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11,506 results for “Dynamics”
Data from: Microclimate predicts within-season distribution dynamics of montane forest birds
Aim Climate changes are anticipated to have pervasive negative effects on biodiversity and are expected to necessitate widespread range shifts or contractions. Such projections are based upon the assumptions that (1) species respond primarily to broad-scale climatic regimes, or (2) that variation in climate at fine spatial scales is less relevant at coarse spatial scales. However, in montane forest landscapes, high degrees of microclimate variability could influence occupancy dynamics and distributions of forest species. Using high-resolution bird survey and under-canopy air temperature data, we tested the hypothesis that the high vagility of most forest bird species combined with the heterogeneous thermal regime of mountain landscapes would enable them to adjust initial settlement decisions to track their thermal niches. Location Western Cascade Mountains, Oregon, USA. Methods We used dynamic occupancy models to test the degree to which microclimate affects the distribution patterns of forest birds in a heterogeneous mountain environment. In all models we statistically accounted for vegetation structure, vegetation composition and potential biases due to imperfect detection of birds. We generated spatial predictions of forest bird distributions in relation to microclimate and vegetation structure. Results Fine-scale temperature metrics were strong predictors of bird distributions; effects of temperature on within-season occupancy dynamics were as large or larger (1–1.7 times) than vegetation effects. Most species (86.7%) exhibited apparent within-season occupancy dynamics. However, species were almost as likely to be warm associated (i.e., apparent settlement at warmer sites and/or vacancy at cooler sites; 53.3% of species) as cool associated (i.e., apparent settlement at cooler sites and/or vacancy at warmer sites; 46.7% of species), suggesting that microclimate preferences are species specific. Main conclusions High-resolution temperature data increase the quality of predictions about avian distribution dynamics and should be included in efforts to project future distributions. We hypothesize that microclimate-associated distribution patterns may reflect species' potential for behavioural buffering from climate change in montane forest environments.
Data from: Climate change and functional traits affect population dynamics of a long-lived seabird
1. Recent studies unravelled the effect of climate changes on populations through their impact on functional traits and demographic rates in terrestrial and freshwater ecosystems, but such understanding in marine ecosystems remains incomplete. 2. Here, we evaluate the impact of the combined effects of climate and functional traits on population dynamics of a long-lived migratory seabird breeding in the southern ocean: the black-browed albatross (Thalassarche melanophris, BBA). We address the following prospective question: ''Of all the changes in the climate and functional traits, which would produce the biggest impact on the BBA population growth rate?'' 3. We develop a structured matrix population model that includes the effect of climate and functional traits on the complete BBA life cycle. A detailed sensitivity analysis is conducted to understand the main pathway by which climate and functional trait changes affect the population growth rate. 4. The population growth rate of BBA is driven by the combined effects of climate over various seasons and multiple functional traits with carry-over effects across seasons on demographic processes. Changes in Sea Surface Temperature (SST) during late winter cause the biggest changes in the population growth rate, through their effect on juvenile survival. Adults appeared to respond to changes in winter climate conditions by adapting their migratory schedule rather than by modifying their at-sea foraging activity. However, the sensitivity of the population growth rate to SST affecting BBA migratory schedule is small. BBA foraging activity during the pre-breeding period has the biggest impact on population growth rate among functional traits. Finally, changes in SST during the breeding season have little effect on the population growth rate. 5. These results highlight the importance of early life histories and carry-over effects of climate and functional traits on demographic rates across multiple seasons in population response to climate change. Robust conclusions about the roles of various phases of the life cycle and functional traits in population response to climate change rely on an understanding of the relationships of traits to demographic rates across the complete life cycle.
Molecular dynamics derived side chain order parameters for Asp, Glu, Asn, and Gln residues in ribonucleases H, and molecular dynamics trajectories for E. coli ribonuclease H
<p>This dataset contains tab-delimited text files reporting the complete calculated S2 side chain order parameters for carboxyl- and carbonyl-containing residues (Asp, Glu, Asn, Gln) in all RNase H homologs studied, as well as complete simulation trajectory data for the E. coli apo, site A, and site B simulations described in the text. We hope that easy access to the complete trajectory data for the experimentally best-characterized homolog will facilitate future comparison between simulation and experiments.</p> <p>order_parameters_magnesium.dat contains the calculated S2 side chain order parameters for carboxyl- and carbonyl-containing residues in each of the three E. coli trajectories supplied in this dataset. These data are sufficient to reproduce Figure 6.</p> <p>2RN2.apo.pdb, 2RN2.MG.siteA.pdb, and 2RN2.MG.siteB.pdb define the equilibrated structure from which the E. coli apo, site A, and site B trajectories were initiated. All protein atoms, including hydrogens, are included, as well as Mg ions. Explicit waters and chloride counterions have been removed.</p> <p>2RN2.apo.dcd, 2RN2.MG.siteA.dcd, and 2RN2.MG.siteB.dcd contain 100ns trajectories initiated from the starting structures in the corresponding pdb files. Frames were written every 4.5ps. Trajectories have been aligned to the initial structure of the apo trajectory to facilitate visualization.</p> <p>The remaining *.S2.CO.dat files contain the calculated S2 side chain order parameters for all apo RNases H simulated in this study. Each file is named for the PDB ID of its corresponding structure (see Table 1). These data are sufficient to reproduce Figure 8 (which reports on only the conserved active-site residues).</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 70 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Molecular dynamics simulations of the temperature-induced unfolding of crambin follow the Arrhenius equation.
<p>These are the output files from the Gromacs simulations of the temperature induced unfolding of crambin.</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 70 wt% C12E5, T=320K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 70 wt% C12E5, T=333K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Molecular dynamics simulations of the temperature-induced unfolding of crambin follow the Arrhenius equation.
<p>These are the SPSS files for the statistical analysis of the temperature induced unfolding of crambin.</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 60 wt% C12E5, T=333K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 60 wt% C12E5, T=320K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 60 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Dataset of paper: Supervised and Dynamic Neuro-Fuzzy Systems to Classify Physiological Responses in Robot-Assisted Neurorehabilitation (PLOS One)
<p>The data set contains number of user, user's physiological signals (Pulse, SCL, SCR, Respiration rate, Skin temperature), Label, Difficulty level from relax to stress. Label is codified from 1 to 5 corresponding to the Difficulty level.</p>
NDVI datasets related with the article "A comparative study of cross-product NDVI dynamics in the Kilimanjaro region - a matter of sensor, degradation calibration, and significance"
<p>AHVRR GIMMS and MODIS NDVI input datasets and main results on seasonal and long-term NDVI dynamics related with the manuscript "A comparative study of cross-product NDVI dynamics in the Kilimanjaro region - a matter of sensor, degradation calibration, and significance" recently published in Remote Sensing 8(2), 159, doi: 10.3390/rs8020159.</p>
Learning stochastic process-based models of dynamical systems from knowledge and data - Libraries, incomplete models and data
<p>The archive contains all libraries of domain knowledge, the incomplete models and the data used in the experiments described in the manuscript titled "Learning stochastic process-based models of dynamical systems from knowledge and data" pubilshed in BMC Systems Biology</p>
Molecular dynamics simulation trajectory of a cationic lipid bilayer: 6/94 mol% DMTAP/DMPC
<p><strong>System: </strong>Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 6 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 94 mol-%) lipids in water.</p> <p><strong>Number of DMPC: </strong>120.<br> <strong>Number of DMTAP:</strong> 8.<br> <strong>Number of Cl<sup>-</sup>-ions:</strong> 8.<br> <strong>Number of waters:</strong> 5099.</p> <p><strong>Lipid model:</strong> 'Berger' united-atom [<em>Biophys. J.</em> <strong>72</strong> 2002 (1997)] with DMTAP modification by Gurtovenko et al. [<em>Biophys. J. </em><strong>86</strong> 3461 (2004)].<br> <strong>Water model:</strong> SPC [In <em>Intermolecular Forces,</em> ed. Pullman. 331 (1981)].</p> <p><strong>Simulation engine:</strong> Gromacs 3.x [www.gromacs.org]</p> <p><strong>Trajectory length:</strong> 109 ns.<br> <strong>Previously equilibrated for:</strong> 32 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with lipids and water coupled separately at T = 323 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with xy and z coupled separately at p = 1.0 bar.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993); <em>J. Chem. Phys.</em> <strong>103</strong> 8577 (1995)], real-space cutoff at 1.0 nm.<br> <strong>Van der Waals:</strong> Truncated at 1.0 nm.</p> <p><strong>Constraints: </strong>Covalent bond lengths in lipids using LINCS [<em>J. Comput. Chem.</em> <strong>18</strong> 1463 (1997)], in water using SETTLE [J. Comput. Chem. <strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>[1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". <em>J. Phys. Chem. B</em> <strong>113</strong> 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.</p>
Molecular dynamics simulation trajectory of a fully hydrated DMPC lipid bilayer
<p><strong>System: </strong>DMPC (dimyristoylphosphatidylcholine) bilayer in water.</p> <p><strong>Number of lipids: </strong>128.<br> <strong>Number of waters:</strong> 5097.</p> <p><strong>Lipid model:</strong> 'Berger' united-atom [<em>Biophys. J.</em> <strong>72</strong> 2002 (1997)].<br> <strong>Water model:</strong> SPC [In <em>Intermolecular Forces,</em> ed. Pullman. 331 (1981)].</p> <p><strong>Simulation engine:</strong> Gromacs 3.x [www.gromacs.org]</p> <p><strong>Trajectory length:</strong> 110 ns.<br> <strong>Previously equilibrated for:</strong> 20 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with lipids and water coupled separately at T = 323 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with xy and z coupled separately at p = 1.0 bar.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993); <em>J. Chem. Phys.</em> <strong>103</strong> 8577 (1995)], real-space cutoff at 1.0 nm.<br> <strong>Van der Waals:</strong> Truncated at 1.0 nm.</p> <p><strong>Constraints: </strong>Covalent bond lengths in lipids using LINCS [<em>J. Comput. Chem.</em> <strong>18</strong> 1463 (1997)], in water using SETTLE [J. Comput. Chem. <strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>[1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". <em>J. Phys. Chem. B</em> <strong>113</strong> 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.</p>
Molecular dynamics simulation trajectory of a cationic lipid bilayer: 50/50 mol% DMTAP/DMPC in 0.5 M NaCl
<p><strong>System: </strong>Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 50 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 50 mol-%) lipids in 0.5 M NaCl solution.</p> <p><strong>Number of DMPC:</strong> 64.<br> <strong>Number of DMTAP:</strong> 64.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 48.<br> <strong>Number of Cl<sup>-</sup>-ions:</strong> 112.<br> <strong>Number of waters:</strong> 5240.</p> <p><strong>Lipid model:</strong> 'Berger' united-atom [<em>Biophys. J.</em> <strong>72</strong> 2002 (1997)] with DMTAP modification by Gurtovenko et al. [<em>Biophys. J. </em><strong>86</strong> 3461 (2004)].<br> <strong>Water model:</strong> SPC [In <em>Intermolecular Forces,</em> ed. Pullman. 331 (1981)].</p> <p><strong>Simulation engine:</strong> Gromacs 3.x [www.gromacs.org]</p> <p><strong>Trajectory length:</strong> 179 ns.<br> <strong>Previously equilibrated for:</strong> 21 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with lipids and water coupled separately at T = 323 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with xy and z coupled separately at p = 1.0 bar.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993); <em>J. Chem. Phys.</em> <strong>103</strong> 8577 (1995)], real-space cutoff at 1.0 nm.<br> <strong>Van der Waals:</strong> Truncated at 1.0 nm.</p> <p><strong>Constraints: </strong>Covalent bond lengths in lipids using LINCS [<em>J. Comput. Chem.</em> <strong>18</strong> 1463 (1997)], in water using SETTLE [J. Comput. Chem. <strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>[1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". <em>J. Phys. Chem. B</em> <strong>113</strong> 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.</p>
Molecular dynamics simulation trajectory of a cationic lipid bilayer: 50/50 mol% DMTAP/DMPC in 1.0 M NaCl
<p><strong>System: </strong>Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 50 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 50 mol-%) lipids in 1.0 M NaCl solution.</p> <p><strong>Number of DMPC:</strong> 64.<br> <strong>Number of DMTAP:</strong> 64.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 94.<br> <strong>Number of Cl<sup>-</sup>-ions:</strong> 158.<br> <strong>Number of waters:</strong> 5148.</p> <p><strong>Lipid model:</strong> 'Berger' united-atom [<em>Biophys. J.</em> <strong>72</strong> 2002 (1997)] with DMTAP modification by Gurtovenko et al. [<em>Biophys. J. </em><strong>86</strong> 3461 (2004)].<br> <strong>Water model:</strong> SPC [In <em>Intermolecular Forces,</em> ed. Pullman. 331 (1981)].</p> <p><strong>Simulation engine:</strong> Gromacs 3.x [www.gromacs.org]</p> <p><strong>Trajectory length:</strong> 170 ns.<br> <strong>Previously equilibrated for:</strong> 21 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with lipids and water coupled separately at T = 323 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with xy and z coupled separately at p = 1.0 bar.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993); <em>J. Chem. Phys.</em> <strong>103</strong> 8577 (1995)], real-space cutoff at 1.0 nm.<br> <strong>Van der Waals:</strong> Truncated at 1.0 nm.</p> <p><strong>Constraints: </strong>Covalent bond lengths in lipids using LINCS [<em>J. Comput. Chem.</em> <strong>18</strong> 1463 (1997)], in water using SETTLE [J. Comput. Chem. <strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>[1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". <em>J. Phys. Chem. B</em> <strong>113</strong> 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.</p>
Molecular dynamics simulation trajectory of a cationic lipid bilayer: 6/94 mol% DMTAP/DMPC in 1.0 M NaCl
<p><strong>System: </strong>Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 6 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 94 mol-%) lipids in 1.0 M NaCl solution.</p> <p><strong>Number of DMPC:</strong> 120.<br> <strong>Number of DMTAP:</strong> 8.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 89.<br> <strong>Number of Cl<sup>-</sup>-ions:</strong> 97.<br> <strong>Number of waters:</strong> 4921.</p> <p><strong>Lipid model:</strong> 'Berger' united-atom [<em>Biophys. J.</em> <strong>72</strong> 2002 (1997)] with DMTAP modification by Gurtovenko et al. [<em>Biophys. J. </em><strong>86</strong> 3461 (2004)].<br> <strong>Water model:</strong> SPC [In <em>Intermolecular Forces,</em> ed. Pullman. 331 (1981)].</p> <p><strong>Simulation engine:</strong> Gromacs 3.x [www.gromacs.org]</p> <p><strong>Trajectory length:</strong> 230 ns.<br> <strong>Previously equilibrated for:</strong> 12 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with lipids and water coupled separately at T = 323 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with xy and z coupled separately at p = 1.0 bar.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993); <em>J. Chem. Phys.</em> <strong>103</strong> 8577 (1995)], real-space cutoff at 1.0 nm.<br> <strong>Van der Waals:</strong> Truncated at 1.0 nm.</p> <p><strong>Constraints: </strong>Covalent bond lengths in lipids using LINCS [<em>J. Comput. Chem.</em> <strong>18</strong> 1463 (1997)], in water using SETTLE [J. Comput. Chem. <strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>[1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". <em>J. Phys. Chem. B</em> <strong>113</strong> 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.</p>
Molecular dynamics simulation trajectory of a cationic lipid bilayer: 75/25 mol% DMTAP/DMPC in 1.0 M NaCl
<p><strong>System: </strong>Symmetric bilayer of cationic DMTAP (dimyristoyltrimethylammoniumpropane, 75 mol-%) and zwitterionic DMPC (dimyristoylphosphatidylcholine, 25 mol-%) lipids in 1.0 M NaCl solution.</p> <p><strong>Number of DMPC:</strong> 32.<br> <strong>Number of DMTAP:</strong> 96.<br> <strong>Number of Na<sup>+</sup>-ions:</strong> 96.<br> <strong>Number of Cl<sup>-</sup>-ions:</strong> 192.<br> <strong>Number of waters:</strong> 5304.</p> <p><strong>Lipid model:</strong> 'Berger' united-atom [<em>Biophys. J.</em> <strong>72</strong> 2002 (1997)] with DMTAP modification by Gurtovenko et al. [<em>Biophys. J. </em><strong>86</strong> 3461 (2004)].<br> <strong>Water model:</strong> SPC [In <em>Intermolecular Forces,</em> ed. Pullman. 331 (1981)].</p> <p><strong>Simulation engine:</strong> Gromacs 3.x [www.gromacs.org]</p> <p><strong>Trajectory length:</strong> 110 ns.<br> <strong>Previously equilibrated for:</strong> 11 ns.<br> <strong>Sampling rate:</strong> every 10 ps.</p> <p><strong>Time integration step:</strong> 2 fs.</p> <p><strong>Thermodynamic ensemble:</strong> NpT. <br> <strong>Temperature coupling:</strong> 'Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with lipids and water coupled separately at T = 323 K.<br> <strong>Pressure coupling: '</strong>Berendsen' [<em>J. Chem. Phys.</em> <strong>81</strong> 3684 (1984)] with xy and z coupled separately at p = 1.0 bar.</p> <p><strong>Electrostatics: </strong>PME [<em>J. Chem. Phys.</em> <strong>98</strong> 10089 (1993); <em>J. Chem. Phys.</em> <strong>103</strong> 8577 (1995)], real-space cutoff at 1.0 nm.<br> <strong>Van der Waals:</strong> Truncated at 1.0 nm.</p> <p><strong>Constraints: </strong>Covalent bond lengths in lipids using LINCS [<em>J. Comput. Chem.</em> <strong>18</strong> 1463 (1997)], in water using SETTLE [J. Comput. Chem. <strong>13</strong> 952 (1992)].</p> <p><strong>Used in publications: </strong>[1] Markus S. Miettinen, Andrey A. Gurtovenko, Ilpo Vattulainen, and Mikko Karttunen: "Ion Dynamics in Cationic Lipid Bilayer Systems in Saline Solutions". <em>J. Phys. Chem. B</em> <strong>113</strong> 9226 (2009). DOI: 10.1021/jp810233q. [2] Markus S. Miettinen: "Computational Modeling of Cationic Lipid Bilayers in Saline Solutions". PhD Thesis. Aalto University School of Science and Technology, Helsinki, Finland. (2010). ISBN 978-952-60-3194-1.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.