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zenodo32/100

Supplementary material 2 from: Niemi M, Pöyry J, Heiskanen I, Uotinen V, Nieminen M, Erkomaa K, Wallenius K (2014) Variability of soil enzyme activities and vegetation succession following boreal forest surface soil transfer to an artificial hill. Nature Conservation 8: 1-25. https://doi.org/10.3897/natureconservation.8.6369

Figure S1: Explanation note: The studied sites at the onset of the study in June 2003: a) Top b) Grove c) Middle d) North e) Alder f) Spruce.

opencc-by-4.0Aug 2014View details →
zenodo32/100

Supplementary material 5 from: Niemi M, Pöyry J, Heiskanen I, Uotinen V, Nieminen M, Erkomaa K, Wallenius K (2014) Variability of soil enzyme activities and vegetation succession following boreal forest surface soil transfer to an artificial hill. Nature Conservation 8: 1-25. https://doi.org/10.3897/natureconservation.8.6369

Figure S4: Explanation note: The studied s ites after a decade in June 2013: a) Top, b) Grove c) Middle d) North.

opencc-by-4.0Aug 2014View details →
zenodo32/100

Water migration through enzyme tunnels is sensitive to the choice of explicit water model (AldO + CYP2D6)

<p>This repository contains data for the alditol oxidase (AldO) and cytochrome P450 2D6 (CYP2D6). Data underpinning analyses of haloalkane dehalogenase DhaA are available from the related repository:&nbsp;<a href="https://doi.org/10.5281/zenodo.11489893">10.5281/zenodo.11489893</a>.</p> <p><strong>Content:</strong></p> <p><strong>AldO.tar.gz:</strong></p> <p><strong>tt_conda.yml -&gt; conda environment used for the calculations.&nbsp;</strong><br>&nbsp; &nbsp; &nbsp; &nbsp; Usage :<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;conda env create -f tt_conda.yml<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;conda activate tt_conda.yml&nbsp;</p> <p><strong>01_MD_simulations -&gt; the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs).</strong></p> <p>├── 01_MD_simulations<br>│ &nbsp; ├── 01_inputs<br>│ &nbsp; │ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; ├── scripts<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 00_prepare_model.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 01_minimization.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 02_equilibration.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 03_production.sh<br>│ &nbsp; │ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; └── tip4pew<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; ├── 02_outputs<br>│ &nbsp; │ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; └── tip4pew<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; ├── 03_analysis<br>│ &nbsp; │ &nbsp; ├── rmsd_opc.csv<br>│ &nbsp; │ &nbsp; ├── rmsd_tip3p.csv<br>│ &nbsp; │ &nbsp; ├── rmsd_tip4pew.csv<br>│ &nbsp; │ &nbsp; ├── rmsf_opc.csv<br>│ &nbsp; │ &nbsp; ├── rmsf_tip3p.csv<br>│ &nbsp; │ &nbsp; └── rmsf_tip4pew.csv<br>│ &nbsp; └── readme.txt<br><br><strong>02_caver -&gt; results of CAVER calculations, organized by models and TCGs.</strong></p> <p>├── 02_caver<br>│ &nbsp; ├── config_files<br>│ &nbsp; │ &nbsp; ├── calculate_tunnels.txt<br>│ &nbsp; │ &nbsp; └── clustering.txt<br>│ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; ├── readme.txt<br>│ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; └── tip4pew<br>│ &nbsp; &nbsp; &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── 5<br>│ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── 5</p> <p><strong>03_aquaduct -&gt; results of AQUA-DUCT calculations, organized by models and TCGs. &nbsp; </strong></p> <p>├── 03_aquaduct<br>│ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; ├── readme.txt<br>│ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; └── tip4pew<br>│ &nbsp; &nbsp; &nbsp; ├── TCG_d1.0_o1.6<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── 5<br>│ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── 5&nbsp;</p> <p><strong>04_transport_tools -&gt; the results of TransportTools and analysis done from TransportTools results.</strong></p> <p>├── 04_transport_tools<br>│ &nbsp; ├── overall_results<br>│ &nbsp; │ &nbsp; ├── config.in<br>│ &nbsp; │ &nbsp; ├── data<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── exact_matching_analysis<br>│ &nbsp; │ &nbsp; │ &nbsp; └── super_clusters<br>│ &nbsp; │ &nbsp; ├── statistics<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1-initial_tunnels_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1-initial_tunnels_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2-filtered_tunnels_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2-filtered_tunnels_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3-initial_events_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3-initial_events_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4-filtered_events_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4-filtered_events_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; └── comparative_analysis<br>│ &nbsp; │ &nbsp; ├── transport_tools.log<br>│ &nbsp; │ &nbsp; └── visualization<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1-visualize_initial_tunnels.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2-visualize_filtered_tunnels.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3-visualize_initial_events.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4-visualize_filtered_events.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── comparative_analysis<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── sources<br>│ &nbsp; └── scripts<br>│ &nbsp; &nbsp; &nbsp; ├── bottleneck_residues<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 5_bottleneck_residues.py<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── T1.png<br>│ &nbsp; &nbsp; &nbsp; ├── presence_of_tunnels<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 6_tunnels_before_assignment.py<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── before.png<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── number_frames.pkl<br>│ &nbsp; &nbsp; &nbsp; └── water_transport_analysis<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 8_main_figure.py<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── figure2.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── output<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── percent_frames_events_AldO.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── transit_time_median.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── tt_events.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── water_per_frame_AldO.png</p> <p>&nbsp;</p> <p><strong>CYP2D6.tar.gz:</strong></p> <p><strong>tt_conda.yml -&gt; conda environment used for the calculations.&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp; </strong>Usage :<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;conda env create -f tt_conda.yml<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp;conda activate tt_conda.yml&nbsp;</p> <p><strong>01_MD_simulations -&gt; the files to run simulation, out and restart files from simulation and simulation analysis results, organized by models and Tunnel Conformational Groups (TCGs).</strong></p> <p>├── 01_MD_simulations<br>│ &nbsp; ├── 01_inputs<br>│ &nbsp; │ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; ├── scripts<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 01_minimization_heating_CPU_ARES_prep.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 02_equilibration_GPU_ARES_prep.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 03_production_ARES.sh<br>│ &nbsp; │ &nbsp; │ &nbsp; └── prepare_model_3tbg.sh<br>│ &nbsp; │ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; └── tip4pew<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; ├── 02_outputs<br>│ &nbsp; │ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; └── tip4pew<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; ├── 03_analysis<br>│ &nbsp; │ &nbsp; ├── rmsd_opc.csv<br>│ &nbsp; │ &nbsp; ├── rmsd_tip3p.csv<br>│ &nbsp; │ &nbsp; ├── rmsd_tip4pew.csv<br>│ &nbsp; │ &nbsp; ├── rmsf_opc.csv<br>│ &nbsp; │ &nbsp; ├── rmsf_tip3p.csv<br>│ &nbsp; │ &nbsp; └── rmsf_tip4pew.csv<br>│ &nbsp; └── readme.txt</p> <p><strong>02_caver -&gt; results of CAVER calculations, organized by models and TCGs.</strong></p> <p>├── 02_caver<br>│ &nbsp; ├── config_files<br>│ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; ├── readme.txt<br>│ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; └── tip4pew<br>│ &nbsp; &nbsp; &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── 5<br>│ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── 5</p> <p><strong>03_aquaduct -&gt; results of AQUA-DUCT calculations, organized by models and TCGs. &nbsp; </strong></p> <p>├── 03_aquaduct<br>│ &nbsp; ├── opc<br>│ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; ├── readme.txt<br>│ &nbsp; ├── tip3p<br>│ &nbsp; │ &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; │ &nbsp; └── 5<br>│ &nbsp; │ &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── 5<br>│ &nbsp; └── tip4pew<br>│ &nbsp; &nbsp; &nbsp; ├── TCG_d1.7_o1.6_TCG_d1.1_o1.4_TCG_d1.1_o.1.1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── 5<br>│ &nbsp; &nbsp; &nbsp; └── TCG_d2.0_o1.9_TCG_d1.6_o1.6_TCG_d1.6_o1.1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 1<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 2<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 3<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 4<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── 5</p> <p><strong>04_transport_tools -&gt; the results of TransportTools and analysis done from TransportTools results.</strong></p> <p>├── 04_transport_tools<br>│ &nbsp; ├── overall_results<br>│ &nbsp; │ &nbsp; ├── config.in<br>│ &nbsp; │ &nbsp; ├── data<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── exact_matching_analysis<br>│ &nbsp; │ &nbsp; │ &nbsp; └── super_clusters<br>│ &nbsp; │ &nbsp; ├── statistics<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1-initial_tunnels_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 1-initial_tunnels_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2-filtered_tunnels_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 2-filtered_tunnels_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3-initial_events_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 3-initial_events_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4-filtered_events_statistics_bottleneck_residues.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; ├── 4-filtered_events_statistics.txt<br>│ &nbsp; │ &nbsp; │ &nbsp; └── comparative_analysis<br>│ &nbsp; │ &nbsp; ├── transport_tools.log<br>│ &nbsp; │ &nbsp; └── visualization<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 1-visualize_initial_tunnels.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 2-visualize_filtered_tunnels.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 3-visualize_initial_events.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4.pse<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── 4-visualize_filtered_events.py<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; ├── comparative_analysis<br>│ &nbsp; │ &nbsp; &nbsp; &nbsp; └── sources<br>│ &nbsp; └── scripts<br>│ &nbsp; &nbsp; &nbsp; ├── bottleneck_analyses<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 5_bottleneck_residues.py<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── Ch2B-F.png<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── ChS.png<br>│ &nbsp; &nbsp; &nbsp; ├── presence_of_tunnels<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── 6_tunnels_before_assignment.py<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; ├── before.png<br>│ &nbsp; &nbsp; &nbsp; │ &nbsp; └── number_frames.pkl<br>│ &nbsp; &nbsp; &nbsp; └── water_transport_analysis<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── 8_main_figure.py<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── figure2_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── figure2_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── figure2_Ch2S.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── output<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── percent_frames_events_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── percent_frames_events_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── percent_frames_events_ChS.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── transit_time_median_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── transit_time_median_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── transit_time_median_ChS.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── tt_events_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── tt_events_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── tt_events_Ch2S.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── water_per_frame_Ch2B-Ch2F.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; ├── water_per_frame_Ch2C.png<br>│ &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; └── water_per_frame_ChS.png</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Enzyme-Substrate Interaction Dataset and Trained MEI Model for Deep Learning-Driven Insights

<p>This dataset and trained model are provided as part of our research on <em>Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery</em>.</p>

opencc-by-4.0Oct 2024View details →
zenodo32/100

Interactions between methylerythritol phosphate (MEP) pathway metabolites and Escherichia coli K-12 fatty acid biosynthesis enzymes

<p>Raw data files from native mass spectrometry analyses examining the interactions between methylerythritol phosphate (MEP) pathway metabolites and Escherichia coli K-12 fatty acid biosynthesis enzymes. Recorded in positive mode direct injection.</p>

opencc-by-4.0Oct 2024View details →
dryad32/100

Data from: Shrub encroachment does not reduce the activity of some soil enzymes in Mediterranean semiarid grasslands

Shrub encroachment is a worldwide phenomenon with implications for desertification and global change. We evaluated its effects on the activities of urease, phosphatase and b-glucosidase in Mediterranean semiarid grasslands dominated by Stipa tenacissima by sampling 12 sites with and without resprouting shrubs along a climatic gradient. The presence of shrubs affected the evaluated enzymes at different spatial scales. Soils under S. tenacissima tussocks and in bare ground areas devoid of vascular plants had higher values of phosphatase and urease when the shrubs were present. For the b-glucosidase, this effect was site-specific. At the scale of whole plots (30 m 30 m), shrubs increased soil enzyme activities between 2% (b-glucosidase) and 22% (urease), albeit these differences were significant only in the later case. Our results indicate that shrub encroachment does not reduce the activity of extracellular soil enzymes in S. tenacissima grasslands.

opencc-zeroDec 2011View details →
dryad32/100

Data from: Convergence in organ size but not energy metabolism enzyme activities among wild Lake Whitefish (Coregonus clupeaformis) species pairs

The repeated evolution of similar phenotypes by similar mechanisms can be indicative of local adaptation, constraints or biases in the evolutionary process. Little is known about the incidence of physiological convergence in natural populations, so here we test whether energy metabolism in 'dwarf' and 'normal' Lake Whitefish evolves by similar mechanisms. Prior genomic and transcriptomic studies have found that divergence in energy metabolism is key to local adaptation in whitefish species pairs, but that distinct genetic and transcriptomic changes often underlie phenotypic evolution among lakes. Here, we predicted that traits at higher levels of biological organization, including the activities of energy metabolism enzymes (the product of enzyme concentration and turnover rate) and the relative proportions of metabolically active tissues (heart, liver, skeletal muscle), would show greater convergence than genetic and transcriptomic variation. We compared four whitefish species pairs and found convergence in organ size whereby all dwarf whitefish populations have a higher proportion of red skeletal muscle, three have relatively larger livers and two have relatively larger ventricles than normal fish. On the other hand, hepatic and muscle enzyme activities showed little convergence and were largely dependent on lake of origin. Only the most genetically divergent species pair (Cliff Lake) displayed white muscle enzyme activities matching results from laboratory-reared normal and dwarf whitefish. Overall, these data show convergence in the evolution of organ size, but not in the activities of candidate enzymes of energy metabolism, which may have evolved mainly as a consequence of demographic or ecological differences among lakes.

opencc-zeroDec 2015View details →
dryad32/100

Data from: Within-species tradeoffs in plant-stimulated soil enzyme activity and growth, flowering and seed size

1. Soil microbial communities affect species demographic rates of plants. In turn, plants influence the composition and function of the soil microbiome, potentially resulting in beneficial feedbacks that alter their fitness and establishment. For example, differences in the ability to stimulate soil enzyme activity among plant lineages may affect plant growth and reproduction. 2. We used a common garden study to test differences in plant-stimulated soil enzyme activity between lineages of the same species across developmental stages. 3. Lineages employed different strategies whereby growth, days to flowering and seed size traded-off with plant-stimulated soil enzyme activity. Specifically, the smaller seeded lineage stimulated more enzyme activity at the early stage of development and flowered earlier while the larger seeded lineage sustained lower but consistent enzyme activity through development. 4. We suggest that these lineages, which are both successful invaders, employ distinct strategies (a colonizer and a competitor) and differ in their influence on soil microbial activity. Synthesis. The ability to influence the soil microbial community by plants may be an important trait that trades-off with other growth, flowering and seed size for promoting plant establishment, reproduction and invasion.

opencc-zeroDec 2017View details →
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Soil extracellular enzyme activity and stoichiometry in China's forests

<p>1. Ecoenzymatic stoichiometry links microbial decomposition with nutrient mineralization and improves our understanding of nutrient cycling in terrestrial ecosystems. Microbial C:N:P acquisition in the topsoil converged at a ratio of 1:1:1 in global ecosystems. However, whether the ratio of microbial acquisition is stable in forest soils, and is applicable among different soil depths remain unknown.</p> <p>2. Based on large-scale soil sampling in China's forests, we examined the patterns and environmental drivers of the eight most-widely measured enzyme activities and the relevant stoichiometry.</p> <p>3. We found that the ratio of C:N:P acquisition significantly deviated from 1:1:1. The specific enzyme activities (g SOC-1) did not change significantly with latitude except those for xylosidase and acid phosphatase. Similarly, only the C:P acquisition ratio increased with latitude. Vertically, the specific activities of C-acquiring enzymes mainly increased, N-acquiring enzymes decreased, and P-acquiring enzymes did not change with soil depth. Moreover, all ratios of microbial acquisition decreased, and the percentage of recalcitrant C increased significantly with increasing depth. Our study also showed that temperature and soil C:N ratio were the important factors in explaining the variations in specific enzyme activities and microbial nutrient acquisition.</p> <p>4. Our results indicated that no constant microbial C:N:P acquisition ratio can be widely recognized, and that SOC quality changed from labile to recalcitrant with depth. We highlight that depth-dependent enzymatic processes should be considered in future SOC dynamic models.</p>

opencc-zeroMar 2020View details →
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Data from: Heritability, environmental effects, and genetic and phenotypic correlations of oxidative stress resistance-related enzyme activities during early life stages in Atlantic salmon

Oxidative stress (OS) may pose important physiological constraints on individuals, affecting trade-offs between growth and reproduction or ageing and survival. Despite such evolutionary and ecological importance, the results from studies on the magnitude of individual variation in OS resistance and the underlying causes of this variation such as genetic, environmental, and maternal origins, remain inconclusive. Using a high throughput methodology, we investigated the activity levels in three OS resistance-related enzymes (superoxide dismutase, SOD; glutathione reductase, GR; glutathione S-transferase, GST) during the early life stages of 1000 individuals from 50 paternal half-sib families in two populations of Atlantic salmon. Using animal mixed models, we detected the presence of narrow-sense heritability for SOD and GST; that for GST differed between populations due to differences in environmental variance. We found support for the presence of common environmental variation, including maternal effects, for only GR. Using a bivariate animal model, we detected a positive environmental correlation between activity levels of SOD and GST but were unable to detect an additive genetic correlation. Our results complement previous heritability findings for levels of reactive oxygen species or OS resistance by demonstrating the presence of heritability for OS-related enzyme activities. Our findings provide a foundation for future work, such as investigations on the evolutionary importance of variation in enzyme activities. In addition, our findings emphasise the importance of accounting for developmental stage, environmental variance, and kin relationships when investigating the OS-response at the enzyme activity level.

opencc-zeroDec 2015View details →
dryad32/100

Data from: Structural reorganization of the chromatin remodeling enzyme Chd1 upon engagement with nucleosomes

The yeast Chd1 protein acts to position nucleosomes across genomes. Here, we model the structure of the Chd1 protein in solution and when bound to nucleosomes. In the apo state, the DNA-binding domain contacts the edge of the nucleosome while in the presence of the non-hydrolyzable ATP analog, ADP-beryllium fluoride, we observe additional interactions between the ATPase domain and the adjacent DNA gyre 1.5 helical turns from the dyad axis of symmetry. Binding in this conformation involves unravelling the outer turn of nucleosomal DNA and requires substantial reorientation of the DNA-binding domain with respect to the ATPase domains. The orientation of the DNA-binding domain is mediated by sequences in the N-terminus and mutations to this part of the protein have positive and negative effects on Chd1 activity. These observations indicate that the unfavorable alignment of C-terminal DNA-binding region in solution contributes to an auto-inhibited state.

opencc-zeroDec 2016View details →
zenodo32/100

First two years of reimbursed enzyme replacement therapy in the treatment of Fabry's disease in Poland

<p>There is dataset of collected information from&nbsp;seven largest academic centers in Katowice, Krak&oacute;w, Wrocław, Poznań, Gdańsk, Warszawa, and Ł&oacute;dź in Poland. The questionnaire included the following data: number of patients treated, number of patients qualified for ERT, and patient characteristics.&nbsp;</p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>

openother-openJul 2021View details →
zenodo32/100

Non-Coordinative Binding of O2 at the Active Center of a Copper-Dependent Enzyme

<p>Data underlying the figures in the publication &ldquo;Non-Coordinative Binding of O<sub>2</sub> at the Active Center of a Copper-Dependent Enzyme&rdquo;, published in <em>Angew.Chem. Int. Ed.,</em> <strong>2021</strong>, 60,6154 &ndash;6159.</p> <p><a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202014981">https://onlinelibrary.wiley.com/doi/10.1002/anie.202014981</a></p> <p>Table of contents:</p> <p><strong>1. Dataset</strong>; Zip file containing the data and model validation report of X-ray crystallography diffraction data of complexes in the publication, collected at the swiss light source at 100K. Data processed and refined according to standard procedure.</p>

opencc-by-4.0Jul 2021View details →
zenodo32/100

The moisture plasticizing effect on enzyme-catalyzed reactions in model and real systems in view of legume ageing and their hard to cook development

<p>Data used for the figures in the article</p>

opencc-by-4.0Aug 2021View details →
dryad32/100

Chemical analyses of three lysergic acid amide-producing Aspergillus species and sequences for phylogenetic analyses of associated enzymes

<p>Ergot alkaloids derived from lysergic acid have impacted humanity as contaminants of crops and as the bases of pharmaceuticals prescribed to treat dementia, migraines, and other disorders. Several plant-associated fungi in the Clavicipitaceae produce lysergic acid derivatives, but many of these fungi are difficult to culture and manipulate. Some <i>Aspergillus</i> species, which may be more ideal experimental and industrial organisms, contain an alternate branch of the ergot alkaloid pathway but none were known to produce lysergic acid derivatives. We mined genomes of <i>Aspergillus</i> species for ergot alkaloid synthesis (<i>eas</i>) gene clusters and discovered three species––<i>A. leporis, A. homomorphus, </i>and <i>A. hancockii</i>––had <i>eas</i> clusters indicative of the capacity to produce a lysergic acid amide. In culture, <i>A. leporis, A. homomorphus, </i>and <i>A. hancockii</i> produced lysergic acid amides, predominantly lysergic acid α-hydroxyethylamide (LAH). <i>Aspergillus leporis</i> and <i>A. homomorphus</i> produced high concentrations of LAH and secreted most of their ergot alkaloid yield into the culture medium. Phylogenetic analyses indicated genes encoding enzymes leading to the synthesis of lysergic acid were orthologous to those of the lysergic acid amide-producing Clavicipitaceae; however, genes to incorporate lysergic acid into an amide derivative evolved from different ancestral genes in the <i>Aspergillus</i> species. Our data demonstrate fungi outside the Clavicipitaceae produce lysergic acid amides and indicate the capacity to produce lysergic acid evolved once, but the ability to insert it into LAH evolved independently in <i>Aspergillus</i> species and the Clavicipitaceae. The LAH-producing <i>Aspergillus </i>species may be useful for study and production of these pharmaceutically important compounds.</p>

opencc-zeroSep 2021View details →
zenodo32/100

STROBE checklist for: Serum soluble angiotensin-converting enzyme 2 level and its potential association with the renin-angiotensin-aldosterone system in non-hypertensive covid-19 patients: an observational study

<p>Serum soluble angiotensin-converting enzyme 2 level and its potential association with the renin-angiotensin-aldosterone system in non-hypertensive covid-19 patients: an observational study</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

EMERGE 2016 Autochamber Sites Enzyme Assays

METHODS:<br>The activity of five hydrolytic enzymes β-d-glucosidase, β-d-xylosidase, N-acetyl-β-d-glucosaminidase, arylsulphatase, and phosphatase was assessed using a fluorometric enzyme assay following methods adapted from Saiya-Cork et al. 2002 and DeForest, 2009. Briefly, a soil slurry was prepared for each sample by blending 1 g of soil with 125 mL of sodium acetate buffer (50 mM; pH 6.2). The soil slurry was then transferred to a 96 well flat-bottom black microplate. Following, 4-methylumbelliferyl (MUB) standard solution and fluorescently linked enzyme substrates were added to the respective wells and the plates were incubated at 25°C for 45 min for β-d-glucosidase, N-acetyl-β-d-glucosaminidase, arylsulphatase, and phosphatase, and 30 min for β-d-xylosidase. Incubation times and substrate concentrations were chosen based on a V-max test performed to capture peak enzyme activity. A buffer only control column and control columns containing only soil slurry and standard were included so background fluorescence could be extracted from assay wells. Fluorescence was read in a BioTek Synergy HT microplate reader at a wavelength of 460 nm emission and 360 nm excitation. Final enzyme activity was reported as µmol activity g-1 dry soil h-1.<br><br>The oxidative enzyme activity of phenol oxidase was assessed with a colorimetric enzyme assay (DeForest, 2009). A soil slurry was prepared for each sample by blending 1 g of soil with 125 mL of sodium acetate buffer (50 mM; pH 6.2), and slurries were transferred to a 96 deep-well plate. A blank column containing only buffer was included in the plate, as well as controls containing only buffer and substrate. L-3,4,-dihydroxy phenylalanine (L-DOPA) was chosen as the substrate for measuring phenol oxidase activity. L-DOPA was added in a non- limiting quantity at 25mM concentration and plates were incubated at 25°C for 24 hours. Following incubation, the supernatant was transferred to a 96 well flat-bottom clear microplate, and absorbance was read in a BioTek Synergy HT microplate reader at 460 nm. Final activity was reported as µmol activity g-1 dry soil h-1.<br><br>DeForest, JL. 2009. The influence of time, storage temperature, and substrate age on potential soil enzyme activity in acidic forest soils using MUB-linked substrates and L DOPA. Soil Biol. Biochem. 41:1180-1186.<br>Saiya-Cork, KR, RL Sinsabaugh, DR Zak. 2002. The effects of long term nitrogen deposition on extracellular enzyme activity in an Acer saccharum forest soil. Soil Biol and Biochem 34: 1309-1315.<br><br>COLUMN DEFINITIONS:<br>ARYL = Arylsulfatase<br>BG = beta-glucosidase<br>NAG = N-acetylglucosidase<br>XYLO = Xylosidase<br>PHOS = Phosphatase<br>PhenOx= Phenol Oxidase<br><br>FUNDING:<br>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0010580 and DE-SC0016440.

opencc-by-4.0Jan 2023View details →
zenodo32/100

Supplementary data for: Pevonedistat, a Nedd8-activating enzyme inhibitor, in combination with ibrutinib in patients

<p>In a clinical trial to evaluate a NEDD8-activating enzyme inhibitor in treating a mantle cell lymphoma patient, peripheral blood mononuclear cells (PBMCs) were collected from the patient at baseline and after 3 and 24 hours after drug infusion. Then, single-cell RNA-seq was conducted on the collected PBMCs. The pre-processed data was saved as a Seurat object.</p>

opencc-by-4.0Mar 2023View details →
dryad32/100

Effects of mowing, spring precipitation, soil nutrients and enzymes on grassland productivity

<p>Little research has assessed how the timing and intensity of grazing might affect plant biomass, available nutrients, and soil extracellular enzyme activity (EEA), and how climate change might influence these responses. We tested the effect of two <span>spring precipitation variability </span>(rainfed control, -30% of ambient), two mowing intensity (moderate, severe), and two mowing season treatments (June, October) on plant and soil properties. We detected an interactive effect of precipitation, mowing intensity, and mowing season on plant biomass. When plots were mowed at a moderate intensity, water reductions had irregular effects on plant biomass depending on the mowing season. Plant biomass was also 11% greater in plots mowed at moderate than severe intensities. Most soil nutrients were unaffected by treatments, except for calcium. Soil EEA was unaffected by treatments; however, the activity of a phosphorus (P)-acquisition enzyme was ≥4 times greater than the activity of nitrogen (N)- and carbon-acquisition enzymes. A substantial amount (adjusted R2= 0.51) of plot-to-plot variation in plant biomass was explained by three soil properties, especially a N-acquisition enzyme and to a lesser degree by plant available P and soil pH. The grassland had a high degree of natural buffering capacity as most soil properties were resistant to shifts in 6-yr spring precipitation and 5-yr simulated grazing intensity and season. Grassland plant biomass varied by treatments and was seemingly limited by biogeochemical constraints, especially the prevalent need to mobilize P and a secondary need to acquire N as plant biomass increased.</p>

opencc-zeroMar 2023View details →
zenodo32/100

Supplementary material 1 from: Habash M, Alshakhshir S, Awwad S, Abu-Samak M (2023) The discovery of potential tumor necrosis factor alpha converting enzyme inhibitors via implementation of K Nearest Neighbor QSAR analysis. Pharmacia 70(2): 247-261. https://doi.org/10.3897/pharmacia.70.e96423

The discovery of potential tumor necrosis factor alpha converting enzyme inhibitors via implementation of K Nearest Neighbor QSAR analysis

opencc-zeroApr 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record