observatory (Zep) . Please read the file named 'Information.txt' for more details about the files.</p>
Data and Codes for "Non-local transport measurements in hybrid quantum Hall - superconducting devices"
<p>Datasets and processing code for reproduction of "Non-local transport measurements in hybrid quantum Hall - superconducting devices".</p>
Shelfbreak Jet Transport from OOI Pioneer
<p>This repository contains hourly Shelfbreak jet transport (Sv) derived from the three central moorings of the OOI Coastal Pioneer Array (https://ooinet.oceanobservatories.org/). </p> <p>Transport is computed from east velocity component (u, in m/s), which is then depth integrated from 15 until 115m. The depth integrated velocity (m2/s) is transformed in transport considering a jet width of 40 km, and then converted into Sv (by dividing by 10^6 factor). Thus, to convert the transport time series back to m2/s, the time series must be divided by 4.10^10.</p> <p>Tides were removed from the velocity component, with Utide harmonic estimation, using the 68 standard tidal coefficients, except for the Semi-annual and Annual components (Sa and SSa).</p> <p>First column: date (datetime format, year-month-day hour:min:second)<br>Second column: Qy (jet transport, in Sv)</p> <p> </p> <p>accompanying paper:</p> <p><span>Camargo, C. M. L.</span>, <span>Piecuch, C. G.</span>, & <span>Raubenheimer, B.</span> (<span>2024</span>). <span>From Shelfbreak to Shoreline: Coastal sea level and local ocean dynamics in the northwest Atlantic</span>. <em>Geophysical Research Letters</em>, <span>51</span>, e2024GL109583. <a href="https://doi.org/10.1029/2024GL109583">https://doi.org/10.1029/2024GL109583</a></p>
Code and datasets for "Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes"
<p>Code and datasets for:</p> <p>Delaney I., M. A. Werder, D. Felix, I. Albayrak, R. M. Boes, D. Farinotti, 2024, Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes. Water Resources Research. </p> <p>For more information, contact Ian Delaney (ianarburua.delaney@unil.ch).</p>
List of charge transport low molecular weight compound geometry files
<p>The dataset comprises geometry files for low molecular weight compounds related to electron transport and hole transport. These files store information about atoms and bonds. The optimization of molecular geometry is performed using Avogadro software. These molecular structural files can subsequently be utilized to calculate energy levels for molecules, for instance, using Gaussian software.</p>
A hierarchical model for eDNA fate and transport dynamics accommodating low concentration samples
<p>Environmental DNA (eDNA) sampling is an increasingly important tool for answering ecological questions and informing aquatic species management . Challenges of using eDNA include determining species source location(s) and accurately and precisely measuring low concentration eDNA samples, especially considering inhibitory compounds and multiple sources of ecological and measurement variability. These challenges must be overcome to optimize our use of modeling frameworks like the eDNA Integrating Transport and Hydrology (eDITH) model. To better understand eDNA fate and transport dynamics, our ability to estimate parameters within the eDITH framework, and our ability to reliably quantify low concentration samples, we developed a hierarchical model and used it to evaluate a fate and transport experiment. Our model addresses several low concentration challenges by modeling the number of copies in each PCR replicate as latent variables with a count distribution and conditioning detection and quantification on replicate copy number. We provide evidence that the eDNA removal rate was not constant through time, estimating that over 80% of eDNA was removed over the first 10 m, traversed in 41 seconds. After this initial period of rapid decay, eDNA decayed slowly with consistent detection through our furthest site 1km from the release location, traversed in 250 seconds. We show that the eDITH model parameters can be difficult to estimate in this scenario. Our model further allowed us to detect extra-Poisson variation in the allocation of copies to replicates. Despite not observing evidence for inhibition as typically quantified using internal positive controls in conjunction with a binary decision rule (e.g., $\Delta$Cq>3), we hypothesized this overdispersion could be due to inhibitors. We extended our hierarchical model to accommodate a continuous effect of inhibitors, and used our model to provide evidence for the inhibitor hypothesis and explore the implications, if true. We show that inhibitors can cause substantial underestimation of eDNA site concentration, bias eDITH model parameter estimates, and attribute measurement variability erroneously to ecological variability. While our model is not a panacea for all challenges faced when quantifying low eDNA concentrations, it provides a framework for a more complete accounting of uncertainty that can be further tested and refined.</p>
Data for: Inversion for Inferring Solar Meridional Circulation: The Case with Constraints on Angular Momentum Transport inside the Sun
<p>Solar meridional circulation profiles inferred by Hatta, Hotta, and Sekii (2024) ("Inversion for Inferring Solar Meridional Circulation: The Case with Constraints on Angular Momentum Transport inside the Sun"). Detailed information about the dataset can be found in a Readme pdf file. </p>
Probing structural superlubricity of two-dimensional water transport with atomic resolution.
<p>Here lies the tabulated data used to create the Figures for the Science manuscript ado1544 titled "Probing structural superlubricity of two-dimensional water transport with atomic resolution".</p>
03_HTMD_Bulk: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Bulk schemes. </p> <p># The forders are organized as:</p> <p>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.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p> <p> </p>
06_HTMD_Tunnels: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Tunnels schemes. </p> <p># The folders are organized as:</p> <p>Input_files/ # Contains .parm7 and .rst files of 30 seed conformations obtained from equilibrations and used for adaptive sampling inputs, <em>run_adaptiveMD.py</em> : Script file executing the adaptive sampling using distance matrix considering protein C-alpha atoms and heavy atoms of DBE.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p> <p> </p>
Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p><strong>00_Caver.tar.gz </strong>- 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). <br>final_clustering<br>├── tunnel_custers # contains caver output files for individual tunnels clusters <br>│ ├── ...<br>├── analysis # contains .csv output files for botttlenecks and tunnels charecteristics of individual tunnels clusters <br>│ ├── ...</p> <p><strong>01_CaverDock_Tunnels_Profile.tar.gz</strong> - Contains tunnel clusters consisting of the top 100 tunnels and the CaverDock analysis files obtained. <br># 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:<br>- <em>calculations/*/caverdock.conf</em> : The config file input for caverdock calculation. <br>-<em> calculations/*/DBE.pdbqt </em>: Input file for the substrate DBE.<br>- <em>calculations/*/stripped_system*.pdbqt </em>: Input file for the Protein/Receptor<br>- c<em>alculations/*/stripped_system*.dsd </em>: Tunnel discretization file. Notes: <br>- <em>calculations/*/stripped_system*.pdb </em>: PDB file for the tunnel. </p> <p><strong>02_Minimization_and_Equilibration.tar.gz</strong> - Contains input and output files used for AMBER minimization and equilibration of the molecular systems and seed conformations used for adaptive sampling simulations.</p> <p><strong>03_HTMD_Bulk</strong> - 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. <br><strong>04_HTMD_Cavity</strong> - 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.<br><strong>05_HTMD_Cavity_Bulk</strong> -<strong> </strong>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. <br><strong>06_HTMD_Tunnels</strong> - 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. </p> <p><strong>07_MD-Analysis.tar.gz</strong> - Contains MD analysis files obtained from 45 micro-seconds adaptive sampling simulations at 310K. <br># 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:<br>- <em>Epoch_Convergence/epochs_dist_counts.csv</em> : 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. <br>- <em>Distances/*/dist_s_r*.csv</em> : 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:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM <br>- <em>RMSD_RMSF/*/*.csv</em> : 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')<br>- <em>COM_RMSF/.xlsx</em> : 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)<br>- <em>Kinetics/kinetics.txt</em> : 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.<br>- <em>Percentages/.csv</em> : 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).<br>- <em>Tunnels_lengths/.csv</em> : Contains <em>tunnel_lengths.csv</em>, <em>Summary of tunnel lengths.xlsx</em> files with lengths of top 100 tunnels snapshots for tunnel clusters p1a, p1b, p2 and p3 in <em>tunnel_lengths.csv</em> 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 <em>Summary of tunnel lengths.xlsx</em> file. <br>- <em>Sample_proportions/.csv</em> : Contains <em>sample_proportions.csv</em> 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<br> or http://www.emma-project.org/v1.2.1/api/generated/pyemma.msm.flux.pathways.html?highlight=transition%20path<br> - <em>*.py</em> : Python scripts used to build and analysis Markov state models with use case and distances of ligand.<br>- <em>Generated_models/</em> : Contains <em>models_rep[].dat</em> files representating the matric data used to build MSM for respective schemes and replicates. The dirs are arranged as below:<br>├── Cavity<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>├── Cavity_Bulk<br>│ ├── model_rep1.dat<br>│ ├── model_rep2.dat<br>│ └── model_rep3.dat<br>└── Tunnels<br> ├── model_rep1.dat<br> ├── model_rep2.dat<br> └── model_rep3.dat </p> <p><br><strong>08_TransportTools.tar.gz</strong> - 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<br>results-*_rep0 # results for replicate 1 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep1 # results for replicate 2 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>results-*_rep2 # results for replicate 3 for given schemes for example cavity, cavity&bulk or tunnels.<br>├── data<br>│ ├── super_clusters<br>├── _internal<br>│ ├── ...<br>├── statistics<br>│ ├── ...<br>- <em>event.csv</em> file contains the aggregated summary of events inferred from the <em>4-filtered_events_statistics.txt</em> files of each results of respective schemes</p> <p><strong>09_MSM_states.tar.gz</strong> - 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: <br>├── Bulk<br>│ ├── rep1 # MSM states for replicate 1<br>│ ├── rep2 # MSM states for replicate 2<br>│ ├── rep3 # MSM states for replicate 3<br>├── Cavity<br>│ ├── rep1 <br>│ ├── rep2 <br>│ ├── rep3 <br>├── Cavity&Bulk<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3<br>├── Tunnels<br>│ ├── rep1 <br>│ ├── rep2<br>│ ├── rep3</p> <p><br><strong>10_MSM_fingerprints.tar.gz</strong> - Contains the Markov state models (MSMs) distances generated from repository dir <strong>09_MSM_states</strong> consisting the model*.pdb files. The distances were calculated using the cpptraj program of AMBER18 package.<br>- <em>MSM_Distances/*/rep*/*.csv</em> : Contains .csv file for the generated MSM models (0,1,2..). The following columns (calculated distances) are present in the .csv files:<br>D107_OD1_DBE_C1 <br>D107_OD2_DBE_C1 <br>D107_OD1_DBE_C2 <br>D107_OD2_DBE_C2 <br>N37_ND2_DBE_Br1 <br>N37_ND2_DBE_Br2 <br>W108_NE1_DBE_Br1 <br>W108_NE1_DBE_Br2 <br>D107_COM_DBE_COM <br>W108_COM_DBE_COM <br>N37_COM_DBE_COM <br>catal_COM_p1aCOM <br>catal_COM_p1bCOM <br>catal_COM_p2COM <br>catal_COM_p3COM <br>p1aCOM_DBE_COM <br>p1bCOM_DBE_COM <br>p2COM_DBE_COM <br>p3COM_DBE_COM <br>catal_COM_DBE_COM <br>p1aCOM_p1bCOM <br>p1aCOM_p2COM <br>p1aCOM_p3COM <br>p1bCOM_p2COM <br>p1bCOM_p3COM <br>p2COM_p3COM </p> <p><br><strong>11_ULS_clustering_and_transition_assignments.tar.gz</strong> - Contains files for analysis of utilization of the substrate DBE. Each folder contains two types of .csv files:<br>1. for the transition detection of DBE and the classification in Bulk (out_), Bottleneck (bt_), Unknown bottleneck (bt_unknown), Inside (in_) and <br>2. the second type as the charecterization on the tunnels utilization: Tunnel (p1a, p1b, p2, and p3), Mixed and Unknnown.<br># the details of the file arangements are as below for the studied schemes Bulk, Cavity, Cavity&Bulk and Tunnels:<br>├── average_tunnel_utilization_per_scheme.png<br>├── average_tunnel_utilization.png<br>├── Bulk<br>│ ├── Bulk_run_htmd_0_combined_df.csv<br>│ ├── Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Bulk_run_htmd_1_combined_df.csv<br>│ ├── Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Bulk_run_htmd_2_combined_df.csv<br>│ └── Bulk_run_htmd_2_transitions_counts.csv<br>├── Bulk&Cavity<br>│ ├── Cavity&Bulk_run_htmd_0_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_1_combined_df.csv<br>│ ├── Cavity&Bulk_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity&Bulk_run_htmd_2_combined_df.csv<br>│ └── Cavity&Bulk_run_htmd_2_transitions_counts.csv<br>├── Cavity<br>│ ├── Cavity_run_htmd_0_combined_df.csv<br>│ ├── Cavity_run_htmd_0_transitions_counts.csv<br>│ ├── Cavity_run_htmd_1_combined_df.csv<br>│ ├── Cavity_run_htmd_1_transitions_counts.csv<br>│ ├── Cavity_run_htmd_2_combined_df.csv<br>│ └── Cavity_run_htmd_2_transitions_counts.csv<br>├── parse_distances_msm.py<br>├── schemes_comparison_piechart_per_scheme.png<br>└── Tunnels<br> ├── Tunnels_run_htmd_0_combined_df.csv<br> ├── Tunnels_run_htmd_0_transitions_counts.csv<br> ├── Tunnels_run_htmd_1_combined_df.csv<br> ├── Tunnels_run_htmd_1_transitions_counts.csv<br> ├── Tunnels_run_htmd_2_combined_df.csv<br> └── Tunnels_run_htmd_2_transitions_counts.csv</p> <p> </p>
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
<p># Contains input, output, and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity&Bulk schemes. </p> <p># The folders are organized as:</p> <p>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.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
04_HTMD_Cavity: Incorporating prior knowledge in the seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites
<p># Contains input, output and restart files used for HTMD (High-throughput molecular dynamics) adaptive sampling simulations at 310K for Cavity schemes. </p> <p># The forders are organized as:</p> <p>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.<br>rep1/<br>└── adaptive_data/<br> ├── generators/ # Contains the initial generator files provided by the user<br> │ ├── ../structure.parm7<br> │ ├── ../input.ncrst<br> │ └── ...<br> ├── input/ # Contains the files needed to start all simulations of all epochs (automatically generated)<br> │ ├── ../equil1.log<br> │ ├── ../input.ncrst<br> │ └── ...<br>└──rep2/<br>...<br>...<br> </p>
Water will find its way: transport through narrow tunnels in hydrolases (water-protein interaction analysis)
<p>Repository including information on the analyses of water-protein interactions by MM/GBSA.</p> <p><strong>hal_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Hal.<br><strong>epx_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results for Epx.<br><strong>lip_mmgbsa_data.tar.gz</strong> - contains a minimal set of files necessary to recreate MM/GBSA calculations and also raw calculation results<strong> </strong>for Lip<strong>.</strong></p> <p>In each archive structure is:<br> {supercluster_id}: - supercluster directory<br> {event_nr}_{rounded_radius}: - transport event directory<br> renum.pdb -pdb file containing protein, water of interest and 8 Angstrom radius sphere of surrounding waters<br> water.txt - file denoting water ID for water of interest in the renum.pdb file<br> binding_[implicit|explicit].dat - file containing MMGBSA results for full implicit and partial explicit system<br> decomposition_[implicit|explicit].dat - file containing MMGBSA decomposition results for full implicit and partial explicit system<br> info_{supercluster_id}.csv - file containing descriptions of transport events in supercluster (columns: event_name, radius, frame, simulation, water_id)</p> <p><strong>results.tar.gz </strong>- contains csv files generated by <em>script collect_data_minimal.py</em> for each protein for full implicit and partial explicit solvent. Columns in those csv file are: event_name, supercluster, radius, frame, simulation, water_id, delta_total, delta_van_der_waals, delta_electrostatic, delta_G_solv</p> <p><strong>scripts.tar.gz</strong> - contains following scripts<br> read_transport_events.py<br> prep_inputs.py<br> run_MMGBSA_single.sh<br> collect_data_minimal.py<br> plot_bin.py</p> <p>To recreate MMGBSA calculations with provided inputs one should:<br>$ python read_transport_events.py [Hal.dat|Epx.dat|Lip.dat] > selection.txt<br>Prepare transport events information in a compatible format. Binary transport events databases are available in the repository.</p> <p>$ python prep_inputs.py selection.txt - {absolute output path} {supercluster_id} 1 <br>Prepare parm and coordinate file for each supercluster id included in computation. Last argument 1 point script to resume preparations from renum.pdb files.</p> <p>$ bash run_MMGBSA_single.sh {residue_count}<br>Runs MMGBSA for both partial explicit and fully implicit systems. Needs to be run for each event in each supercluster.<br>residue_count should be set to 293 for hal, 319 for epx, and 534 for lip.</p> <p>$ python collect_data_minimal.py [hal|epx|lip]/{supercluster_id} [hal|epx|lip]/info_{supercluster_id}.csv [0|1] >> output.csv<br>Gathers data from MMGBSA calculations for a single supercluster into csv file. <br>Last argument should be 0 for fully implicit system, 1 for partial explicit system.</p> <p>$ python plot_bin.py {hal_implicit_csv} {hal_explicit_csv} {epx_implicit_csv} {epx_explicit_csv} {lip_implicit_csv} {lip_explicit_csv}<br>Results visualization. Produces "combined.png".</p>
Fitness landscape of substrate-adaptive mutations in evolved APC transporters
<p>Growth rate calculations:</p> <p>Single colonies of <em>S. cerevisiae</em> Δ10AA pADHXC3GH-<em>GOI</em> were inoculated in YB media supplemented with 4 mm NH<sub>4</sub><sup>+</sup> and 0.1 mg/ml ampicillin, and grown until late logarithmic phase. The cultures were pelleted at 750 × <em>g</em> for 10 min at 30 °C and washed with YB media. The wells in the microplate were filled with the amino acids of interest to a final concentration of 2 mM and with culture cells to a final OD<sub>600</sub> of 0.04, to a final total well volume of 200 µl. Sterile water was added in the space between the wells to avoid evaporation. The prepared microplates included three biological replicates of the strains with the plasmid containing the gene of interest (GOI) and one biological replicate of the strain with the empty vector. The absorbance in each well was measured at 600 nm in 30 min intervals without shaking of the microplate, at 30 °C for 72 h in a SpectraMax ABS Plus plate reader. The data sets (CSV files) are the raw optical density readings from the growth assays, along with plate layout metadata (CSV files). The growth rates were derived based on the Baranyi growth model, using the <em>growthrates</em> package in <em>R</em>.</p> <p>Included transporter genes:</p> <p>Mutated transporters (script growth_rates_mutants_Baranyi2.r)</p> <p><em>AGP1</em>, <em>AGP1</em>-N, <em>AGP1</em>-V,<em> AGP1</em>-NV, <em>AGP1</em>-G, <em>AGP1</em>-T, <em>PUT4</em>, <em>PUT4</em>-S</p> <p>Wild-type transporters (script growth_rates_wild_types_Baranyi2.r)</p> <p><em>AGP1</em>, <em>BAP2</em>, <em>CAN1</em>, <em>HIP1</em>, <em>LYP1</em>, <em>MMP1</em>, <em>PUT4</em></p> <p> </p> <p>Plasmid sequences (Genbank files):</p> <p>pADHXC3GH-AGP1, pADHXC3GH-BAP2, pADHXC3GH-CAN1, pADHXC3GH-HIP1, pADHXC3GH-LYP1, pADHXC3GH-MMP1, pADHXC3GH-PUT4-S L207S, pADHXC3GH-PUT4, pADHXC3GH</p> <p> </p> <p>Measureing relative membrane fluorescence (all transporters in this study have C-terminal GFP tags):</p> <p>Micrographs were analyzed with ImageJ using the script analyse_cell_perimeter.ijm</p>
Reference Energy System for the Transport Sector
<p>This diagram illustrates a reference energy system designed for the transport sector.</p> <p>This visual aid can support the development of models on OSeMOSYS, LEAP, TIMES, or other energy modelling tools, as well as facilitate the integration of energy planning models like MAED and OSeMOSYS, or others.</p> <p>This material has been produced with support from the Climate Compatible Growth (CCG) programme. CCG is funded by UK aid from the UK government. However, the views expressed herein do not necessarily reflect the UK government's official policies. </p>
Carbon emission and NPV data of hydrogen system in transport and building sector
<p>The dataset contains the simulation environment data, including meteorological data, renewable energy system configuration, grid energy structure data, and energy balance data of the hydrogen systems; the comparison on the lifecycle carbon emissions between different vehicles; and results of the building-transportation energy sharing model, including the calculation process and results of net present value and carbon intensity of the hydrogen system.</p>
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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.