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490 results for “Propagation”
Controlling propagation velocity in Al/Ni reactive multilayer systems by periodic 2D surface structuring
Open the record for dataset details and reuse information.
Numerical modeling results for "Rift propagation interacting with pre-existing microcontinental blocks"
<p>Opensource software Paraview is required to open the vtr files. </p><p>Opensource software Matlab is required to open the mat and m files. </p>
Architecture Controls Phonon Propagation in All-Solid Brush Colloid Metamaterials - datasets
<p>Data sets to figures in the publication https://doi.org/10.1002/smll.202304157</p> <p>Fig1 - a) Experimental dispersion plot for close-packed PS particles (diameter <em>d</em> = 307 nm) infiltrated in PDMS (red filled circles), and silica (SiO<sub>2</sub>)-PS GNP assembly (square symbols, <em>d</em> = 214 nm) with the empty symbols denoting the dispersionless, highly localized, rotational mode originating from dipole torsional modes of the individual particles; the wavenumber is normalized with respect to <em>q</em><sub>BZ</sub> along ΓM direction. Calculated band structure along the [111] fcc high-symmetry direction for the PS opal infiltrated in (fluid) PDMS b) and for the DP980 colloidal crystal c) assuming respectively PBCs and IBCs (<em>k<sub>T</sub></em> = 0.021 GPa nm<sup>−1</sup>). Solid lines: longitudinal bands in (b) and non-degenerate bands including inactive bands in (c); dotted lines: quasi-flat (highly localized) band originating from dipole torsional modes (see main text). Shaded regions denote hybridization gaps of dipole-resonance origin (LHG for longitudinal modes; HG for all modes). The horizontal red arrow in (b) indicates the position of the quadrupolar resonant frequency of the individual PS sphere in PDMS. Note that only non-degenerate bands that correspond to longitudinal phonons are shown (we have omitted transverse phonon modes since they were not observed experimentally in Figure 1a).</p> <p> </p> <p> </p> <p>Fig 2- Top panel: experimental BLS spectra of three PS tethered SiO<sub>2</sub> nanoparticle GNP films with different grafting densities, DP1300 (σ = 0.53nm<sup>−2</sup> in (a)), DP530 (σ = 0.27nm<sup>−2</sup> in (b)) and DP1170 (<em>σ</em> = 0.08 nm<sup>−2</sup> in (c)) at a wave vector <em>q</em> (arrows in (d–f)) where a hybridization gap (HG, patterned areas in (d–f)) opens in the dispersion diagrams in (d–f)). The spectra are recorded with VV (black) and VH (grey) polarizations. The isotropic spectra obtained from the subtraction of the VH (depolarized) from the experimental polarized (VV) spectra are represented by Lorentzian lines (red). Bottom panel: experimental dispersion relations of the three systems in (a–c) and their optical images as insets in (d–f). The frequency is obtained from the isotropic spectra recorded at different <em>q's</em>. The direction of <em>q</em> is selected in the transmission and reflection (grey-shaded area) geometries and the magnitude of <em>q</em> is tuned by changing the scattering angle. The HGs denoted by patterned area are clearly observed in each system. The open circles represent the localized mode with <em>q</em>-independent frequency denoted by arrows. The effective medium acoustic modes are represented by red lines in the low-<em>q</em> regime. Note the dip in the BLS intensity at the frequency inside the gap (minimum DOS)</p> <p> </p> <p>Fig3 - Dispersion relation of a) DP1300 and b) DP1170 swollen with 20 wt.% DMP (solid symbols). For comparison, the phonon dispersion in the pristine GNP films (open symbols) is shown in (a,b). The black and red lines denote the low-frequency acoustic regime of DP1170 and plasticized DP1170, while the blue and black dashed lines are to guide the eyes. The vertical arrows indicate the position of the HG indicated by the hatched and shaded areas, whereas the horizontal lines with arrows indicate the frequency <em>f</em><sub>LO</sub> of the flat mode. Insets: Experimental VV (blue) and VH (grey) for the two plasticized samples and optical images of DP1300 with 20% DMP in (a). The isotropic spectra obtained from the subtraction of the VH (depolarized) from the polarized (VV) spectra are represented by Lorentzian lines (red) as in Figure <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/smll.202304157#smll202304157-fig-0002">2a,c</a>.</p> <p> </p> <p> </p> <p>Fig4-Theoretical band diagram of the a–c) sparsely DP1170 (<em>d</em> = 140 nm) and d–f) densely grafted DP1300 (<em>d</em> = 225 nm) considering PBCs with bulk PS sound velocities ( m s<sup>−1</sup>, m s<sup>−1</sup>) along [111] (left column, plots (a,d)), and, IBCs along [111] (middle column, plots (b,e)) and [112] (right column, plots (c,f)); the parameters used for the calculations are: <em>k</em><sub>L</sub> = 1.16 GPa nm<sup>−1</sup>, <em>k</em><sub>T</sub> = 0.20 GPa nm<sup>−1</sup>) with higher than bulk PS sound velocities ( , ), for DP1170 (plots b,c) and <em>k</em><sub>L</sub> = 0.615 GPa nm<sup>−1</sup>, <em>k</em><sub>T</sub> = 0.030 GPa nm<sup>−1</sup> with bulk PS sound velocities for DP1300 (plots (e,f)). Solid and open circles indicate the experimental points. Hatched regions denote hybridization gaps (LHG for longitudinal modes; HG for all modes). Along the high symmetry line ΓL of the fcc Brillouin zone (BZ), dark/light solid and dotted blue lines denote non-degenerate (longitudinal, i.e., of Λ<sub>1</sub> symmetry), doubly-degenerated (transverse, i.e., of Λ<sub>3</sub> symmetry) and deaf (i.e., of Λ<sub>2</sub> symmetry) computed bands, respectively. Along [112] that includes the low symmetry line ΓM of the fcc BZ all bands are non-degenerate of mixed character. The position of the flat band of dipole torsional origin is indicated by a red arrow.</p> <p> </p> <p> </p> <p>Fig5 - a) Evolution of the effective medium slope for the different colloidal SiO<sub>2</sub>-PS GNP assemblies (filled symbols, left axis) and of the enhanced transverse velocity ratio for PS (open symbols, right axis) as a function of the interparticle distance, <em>d</em> = <em>d</em><sub>cal</sub> (Table <a title="Link to table" href="https://onlinelibrary.wiley.com/doi/full/10.1002/smll.202304157#smll202304157-tbl-0001">1</a>), showing a non-linear decay with increasing PS filling fraction (dashed curve is a guide to the eye). b) Redshifted variation of the localized-mode frequency, <em>f</em><sub>LO</sub>, for the GNP colloids with increasing distance <em>d</em>. Blue dotted line denotes the flat mode frequency for a fcc crystal calculated along ΓL (taken at the middle of the BZ) assuming PBCs and bulk velocities for PS; solid gray line: interpolated curve for the various samples. c) Power-law variation of the localized-mode frequency with the tangential stiffness <em>k<sub>T</sub></em>. d) The tangential stiffness <em>k<sub>T</sub></em> as a function of the crowding parameter for the DP1170, DP530 and DP1300 with decreasing <em>σ</em>. In (b,c), all scales are logarithmic, symbols are color-indexed with the grafting-chain density value of the corresponding labeled samples.</p>
Simulation data for the manuscript "Characterizing Optimal Signal Propagation in the Human Brain Network."
<p>See <a href="https://github.com/kuffmode/OI-and-CMs">https://github.com/kuffmode/OI-and-CMs</a></p>
Simulation of optical light propagation
<p><span>Modeling video demonstrating the light propogation in the developed nanostructures based on MXene</span></p>
Simulation Results in the Paper "Propagation of Slow Slip Events on Rough Faults: Clustering, Back Propagation, and Re-rupturing" [Dataset]
<p>Data file "simulations.mat" contains the 5 simulations of slow slip events on flat or rough faults. </p> <table> <tbody> <tr> <td>structure array</td> <td>description</td> <td>reference</td> </tr> <tr> <td>s0</td> <td> 2.5 km long flat fault</td> <td>Fig. 2b</td> </tr> <tr> <td>s1</td> <td>2.5 km long rough fault</td> <td>Fig. 2c</td> </tr> <tr> <td>s2</td> <td>10 km long rough fault</td> <td>Fig. 5</td> </tr> <tr> <td>s3</td> <td>10 km long fractal fault</td> <td>Fig. 7</td> </tr> </tbody> </table> <p>structure array consists of:</p> <p>t: time (s)</p> <p>x: distance (m)</p> <p>v: slip rate (m/s)</p> <p>slip: accumulated slip (m)</p> <p>tau: shear stress (Pa)</p> <p>sigma: normal stress (Pa)</p> <p>notes: description</p> <p> </p> <p>Data file "catalog.mat" contains 3 simulated slow slip events' catalogs on flat and rough faults, c0, c1, and c2, in Fig. 4a, 4b, and 4c, respectively.</p> <p>It consists of:</p> <p>l: rupture length (m)</p> <p>time: time (s)</p> <p>notes: description</p>
Dataset for Automatic Calibration of Microproperties for 3D Parallel Bond Model of Ultra-Deep Carbonate Rocks and the Influence of Confining Pressure on Crack Propagation Patterns
<p>The data set is composed of results of three-dimensional (3D) Discrete Element Method (DEM) modeling performed by Xiaoyun Cheng et al., with the licensed commercial <em>Particle Flow Code </em>3D version 6.0 (PFC3D 6.0) from Itasca Consulting Group, Ltd. Part of the Figures were made with Origin Pro, Version 2021. OriginLab Corporation,Northampton, MA, USA. Part of the Figures were made with ParaView, Version 5.13.1.</p>
API X70 crack propagation tests
<p>Databases of crack propagation tests used for the master's thesis of Alexandre Henrique Oliveira, titled "EVALUATE CRACK GROWTH BY FATIGUE AT DIFFERENT TEMPERATURES IN A WELDED JOINT OF API X70 STEEL PIPES"</p>
Recognition of dominant driving factors behind sap flow of Liquidambar formosana based on back-propagation neutral network method
<p><i><span>Aims:</span></i> This study focused on the applicability of back-propagation (BP) neural networks in simulating sap flow (SF) using meteorological factors and a phenological index (<i><span>PI</span></i>) for <i><span>Liquidambar formosana</span></i><span>,</span> a deciduous broad-leaf tree species in subtropical China, and thus providing a useful and promising alternative to traditional methods for transpiration prediction.</p> <p><i><span>Methods: </span></i>Three-layered BP models with an architecture 4-10-1 <span>(four neurons in the input layer, ten neurons in the hidden layer and one neuron in the output layer) </span>were trained and tested using the Levenberg-Marquardt (LM) algorithm based on in situ observations of SF and concurrent microclimate at the Qianyanzhou Ecological Station, Jiangxi Province, Southeast China. The model performance was verified with testing data not used in model development.</p> <p><i><span>Results: </span></i>The BP models with eight input combinations proved a satisfactory fit: the determination coefficients (<i><span>R</span></i><sup><span>2</span></sup>) and fitting accuracies (<i><span>Acc</span></i>) (about 0.8 and 70%) were significantly higher than those of the multivariate linear regression (MLR) (about 0.5 and 50%), indicating their advantage in solving complex nonlinear problems involved in transpiration. In addition, the BP models showed a bit better performance by adding <i><span>PI</span></i><i> </i><span>to</span> the input family. The best BP model was achieved taking air temperature (<i><span>T</span></i><sub><span>a</span></sub>), relative humidity (<i><span>RH</span></i>), average net radiation (<i><span>ANR</span></i>) and <i><span>PI</span></i> as the input and sap flux density (<i><span>v</span></i><sub><span>s</span></sub>) as the output, with maximum <i><span>R</span></i><sup><span>2</span></sup> and <i><span>Acc</span></i> as high as 0.95 and 90%, respectively.</p> <p><i><span>Conclusions: </span></i><span>The</span><i> </i><span>BP</span><i> </i><span>models with input combination of </span><i><span>T</span></i><sub><span>a</span></sub><sub><span>, </span></sub><i><span>RH, ANR </span></i><span>and</span><i><span> PI </span></i><span>mirrored very well measured daily variations in </span><i><span>v</span></i><sub><span>s</span></sub>. The results could be used to fine-tune sap flow estimation by <i><span>Liquidambar formosana</span></i>, and thus shed light on the eco-hydrological process related to transpiration for deciduous broad-leaf trees.</p>
Source data for "Drought self-propagation in drylands due to land–atmosphere feedbacks"
<p>Source data for the analysis performed for "Drought self-propagation in drylands due to land–atmosphere feedbacks". Please refer to https://doi.org/10.5281/zenodo.5839819 for the code.</p>
data for manuscript 'Two remarkable characteristics of near-inertial wave propagation in the subtropical northwestern Pacific'
<p>Subsurface mooring observational data of Typhoon Sunvn in western Pacific</p>
Dataset for article "Fatigue crack initiation and propagation relation at notched specimens with welded joint characteristics"
<p>The dataset presents is a collection of fatigue test data obtained from artificially notched specimens with weld characteristics. The data was used to investigate the relation between crack initiation and propagation in welded joints of different notch acuity (different radii and opening angle) by excluding the effect of geometrical variation along weld seams. The experiments show that the investigated relationship basically depends on the notch acuity, the load level and the stress ratio.</p> <p> </p> <p>For detailed information about the tests and the assessment please refer to the article:</p> <p>Braun M, Fischer C, Baumgartner J, Hecht M, Varfolomeev I. Fatigue Crack Initiation and Propagation Relation of Notched Specimens with Welded Joint Characteristics. <em>Metals</em>. 2022; 12(4):615. https://doi.org/10.3390/met12040615 </p>
Impact of gigahertz and terahertz transport regimes on spin propagation and conversion in the antiferromagnet IrMn
<p>Data for the publication "Impact of gigahertz and terahertz transport regimes on spin propagation and conversion in the antiferromagnet IrMn" published in Applied Physics Letters. The following datasets are provided: GHz and THz raw data as function of the IrMn thickness, THz raw data spectra of the Pt|AF|F sample set, GHz and THz charge currents for forward (N|AF|F) and reversly-grown (F|AF|N) trilayer samples for N= Pt, W and Ta with varying AF thickness as well as the frequency-dependence of the charge current in the THz regime. Moreover, the temporal dynamics of the spin current densities from IrMn thicknesses 0nm, 3nm and 6nm are supplied.</p>
Signal propagation within the MCL-1/BIM protein complex - DATA
<p>Data supporting: "Signal propagation within the MCL-1/BIM protein complex"; Journal of Molecular Biology (JMB), February 2022 (DOI: 10.1016/j.jmb.2022.167499). </p>
Supplementary data for: "Transition from sub-Rayleigh anticrack to supershear crack propagation in snow avalanches"
<p>This Folder contains supplementary data for the paper "Transition from sub-Rayleigh anticrack to supershear crack propagation in snow avalanches". Please see ReadMe.txt for more details.</p> <p> </p>
Data for Embryo-scale epithelial buckling forms a propagating furrow that initiates gastrulation
<p>This is the data and code corresponding to <a href="https://doi.org/10.1038/s41467-022-30493-3">Embryo-scale epithelial buckling forms a propagating furrow that initiates gastrulation</a>, <em>Nature Comms.</em> <strong>13</strong>:3348.</p> <p><strong>Code</strong></p> <p>The file Surface_Evolver_script.txt is ... the Surface Evolver script, use with <a href="http://facstaff.susqu.edu/brakke/evolver/evolver.html">Ken Brakke's Surface Evolver</a></p> <p><strong>Data sources</strong></p> <p>The figure panels are based on the datafiles listed below, visualised either with <a href="http://facstaff.susqu.edu/brakke/evolver/evolver.html">Ken Brakke's Surface Evolver </a>or with <a href="http://www.gnuplot.info/">gnuplot 5</a>. When the datafiles listed do not contain the primary data, they contain a commented last line which is the command line for the software <a href="https://doi.org/10.5281/zenodo.5911337">datamerge</a> to generate it from other datafiles found in the Data_sources subdirectory.</p> <p><strong>Figure 1</strong></p> <p>Fig. 1c : data provided in <code>data_for_Fig_1g.tsv</code></p> <p>Fig. 1f : visualisation from Surface Evolver file <code>Young100dixPoisson000Step013.dmp</code></p> <p>Fig. 1g : <code>myosin_profile_us_only.pdf</code> generated with gnuplot script <code>myosin_profile.plot</code></p> <p><strong>Figure 2</strong></p> <p>Fig. 2a : <code>strain_profile_100_0_time_013.pdf</code> generated with gnuplot script <code>strain_profile.plot</code></p> <p>Fig. 2b : visualisation from Surface Evolver file <code>Young100dixPoisson000Step013.dmp</code></p> <p>Fig. 2c : <code>stress_profile_100_0_time_013.pdf</code> gnuplot script <code>stress_profile.plot</code></p> <p>Fig. 2d : visualisation from Surface Evolver file <code>Young100dixPoisson000Step063.dmp</code></p> <p><strong>Figure 3</strong></p> <p>Fig. 3a : <code>myosin_area.pdf</code> generated with gnuplot script <code>area_AP_DV_paper.plot</code></p> <p>Fig. 3b : <code>area_stripes_t=-1.pdf</code> generated with gnuplot script <code>area_AP_DV_paper.plot</code></p> <p>Fig. 3c : <code>only_AP_stripes_t=-1.pdf</code> generated with gnuplot script <code>area_AP_DV_paper.plot</code></p> <p>Fig. 3d : <code>only_DV_stripes_t=-1.pdf</code> generated with gnuplot script <code>area_AP_DV_paper.plot</code></p> <p>Fig. 3e : visualisation from Surface Evolver file <code>Young100dixPoisson000Step013.dmp Young100dixPoisson000Step063.dmp Young100dixPoisson000Step163.dmp Young100dixPoisson000Step213.dmp</code></p> <p><strong>Figure 4</strong></p> <p>Fig. 4a : visualisation from Surface Evolver file <code>Young100dixPoisson000Step213.dmp</code></p> <p>Fig. 4b : <code>furrow_propagation.pdf</code> generated with gnuplot script <code>furrow_propagation.plot</code></p> <p>Fig. 4c : <code>rate_of_furrowing_t=3.pdf</code> generated with gnuplot script <code>furrow_propagation.plot</code></p> <p>Fig. 4f : <code>curvature.pdf</code> generated with gnuplot script <code>curvature.plot</code></p> <p><strong>Figure 5</strong></p> <p>Fig. 5a : visualisation from Surface Evolver file <code>Young100dixPoisson000Step013.dmp Young100dixPoisson000Step063.dmp Young100dixPoisson000Step113.dmp Young100dixPoisson000Step163.dmp Young100dixPoisson000Step213.dmp</code></p> <p>Fig. 5b : visualisation from Surface Evolver file <code>Young100dixPoisson000Step013.dmp Young100dixPoisson000Step140.dmp</code></p> <p>Fig. 5d : <code>stress_laserablations_profile_100_0_time_063.pdf</code> generated with gnuplot script <code>stress_profile_for_laser_ablations.plot</code></p> <p>Fig. 5e : <code>laser_ablation_recoil.pdf</code> generated with gnuplot script <code>laser_ablation_recoil.plot</code></p> <p><strong>Supp Figure 1</strong></p> <p>Fig. S1a : <code>time_profile_FINI_100_0.pdf</code> generated with gnuplot script <code>time_profile_stress.plot</code></p> <p>Fig. S1b : visualisation from Surface Evolver file <code>Young100dixPoisson000Step013.dmp</code></p> <p>Fig. S1d : visualisation from Surface Evolver file <code>Young100dixPoisson000Step063.dmp</code></p> <p>Fig. S1e : <code>strain_profile_100_0_time_063.pdf</code> gnuplot script <code>strain_profile.plot</code></p> <p>Fig. S1f : <code>stress_profile_100_0_time_063.pdf</code> gnuplot script <code>stress_profile.plot</code></p> <p><strong>Supp Figure 1</strong></p> <p>Fig. S2a : <code>area_stripes_time_evolution.pdf</code> generated with gnuplot script <code>area_AP_DV_paper.plot</code></p> <p>Fig. S2b : <code>area_stripes_time_evolution_SPIM.pdf</code> generated with gnuplot script <code>area_AP_DV_paper.plot</code></p> <p><strong>Supp Figure 3</strong></p> <p>Fig. S3a : visualisation from Surface Evolver file <code>Young100dixPoisson000Step213.dmp</code></p> <p>Fig. S3c : <code>AP_binned_strain.pdf</code> generated with gnuplot script <code>AP_binned_strain.plot</code></p> <p>Fig. S3d : <code>furrow_propagation_experimental.pdf</code> generated with gnuplot script <code>plot_furrow.plot</code></p> <p>Fig. S3e : <code>curvature_DV_indiv.pdf</code> generated with gnuplot script <code>curvature.plot</code></p> <p>Fig. S3f : <code>depth_Gastrulation_ordi_Wild_Type_STITCHED_100_0.pdf</code> generated with gnuplot script <code>depth.plot</code></p>
Streamer propagation in humid air
<p>This dataset includes the input and output files for the paper: Streamer propagation in humid air.</p> <p><strong>Input files</strong>:</p> <p># <em>Plasma-chemistry and transport coefficients</em></p> <p>chemistry_files/*.txt</p> <p># <em>Configuration files</em></p> <p>config_files/*.cfg</p> <p># <em>Initial conditions (densities)</em></p> <p>config_files/m_user.f90</p> <p><strong>Output files (output_files)</strong>:</p> <p>*.silo</p> <p>*.txt</p> <p>Output data generated with the software afivo-streamer (https://gitlab.com/MD-CWI-NL/afivo-streamer) corresponding to the commit 1ff2676ba48a5eb568f06c7b11a548629a5ff20c</p>
Changes of MJO propagation characteristics under global warming
<p>The change of Madden–Julian Oscillation (MJO) propagation and periodicity characteristics under global warming is investigated using two selected CMIP5 models. It is found that the MJO period tends to be shorter, while its eastward propagation tends to be accelerated. Meanwhile the main MJO activity center shifts eastward toward the central equatorial Pacific. Two factors are possibly responsible for the increased eastward phase speed of the MJO. The first is attributed to the increase of atmospheric static stability that accelerates equatorial Kelvin wave speed. The second is attributed to the increase of zonal asymmetry of MJO-scale moist static energy (MSE) tendency, which is contributed to the combined effect of anomalous circulation and mean MSE gradient. A theoretical framework is constructed to understand the relative role of anomalous heating and mean static stability changes in determining MJO-scale circulation change. It is found that their effect is phase dependent. During the initial warming phase the circulation change is primarily controlled by the heating change, whereas during the later warming phase it is primarily controlled by the static stability change. The eastward shift of the main MJO activity center is possibly caused by the occurrence of an El Niño-like mean SST change and associated vertical overturning circulation change in the tropical Pacific.</p>
A DNA-Micropatterned Surface for Propagating Biomolecular Signals by Positional on-off Assembly of Catalytic Nanocompartments
<p>Data underlying the figures in the publication: Maffeis, V. <em>et al.</em> “A DNA-Micropatterned Surface for Propagating Biomolecular Signals by Positional on-off Assembly of Catalytic Nanocompartments” <em>Small</em> <strong>2022</strong>, 2202818, <a href="https://doi.org/10.1002/smll.202202818">https://doi.org/10.1002/smll.202202818</a></p> <p>Concept figures, Unicode origin graph (opj), TEM pictures, AFM pictures, LSM pictures</p> <p>TOC</p> <ol> <li><strong>Figure 1</strong> Concept figure representing the 3D view of micropatterned CNC immobilization promoting a cascade reaction between two distinct CNCs that ultimately results in a bioluminescent surface.</li> <li><strong>Figure 2</strong> Schematic reactions involved in the biomolecular signal propagation.</li> <li><strong>Figure 3</strong> Design and characterization of CNC-1 and CNC-2 (SLS and TEM).</li> <li><strong>Figure 4</strong> Schematic rapresentation of DNA synthesis inside Klenow-CNCs by SYBR green I.</li> <li><strong>Figure 5</strong> <em>a)</em> FCS autocorrelation curves of free Atto-488-template DNA (red) and Atto-488-template DNA CNC (blue); <em>b)</em> Activity of free Klenow polymerase, Klenow polymerase CNC with melittin, Klenow polymerase CNC without melittin at 25 °C; <em>c)</em> Enzyme activity of melittin-permeabilized Klenow CNCs and free Klenow fragment treated for 1 h at 55 °C; <em>d)</em> Enzyme activity of melittin-permeabilized Klenow CNCs and free Klenow fragment treated for 1 h at 75 °C.</li> <li><strong>Figure 6</strong> <em>a)</em> Chemical functionalization of the micro-printed glass surface with the amino-functionalized ssDNA; <em>b)</em> AFM height image of the DNA-functionalized glass slide recorded in 10 mm Tris-HCl buffer at pH 7.2 at the resolution of 128 lines; <em>c)</em> CLSM image of glass surface microprinted with Cy5-labeled NH2-modified 31-mer.</li> <li><strong>Figure 7</strong> <em>a)</em> left, AFM height image, middle, phase type image, and right, height profile (corresponding to dashed white line) of tandem CNCs attached via DNA hybridization on the microprinted glass surface recorded in 10 mm Tris-HCl buffer at pH 7.2; <em>b)</em> left, AFM height image, middle, phase type image and right, corresponding height profile of a single CNC; <em>c)</em> CLSM micrographs of polymersomes labeled with either cholesterol functionalized Atto-488 (green) or Dylight-633 (red)-DNA and immobilized by hybridization on a microprinted glass surface. Left panel, 488-channel, middle panel, 633-channel, right panel, merged image. Scale bars: 5 µm; <em>d)</em> Bioluminescence generation by permeable Klenow-CNCs and permeable ATP sulfurylase-CNCs (blue), by nonpermeable Klenow-CNCs and nonpermeable ATP sulfurylase-CNCs (pink), by the substrate mix alone (black), and by D-Luciferin and luciferase (red). Error bands represent ±SD, n = 3 replicates; <em>e)</em> QCM-D measurement of immobilized CNCs following repeated loading-removal cycles. Frequency (blue) and dissipation (brown) were recorded at three overtones (n = 3, 5, 7) as a function of time. (i, iv, vii) Addition of adaptor DNA, (ii, v, viii) immobilization of 22-mer polymersomes, and (iii, vi, ix) separation of DNA strands with 1 m NaOH.</li> </ol>
Competition dynamics in long-term propagations of Schizosaccharomyces pombe strain communities
<p>Experimental evolution studies with microorganisms such as bacteria and yeast have been an increasingly important and powerful tool to draw long-term inferences of how microbes interact. However, while several strains of the same species often exist in natural environments, many ecology and evolution studies in microbes are typically performed with isogenic populations of bacteria or yeast. In the present study, we firstly perform a genotypic and phenotypic characterization of two lab and eight natural strains of the yeast <i>Schizosaccharomyces pombe</i>. We then propagated, in a rich resource environment, yeast communities of 2-, 3-, 4- and 5-strains for hundreds of generations and asked which fitness related phenotypes – maximum growth rate or relative competitive fitness – would better predict the outcome of a focal strain during the propagations. While the strain's growth rates would wrongly predict long-term co-existence, pairwise competitive fitness with a focal strain qualitatively predicted the success or extinction of the focal strain by a simple multi-genotype population genetics model, given the initial community composition. Interestingly, we have also measured the competitive fitness of the ancestral and evolved communities by the end of the experiment (≈370 generations) and observed frequent maladaptation to the abiotic environment in communities with more than three members. Overall, our results aid establishing pairwise competitive fitness as good qualitative measurement of long-term community composition but also reveal a complex adaptive scenario when trying to predict the evolutionary outcome of those communities.</p>
ScienceDex guides
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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.