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34 results for “molecular clusters”
Dataset of "Molecular dynamics of evaporative cooling of water clusters"
<p>The cooling of water clusters through evaporation into a vacuum is studied using classical molecular dynamics with the SPC water model, and the results are compared with semimacroscopic theory. A model based on the Hertz–Knudsen equation underestimates the cooling rates. A modified approach, which accounts for the Kelvin equation, provides better results. While the rotational temperature of the clusters is in equilibrium with their internal temperature, the translational temperature of the clusters “as individual particles” remains unchanged.</p>
Simulations from "Using Molecular Gas Observations to Guide Initial Conditions for Star Cluster Simulations"
<p>This dataset contains the simulation results from the article "Using Molecular Gas Observations to Guide Initial Conditions for Star Cluster Simulations" (submitted to MNRAS).</p> <p><br> The data is grouped by simulation and by particle type (gas, sinks and stars). Gas is uploaded with one snapshot per 0.05 Myr, sinks and stars with one snapshot per 0.01 Myr. The data is stored in AMUSE data format, which uses hdf5.</p>
Molecular Models and Wave Function Definitions for Models A-G of the [2Fe]F Cluster in FeFe-hydrogenase Maturase Enzyme HydF
<p>The dataset contains all relevant atomic positional coordinates for 2Fe-cluster models, and electronic wave function data (using formatted Gaussian checkpoint files) as described in the related publication (see citation below).</p> <p>The version 2.0 contains additional models for [2Fe-2S] cluster linked [2Fe]F constructs.</p> <p>The top folder contains "analysis.xlsx" electronic spreadsheet that summarizes all the numerical results for absolute and relative electronic energy values, internal coordinates, calculated and scaled vibrational frequencies for diatomic stretching modes. The details of developing scaled quantum forcefields as a function of level of theory and model composition are also given.<br> The schematic structural definitions are given in the "models.pdf" file and keys for abbreviations are provided in "symbols.txt" file.<br> </p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: Calibration Data
<p>Calibration data accompanying our work, "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data A
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part A</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data C
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part C.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data B
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part B.</p>
Simulation data on the growth of atmospheric molecular clusters and particles
<p>This data set contains output data from cluster population simulations performed with Atmospheric Cluster Dynamics Code (ACDC) model, which simulates the formation of clusters from atmospheric vapors and the growth of these clusters by further molecular and cluster-cluster collisions. The data can be used for investigating the formation and growth of atmospheric particles from inorganic and organic vapors.</p> <p>The data is output of a computational process model, and hence does not represent a specific time period or location. Simulation sets are calculated for a one or two-component system containing a quasi-unary inorganic compound representing a mixture of sulfuric acid and ammonia (SA) and/or oxidized organic vapors corresponding to a low volatility organic compound (LVOC) and an extremely-low volatility organic compound (ELVOC). The external conditions in the simulations correspond to those in the CLOUD (Cosmics Leaving Outdoor Droplets) chamber at temperature of 5 C°.</p> <p>Data are provided for 14 simulations.</p> <p><strong>References</strong></p> <p>Kontkanen J, Stolzenburg D, Olenius T, Yan C, Dada L, Ahonen L, Simon M, Lehtipalo K, Riipinen I (2022) What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?. Environ. sci. Atmos. <a href="https://doi:10.1039/d1ea00103e">https://doi:10.1039/d1ea00103e</a> </p> <p>Olenius T, Riipinen I (2017) Molecular-resolution simulations of new particle formation: Evaluation of common assumptions made in describing nucleation in aerosol dynamics models. Aerosol Sci. Tech. 51:397 – 408. <a href="https://doi.org/10.1080/02786826.2016.1262530">https://doi.org/10.1080/02786826.2016.1262530</a></p> <p>Olenius T, Atmospheric Cluster Dynamics Code. <a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a> </p> <p>McGrath MJ et al. (2012) Atmospheric Cluster Dynamics Code: a flexible method for solution of the birth-death equations. Atmos. Chem. Phys. 12:2345 – 2355. <a href="https://doi.org/10.5194/acp-12-2345-2012">https://doi.org/10.5194/acp-12-2345-2012</a></p> <p><strong>Data description</strong></p> <p>The data is in the form of text files. The provided data files (total compressed size ~10GB) correspond to simulation output from the ACDC model. Simulation sets are shown in the table below and further described in Kontkanen et al. (2022). For the interpretation of the model output, the interested user is referred to the manual of ACDC model (<a href="https://github.com/tolenius/ACDC">https://github.com/tolenius/ACDC</a>). </p> <table align="left"> <tbody> <tr> <td> <p>Simulation set</p> </td> <td> <p>Model compounds</p> </td> <td> <p>Vapor concentrations (cm<sup>-3</sup>)</p> </td> <td> <p>Method to retrieve evaporation rates</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>SA</p> </td> <td> <p><em>C</em><sub>SA </sub>= 8.0*10<sup>6</sup>, 2.0*10<sup>7</sup>, 4.7*10<sup>7</sup>, 1.1*10<sup>8</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>SA</p> </td> <td> <p><em>C</em><sub>SA </sub>= 2.0*10<sup>7</sup>, 4.7*10<sup>7</sup>, 1.1*10<sup>8</sup></p> </td> <td> <p>QC data and Kelvin eq.<br> <em>(non-classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>LVOC</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 5.0*10<sup>7</sup>, 1*10<sup>8</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>LVOC,<br> ELVOC</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 5.0*10<sup>7</sup>, 1*10<sup>8</sup><br> <em>C</em><sub>ELVOC </sub>= 1.0 *10<sup>7</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>LVOC,<br> SA</p> </td> <td> <p><em>C</em><sub>LVOC </sub>= 2.0*10<sup>7</sup>, 5.0*10<sup>7</sup>, 1*10<sup>8</sup><br> <em>C</em><sub>SA </sub>= 8.0*10<sup>6</sup></p> </td> <td> <p>Kelvin eq.<br> <em>(classical evaporation rates)</em></p> </td> </tr> </tbody> </table> <p> </p>
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West) in Bumps on the back: An unusual morphology in phylogenetically distinct Peridinium aff. cinctum (= Peridinium tuberosum; Peridiniales, Dinophyceae)
◂Fig. 6 A molecular phylogeny of 56 systematically representative Peridiniaceae, including 42 accessions assignable to P. cinctum from various geographic regions. Maximum likelihood tree (– ln = 21,884.93), as inferred from a rRNA nucleotide alignment (1137 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (CZE Czech Republic, E East, GER Germany, HET Heterocapsaceae, N North, PPE Protoperidiniaceae, POL Poland, rbn ribotype n, S South, SWE Sweden, UKR Ukraine, W West)
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae) in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 4 A molecular tree of 51 systematically representative Peridiniaceae, including all 28 accessions assignable to P. volzii. Maximum Likelihood tree (–ln = 22,017.62), as inferred from a rRNA nucleotide alignment (1,129 parsimony-informative sites) and with strain number information. Numbers on branches are ML bootstrap (above) and Bayesian support values (below) for the clusters (asterisks indicate maximal support values, values under 50 and 0.90, respectively, are not shown). Clades are indicated (abbreviations: HET, Heterocapsaceae; PPE, Protoperidiniaceae)
Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies
<p><span>Clustering approaches are pivotal to handle the many sequence variants obtained in DNA metabarcoding datasets, therefore they have become a key step of metabarcoding analysis pipelines. Clustering often relies on a sequence similarity threshold to gather sequences in Molecular Operational Taxonomic Units (MOTUs), each of which ideally representing a homogeneous taxonomic entity, e.g. a species or a genus. However, the choice of the clustering threshold is rarely justified, and its impact on MOTU over-splitting or over-merging even less tested. Here, we evaluated clustering threshold values for several metabarcoding markers under different criteria: limitation of MOTU over-merging, limitation of MOTU over-splitting, and trade-off between over-merging and over-splitting. We extracted sequences from a public database for nine markers, ranging from generalist markers targeting Bacteria or Eukaryota, to more specific markers targeting a class or a subclass (e.g. Insecta, Oligochaeta). Based on the distributions of pairwise sequence similarities within species and within genera, and on the rates of over-splitting and over-merging across different clustering thresholds, we were able to propose threshold values minimizing the risk of over-splitting, that of over-merging, or offering a trade-off between the two risks. For generalist markers, high similarity thresholds (0.96-0.99) are generally appropriate, while more specific markers require lower values (0.85-0.96). These results do not support the use of a fixed clustering threshold. Instead, we advocate a careful examination of the most appropriate threshold based on the research objectives, the potential costs of over-splitting and over-merging, and the features of the studied markers.</span></p>
Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies
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Data presented in figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters and potential precursor molecules present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)"
<p>This dataset includes the data shown in the figures of "Measurement Report: Insights into the chemical composition and origin of molecular clusters present in the free troposphere over the Southern Indian Ocean: observations from the Maïdo observatory (2150 m a.s.l., Reunion Island)". Read me files containing information on the reported data can be found in the different folders. </p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 35 Binding Site Data
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 35 binding sites.</p>
Influence of temperature on the molecular composition of ions and charged clusters during pure biogenic nucleation
<p>Data of Figures in manuscript:</p> <p>Influence of temperature on the molecular composition of ions and charged clusters during pure biogenic nucleation.</p> <p>Frege et al., Atmos. Chem. Phys. 18, 65–79, 2018</p> <p>https://doi.org/10.5194/acp-18-65-2018</p> <p> </p>
Supplementary data for Cariou et al (2020, Molecular Ecology Resources, "How consistent is RAD-seq divergence with DNA-barcode based clustering in insects?")
<p>This dataset accompanies a paper by Cariou et al, to be published in Molecular Ecology Resources, where we assessed in 92 insect species if the genetic clustering of specimens into species like units, on the basis of mitochondrial DNA, was consistent with genome wide divergence, as estimated by RAD-seq data. The present repository includes: (1) a detailed description of the bioinformatic analysis indicating which programs were used, together with parameter values, (2) the raw RAD-seq data following demultiplexing, (3) the consensus sequences of all RAD loci for all specimens, and (4) large tables indicating genetic distances at all RAD loci for all species.</p>
Experimental microwave spectroscopic and quantum-chemical investigation of the HCl(H2O)n (n=1-5,7) molecular clusters
<p>In this dataset, experimental microwave rotational spectroscopy data and corresponding theoretical calculations for the HCl(H2O)n (n=1-5,7) clusters are stored. </p>
Collision-induced dissociation of protonated uracil water clusters probed by molecular dynamics simulations
<p>This dataset contains the input and output files associated to the work of "Collision-induced dissociation of protonated uracil water clusters probed by molecular dynamics simulations"</p> <p> </p> <p> </p>
Figure data of "Measurement report: Molecular-level investigation of atmospheric cluster ions at the tropical high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes"
<p>This dataset involves the data that is used for the figures in "Measurement report: Molecular-level investigation of atmospheric cluster ions at the tropical high-altitude research station Chacaltaya (5240 m a.s.l.) in the Bolivian Andes".</p>
Explaining the luminosity spread in young clusters: proto and pre-main sequence stellar evolution in a molecular cloud environment
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2018MNRAS.474.1176J">Jensen & Haugbølle (2018)</a>. MESA version 8845.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx2844">10.1093/mnras/stx2844</a></p>
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