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opencc-by-4.0Feb 2024View details →

Data for: Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics

Dataset for Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics [<a href="https://scipost.org/SciPostPhysCore.7.3.049">SciPost Phys. Core 7, 049 (2024)</a>].

opencc-by-4.0Apr 2022View details →

Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018

In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated.  For each of the 246 stations sampled, we proceeded as follows. We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°. For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25). For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days. Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.  Furthermore, two different velocity fields were used, which are described as follows.  Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day. The following Eulerian diagnostics were calculated: <ul> <li> Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below) </li> <li> Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2) </li> <li> Divergence ([EulerDiverg], d-1): du/dx + dv/dy </li> <li> Vorticity ([Vorticity], d-1): dv/dx - du/dy </li> <li> Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy. </li> </ul> The following Lagrangian diagnostics were calculated: <ul> <li> Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time. </li> <li> Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25. </li> <li> Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling. </li> <li> Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero. </li> </ul>

opencc-by-4.0Jun 2022View details →

Dynamic FRET example videos related to "Mars, a molecule archive suite for reproducible analysis and reporting of single-molecule properties from bioimages"

Videos of dynamic switching between iso-I and iso-II conformations of a holiday junction at 50 mM Magnesium resulting in high and low FRET from Cy3 and Alexa647 labels positioned on the arms. Holiday junctions are surface immobilized through a biotin attachment and imaged using TIRF microscopy. The camera sensor is split using a dual view so that the acceptor emission is on the top and the donor emission is on the bottom. Videos from each position are provided as compressed zip files containing a sequence of tif files and associated metadata text file. Image sequences were collected using Micro-Manager 2.0 using ALEX or alternating laser excitation with alternating 637 and 532 pulses separated as two different channels. Beam profile images are provided for 637 and 532 excitation allowing for correction of the non-uniform beam profiles. The following 2D affine transformation matrix can be used to transform from the top acceptor emission region to the bottom donor emission region during processing. Affine 2D transformation from top to bottom: (m00, m01, m02, m10, m11, m12), (1.00276, 0.000208, 1.01236, 0.000267, 1.00312, 507.21025) A detailed image processing workflow for this dataset using Mars can be found under the example section at <a href="https://duderstadt-lab.github.io/mars-docs/">https://duderstadt-lab.github.io/mars-docs/</a> or directly at <a href="https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/">https://duderstadt-lab.github.io/mars-docs/examples/FRET_dynamic/</a>

opencc-by-4.0Apr 2021View details →

Connexin 46 and connexin 50 gap junction channel properties are shaped by structural and dynamic features of their N-terminal domains

Provided are reduced trajectories (.dcd) of the MD simulations -- each trajectory has 100 ps/frame with only protein and ion atoms remaining. Each set of trajectories are accompanied by a protein structure file (.psf) which is required to visualize the trajectories in VMD. Additionally, the z-trajectories of each intracellular ion (2 ps/frame) are provided in zipped files. To re-create the potentials of mean force (PMF) in Yue & Haddad et al., use the scripts provided with the paper (https://github.com/reichow-lab/Yue-Haddad_et-al.JPhysiol2021):   <pre><code class="language-bash">python3 GapJ_Analysis.py "Cx46_Ace_Produc-1_POT_*"</code></pre> <ul> <li>Choose a bin size in Å (3)</li> <li>Choose an output name (Cx46_Ace)</li> <li>Choose option (M)</li> <li>Choose time (ps) / frame (2)</li> <li>Choose column from file (1)</li> <li>Choose binmin/binmax (auto)</li> </ul>

opencc-by-4.0Jan 2021View details →

Unique dynamics and exocytosis properties of GABAergic synaptic vesicles revealed by three-dimensional single vesicle tracking

This data set includes x, y, and z trajectories of all GABAergic synaptic vesicles that we used for the study. These GABAergic synaptic vesicles in inhibitory presynaptic terminals of living primary hippocampal neurons were labeled by single quantum dots (QDs) conjugated with anti-VGAT antibody under electrical stimulation, and were tracked three-dimensionally by using a dual-focus imaging in real-time. Each trajectory data indicates x, y, and z positions (nanometer-scale) over time from the start of imaging to the moment of vesicle fusion. The electrical stimulation to the neurons was applied for 120 s, starting from 20 s.

opencc-by-4.0Nov 2024View details →

Raw data to "Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions"

<div> This directory contains the data used to generate the numerical results in the work "Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions [1]". To get an overview of the organization of the directory and a description of the data we recommend the README.md file. [1]: P. Adelhardt, A. Duft and K. P. Schmidt, Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions, <a href="https://arxiv.org/abs/2408.13145">arXiv:2408.13145</a>     </div>

Figure 3 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn

Figure 3. Dynamics of the soil arbuscular mycorrhizal fungal spore density within Desmodium triflorum coverage levels and seasons.

Figure 6 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn

Figure 6. Conceptual framework demonstrating possible mechanisms of soil arbuscular mycorrhizal fungi (AMF) during the spreading process of Desmodium triflorum in the Zoysia tenuifolia lawn. Numbers 1, 2, 3, and 4 indicate different spreading stages of the invasive plant D. triflorum. Corresponding mycorrhizal structures were shown as the four microscopic views. Light-green and medium-yellow circles indicate AM fungal spores predominantly produced by the root mycorrhizal structures of Z. tenuifolia and D. triflorum, respectively. Medium-green and dark-yellow lines indicate the life cycle of spores in Z. tenuifolia plants and in D. triflorum plants, respectively. The AM fungi might influence the spread of D. triflorum by the following steps: (1) the early stage of the lawn's development with only Z. tenuifolia growing but without D. triflorum present. This occurs at the very beginning of the lawn establishment, and the AM fungal spores that previously existed in the lawn soil first infected the fine roots of Z. tenuifolia and completed the life cycle on their own. (2) The early spreading stage of D. triflorum (level 1). The roots of the two plants come into contact with each other, inducing the external hyphae that originally grow closely on the Z. tenuifolia roots to infect the roots of D. triflorum. The difference between the mycorrhizal infections of the two host plants contributes to higher root mycorrhizal colonizations of D. triflorum compared with Z.tenuifolia. However, at this stage,D. triflorum is not as competitive as Z. tenuifolia in the lawn, although it has advantages in terms of mycorrhizal infections. Therefore, the soil AM fungal spores are still predominantly produced by the mycorrhizal structures of the AMF-infected Z. tenuifolia roots. (3) The intermediate spreading stage of D. triflorum (levels 2 and 3). Desmodium triflorum continues to spread in the lawn. The contact of the two plants becomes more frequent and further induces a much closer relationship between the AM infections of the two plants. The increased D. triflorum plants in the lawn and the advantage of D. triflorum in root mycorrhizal infections facilitate the contribution of the mycorrhizal structures of the D. triflorum roots to sporulation. Thus, in this stage, the soil AM fungal spores were produced by the mycorrhizal structures of both plants, thereby inducing insignificant correlations between the spore densities and the root colonizations of either Z. tenuifolia or D. triflorum. (4) The late spreading stage of D. triflorum (levels 4 and 5). Desmodium triflorum is dominant in the lawn.The large numbers of D. triflorum plants and the AM infection advantage of D. triflorum facilitate AMF sporulation in the soil, thereby inducing significant correlations between the spore densities and the root colonizations of D. triflorum. At the different spreading stages of D. triflorum, the soil AM fungal communities also change as a result of the changed contributions of the AMF-infected host plants to the sporulation.

Figure 5 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn

Figure 5. The relative abundance and community composition at the family (A) and species levels (B) of arbuscular mycorrhizal fungi (AMF) in soils of different Desmodium triflorum coverage levels.

Figure 2 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn

Figure 2. Dynamics of the total, hyphal, and vesicular colonizations of Zoysia tenuifolia and Desmodium triflorum among different D. triflorum coverage levels and seasons. "Season," "Coverage," and "Species" indicate ANOVA results of each indicator among seasons and D. triflorum coverage levels and between the two plants, respectively.

Figure 4 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn

Figure 4. Correlations among the root mycorrhizal colonizations, arbuscular mycorrhizal fungal spore densities ("AMF spore density"), and soil properties in different coverage levels of Desmodium triflorum. ZTC, ZHC, and ZVC in light-green circles indicate the total colonization (TC), hyphal colonization (HC), and vesicular colonization (VC) of Zoysia tenuifolia, respectively. DTC, DHC, and DVC in light-red circles indicate the TC, HC, and VC of D. triflorum, respectively. Green lines and green-colored numbers indicate significant correlations between the colonization indicators of Z. tenuifolia and corresponding correlation coefficients, respectively. Red lines and red-colored numbers indicate significant correlations between the colonization indicators of Z. tenuifolia and corresponding correlation coefficients, respectively. Dark-green double arrows and dark-green numbers indicate the correlations between the colonizations of Z. tenuifolia and those of D. triflorum and corresponding correlation coefficients, respectively. Light-blue double arrows and light-blue numbers indicate the correlations between the spore densities and soil properties/root colonizations and corresponding correlation coefficients,respectively. Darkyellow double arrows and dark-yellow numbers indicate the correlations between the soil properties and root colonizations and corresponding correlation coefficients, respectively. Correlation is significant at: *P <0.05; **P <0.01; ***P <0.001. The minus sign indicates a negative correlation. Insignificant correlations are not shown.

Figure 1 in Dynamics of arbuscular mycorrhizal fungi in relation to root colonization, spore density, and soil properties among different spreading stages of the exotic plant threeflower beggarweed (Desmodium triflorum) in a ZoysiO tenuifoliO lawn

Figure 1. Dynamics of the soil physiochemical properties (average ± SE, n = 5) within different Desmodium triflorum coverage levels and seasons. "Season" and "Coverage" indicate ANOVA results of each indicator among seasons and D. triflorum coverage levels, respectively. Level 1, level 2, level 3, level 4, and level 5 indicate the coverage levels of D. triflorum in the Zoysia tenuifolia lawn, respectively, in this and all following figures.

opencc-by-4.0May 2019View details →

Megakaryocyte volume modulates bone marrow niche properties and cell migration dynamics

Supplementary videos showing raw time and z-stacks as well a final, processed result for Neutrophil tracking in naive and platelet depleted mouse. Matlab scripts to run simulation of megakaryocytes, neutrophils and hematopoetic stem cell in a vessel environment. Ilastik training data set used in segmentation of bone and bone marrow.

opencc-by-4.0Feb 2021View details →

Tailoring the optical and dynamic properties of iminothioindoxyl photoswitches through acidochromism

Multi-responsive functional molecules are key for obtaining user-defined control of the properties and functions of chemical and biological systems. In this respect, pH-responsive photochromes, whose switching can be directed with light and acid–base equilibria, have emerged as highly attractive molecular units. The challenge in their design comes from the need to accommodate application-defined boundary conditions for both light- and protonation-responsivity. Here we combine time-resolved spectroscopic studies, on time scales ranging from femtoseconds to seconds, with density functional theory (DFT) calculations to elucidate and apply the acidochromism of a recently designed iminothioindoxyl (ITI) photoswitch. We show that protonation of the thermally stable Z isomer leads to a strong batochromically-shifted absorption band, allowing for fast isomerization to the metastable E isomer with light in the 500–600 nm region. Theoretical studies of the reaction mechanism reveal the crucial role of the acid–base equilibrium which controls the populations of the protonated and neutral forms of the E isomer. Since the former is thermally stable, while the latter re-isomerizes on a millisecond time scale, we are able to modulate the half-life of ITIs over three orders of magnitude by shifting this equilibrium. Finally, stable bidirectional switching of protonated ITI with green and red light is demonstrated with a half-life in the range of tens of seconds. Altogether, we designed a new type of multi-responsive molecular switch in which protonation red-shifts the activation wavelength by over 100 nm and enables efficient tuning of the half-life in the millisecond–second range.   Article information: <a href="https://doi.org/10.1039/D0SC07000A">https://doi.org/10.1039/D0SC07000A</a>

opencc-by-4.0Jun 2023View details →

Biochemical networks with simulation-based estimations of dynamical properties

This datasets collection was first introduced in the article:  <a href="https://academic.oup.com/bioinformatics/article/39/11/btad678/7407341" target="_blank" rel="noopener">Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs</a>. (Bioinformatics 2023)   The collection contains three datasets that contain information about three dynamical properties computed on a set of 483 biochemical pathways downloaded from the BioModels database. The three dynamical properties are: <ul> <li>robustness</li> <li>sensitivity</li> <li>monotonicity</li> </ul> The files are organized as follows: <ol> <li>The `pathways` directory contains 483 files in .dot format for each biochemical pathway downloaded from the BioModels database (May 2021), represented in Petri net format (see <a href="https://doi.org/10.5220/0008964700320043">this article</a> for the exact definition). The file name is the ID of the pathway in the BioModels database.</li> <li>The other folders contain one .csv file for each property. A single .csv file contains 4 columns: <ol> <li>`PathwayID`: the ID of the Pathway in the BioModels database</li> <li>`Input`: the input molecular species on which the property has been assessed</li> <li>`Output`: the output molecular species on which the property has been assessed</li> <li>`Property`: the value of the property assessed with numerical simulations on the pathway for that particular input/output species pair.</li> </ol> </li> <li>The `loader.py` file is an optional script that allows to use the data in python. The script requires that the libraries `networkx`, `pandas`, and `pydot` are installed in the target machine.</li> </ol>

opencc-by-4.0Nov 2020View details →

Effect of Strain Amplitude on Static and Dynamic Mechanical Properties of Tight Sedimentary Rocks: An Experimental Study

We perform increasing-amplitude triaxial unload cycling tests on three tight sedimentary rocks to investigate the strain-dependent mechanical properties.

opencc-by-4.0Jan 2022View details →

Data associated to the article "Effects of fluoride salt addition to the physico-chemical properties of the MgCl2-NaCl-KCl heat transfer fluid : a molecular dynamics study"

Contains input file and data used to generate the figures of the article: Effects of fluoride salt addition to the physico-chemical properties of the MgCl2-NaCl-KCl heat transfer fluid : a molecular dynamics study Weiguang Zhou, Yanping Zhang, Mathieu Salanne https://chemrxiv.org/engage/chemrxiv/article-details/618e903a2bf8a950c7d98e5d The files data.inpt and runtime.inpt are used to simulate the system using the software MetalWalls The files MgNaKCl.txt, MgNaKClF01.txt, MgNaKClF05.txt, MgNaKClF10.txt, MgNaKClF20.txt contain the computed densities, viscosities and thermal conductivities at various temperatures for several compositions (provided in the header of the files)

opencc-by-4.0Oct 2021View details →

High-Throughput Screening of Tribological Properties of Monolayer Films using Molecular Dynamics and Machine Learning: Supplemental Repository

Supplemental repository for the "High-Throughput Screening of Tribological Properties of Monolayer Films using Molecular Dynamics and Machine Learning" article. Contains calculated tribological properties of dual-monolayer systems from Molecular Dynamics (MD) simulation and Machine Learning (ML).