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49 results for “Structure probing”
Data for: The thermo-optical coefficient as an alternative probe for the structural arrest of polymeric glass formers
<p>The data is supplementary to the publication "The thermo-optical coefficient as an alternative probe for the structural arrest of polymeric glass formers", DOI: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.polymer.2024.126868" target="_blank" rel="noreferrer noopener">10.1016/j.polymer.2024.126868</a>, and contains processed raw data.</p> <p>Key words: Temperature-modulated optical refractometry, Solidification, Glass transition temperature, Thermo-optical coefficient, Structural arrest</p> <p>The data sets contain measured data on Temperature-modulated optical refractometry (TMOR) of a model epoxy polymer from the viscoelastic temperature range, through the glass transition to the glassy state. The data sets were collected via the temperature-jump method.</p> <p>Material details:</p> <ul> <li>Bisphenol A Diglycidyl ether (DGEBA, DER332, CAS 1675-54-3) + Difunctional and trifunctional carbocylic acids (Pripol1040, Croda) +1,5,7-triazabicyclo[4.4.0]dec-5-en (TBD, CAS 5807-14-7, 10 mol-% relative to carboxylic acid functions)</li> </ul> <p>Funding received from:</p> <ul> <li>German Research Foundation (DFG), project number: 521902629</li> </ul>
Data to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"
<p>This data set corresponds to the journal article "The capping agent is the key: Structural alterations of Ag NPs during CO2 electrolysis probed in a zero-gap gas-flow configuration"</p>
Dataset for Direct Geometric Probe of Singularities in Band Structure
<p>Included here is the processed data illustrated in the figures of both the main text, and the supplemental material. Below is a description of each file's contents.</p> <p> </p> <p><strong>Figure2Dcode.m</strong> contains the MATLAB code that generates Figure 2D of the main text. It takes the band populations inferred from five iterations of measurements, and calculates the means and standard errors for data taken at each theta as defined in the main text.</p> <p> </p> <p><strong>Figure2Ddata.csv</strong> contains the data illustrated in Figure 2D of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/12 radians for each column that follows. Row 5 is the error for the n=1 population, row 6 is the error for the n=2 population, and row 7 is the error on the sum of the population in bands with index not equal to 1 or 2.</p> <p> </p> <p><strong>Figure3Bcode.ipynb</strong> contains the jupyter notebook that generates Figure 3B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be the intermediate quasi-momenta, and different colors are used to differentiate between different acceleration times.</p> <p> </p> <p><strong>Figure3Bdata.csv</strong> contains the data illustrated in Figure 3B of the main text. The five columns correspond to the five different trajectory evolution times (0.5, 0.9, 1.3, 1.7, 2.1 milliseconds) shown in the Figure 3B. The first nine rows correspond to the nine trajectory midpoint positions in the Brillouin zone, as showed in Figure 3A; the first row corresponds to a midpoint at <strong>K</strong>. The next nine rows are the errors on the measurements.</p> <p> </p> <p><strong>Figure4Ccode.m</strong> contains the MATLAB code that generates Figure 4C of the main text. It takes the band populations inferred from twelve iterations of measurements, each at a different theta as defined in the main text, and calculate the means and standard errors for data taken at each theta.</p> <p> </p> <p><strong>Figure4Cdata.csv </strong>contains the data illustrated in Figure 4C of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/6 radians for each column that follows. Row 11 is the error for the n=3 population, row 12 is the error for the n=4 population, and row 13 is the error on the sum of the population in bands with index not equal to 3 or 4.</p> <p> </p> <p><strong>FigureS3Bcode.m</strong> contains the MATLAB code that generates Figure S3B of the main text. It takes the band populations inferred from seven iterations of measurements, and calculates the means and standard errors for data taken for each hold time at quasi-momentum Q as defined in the main text. The result is then fitted to a sine with exponentially decaying envelope.</p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data. A decay to account for coherence loss is added.</p> <p> </p> <p><strong>FigureS3data.csv </strong>contains the data illustrated in Figure 3 of the supplementary material. The first row is the data values, and the second row are the error bars.</p> <p> </p> <p><strong>FigureS4Bcode.m</strong> contains the MATLAB code that generates Figure S4B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be acceleration time, and different colors are used to differentiate between different intermediate points.</p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the paths in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data.</p> <p> </p> <p><strong>FigureS4data.csv </strong>contains the data illustrated in Figure 4 of the supplementary material. The first five rows are the normalized ground band population for five different trajectory mid points on the <strong>K</strong> - <strong>M</strong> - <strong>K'</strong> line of the Brillouin zone.; the first row is for a midpoint at <strong>K</strong>, and the fifth row is for a midpoint at <strong>M</strong>. The columns give the trajectory traversal times; the first column corresponds to a traversal time of 0.1 ms and each column corresponds to a new traversal time incremented by 0.2 ms. Rows 6-10 are the error bars for the measurements.</p> <p> </p> <p><strong>FigureS5Bcode.zip</strong> contains the codes that generate Figure S5B of the main text. For each subplots in Fig.S5B, the corresponding MATLAB code in the zip file takes the band populations inferred from three iterations of measurements, and calculate the means and standard errors for data taken at each acceleration time.</p> <p> </p> <p><strong>FigureS5Bdata.csv </strong>contains the data illustrated in Figure 5B of the supplementary material. Rows 1-20 correspond to subpanel (iii) in the Figure S5 of the supplementary material. Rows 21-40 correspond to subpanel (ii) in the Figure S5 of the supplementary material. Rows 31-60 correspond to subpanel (i) in the Figure S5 of the supplementary material.</p> <p>Rows 1-10 correspond to the band index and give the normalized band population; row 1 corresponds to band index n=1 and row 10 corresponds to band index n=10. Rows 21-30 correspond to the band index and give the normalized band population; row 21 corresponds to band index n=1 and row 30 corresponds to band index n=10. Rows 41-50 correspond to the band index and give the normalized band population; row 41 corresponds to band index n=1 and row 50 corresponds to band index n=10.</p> <p>Rows 11-20 (31-40) [51-60] give the error in the band populations for measurements in panel iii (ii) [i].</p> <p> </p> <p><strong>FigureS5Cdata.csv </strong>contains the data illustrated in Figure 5C of the supplementary material. The first (second) column is the vertical (horizontal) axis. The fourth (third) column is the error in the points on the vertical (horizontal) axis.</p> <p> </p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. In figure S6A, at each point in time shown the state is projected onto the instantaneous eigenbasis and the different band populations are extracted. In figure S6B and figure S6C, the whole experiment sequences corresponding to figure 2 and figure 4 in the main text are simulated, and the final population obtained is plotted, with the measurement results copied for reference.</p> <p> </p> <p><strong>FigureS7code.nb</strong> contains the mathematica notebook that generate Figure S7 of the main text. This code uses the two-band model described in the supplemental material to perform simulation.</p> <p> </p> <p><strong>Image_fitting.zip</strong> contains the MATLAB code and functions that were used to analyze the band mapping images. <strong>multiboxFit_v7_1.m</strong> is the main code that uses other MATLAB functions in the zip file. Overall, it takes absorption images as input, finds the position of each peak (<strong>BoxGenerator_v1_0.m</strong>), fit for the population in each peak in the images (<strong>createFit2D.m</strong>), assign the correct band number given the final quasi-momentum in the sequence (<strong>BoxesBandsThing_v2.m</strong>), and finally plot the inferred band populations, along with a visualization of the original images overlain with a Brillouin zone (<strong>PlotBZ_v2.m</strong>). The result of fits are saved in a separate file that are accessed by other analysis codes. Figure S2 and Figure S5C are also generated with this code.</p> <p> </p> <p>Additional codes <strong>Q_path_BZ.py</strong>,<strong> group_velo.py </strong>&<strong> diffr_img.py</strong> are included in<strong> Full Hamiltonian simulation.zip</strong> to ensure the correct functionality of the codes included.</p>
A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage - Fig. 4e
<p>The single-molecule FRET dataset underlying Fig. 4e of "A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage", DOI: 10.1039/C8SC00681D</p>
A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage - Fig. 4f
<p>The single-molecule FRET dataset underlying Fig. 4f of "A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage", DOI: 10.1039/C8SC00681D</p>
A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage - Fig. 4d
<p>The single-molecule FRET dataset underlying Fig. 4d of "A bi-terminal protein ligation strategy to probe chromatin structure during DNA damage", DOI: 10.1039/C8SC00681D</p>
Strain, Young's modulus, and structural transition of EuTiO3 thin films probed by micro-mechanical methods (Dataset)
<p>Datafiles of the Article "Strain, Young’s modulus, and structural transition of EuTiO3 thin films probed by micro-mechanical methods (Dataset)"</p>
Probing structural superlubricity of two-dimensional water transport with atomic resolution.
<p>Here lies the tabulated data used to create the Figures for the Science manuscript ado1544 titled "Probing structural superlubricity of two-dimensional water transport with atomic resolution".</p>
Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science
<p>Files concerning the publication "Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science"(arxiv:2205.13308 )</p>
Molecular dynamics simulations with grand-canonical reweighting suggest cooperativity effects in RNA structure probing experiments
<p>Molecular dynamics simulations of an RNA GAAA tetraloop interacting with SHAPE reagent 1-Methyl-7-nitroisatoic anhydride (1m7) in different numer of copies (1 to 19). See also https://arxiv.org/abs/2209.12640 and https://github.com/bussilab/paper-shapemd.</p>
Datesets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: 10.1002/pssa.202300148
<p>Datasets and images of the publication "Probing crystallinity and grain structure of 2D materials and 2D-like van der Waals heterostructures by low-voltage electron diffraction" - DOI: <a href="https://www.doi.org/10.1002/pssa.202300148">10.1002/pssa.202300148</a></p> <p>The Jupyter Notebooks for analyzing the datasets and generating all the figures are available at <a href="https://gitlab.com/JohMu/tds_hios_manuscript">https://gitlab.com/JohMu/tds_hios_manuscript</a>.</p> <p><strong>MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>192x192 scan pixels</li> <li>200x200 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 10.56 mm</li> <li>Camera pixel size: 4x5.86 µm = 23.44 µm (original dataset with 4x4 binning)</li> <li>File location: Figure 2_3_S1.zip -> 230101205338_20kV_hexz0_camz-10_posi_003_good\scan_data_bin2_centered_crop-imgNx200.h5</li> <li>The original raw dataset (23 GB, 192x192 scan pixels, 800x800 camera pixels, camera pixel size: 5.86 µm), the scan reference dataset and the Jupyter Notebook for the shift-compensation is available from the author. The dataset uploaded here is binned by a factor of 4 and shift-compensated.</li> </ul> <p><strong>C60/MoS<sub>2</sub> 4D-STEM dataset:</strong></p> <ul> <li>113x113 scan pixels</li> <li>512x512 camera pixels</li> <li>Acceleration voltage: 20kV</li> <li>Camera length: 20.56 mm</li> <li>Camera pixel size: 5.86 µm</li> <li>File location: Figure 4.zip -> scan_data_scan113x113_gzip.h5</li> </ul> <p> </p>
Eclipses of continuous gravitational waves as a probe of stellar structure
<p>Jupyter notebooks to reproduce the results and figures of the paper "Eclipses of continuous gravitational waves as a probe of stellar structure". Input files and data produced from the MESA simulations in the paper is also included. MESA version used is 10398.</p> <p>The jupyter notebooks included are:</p> <p>- Notebook.ipynb: Containing all calculations and plots except for the pop-synth section.</p> <p>- accreting_NS.ipynb: Containing the calculations and plots for the pop-synth section.</p> <p>Further details can be found inside these notebooks.</p> <p> </p> <p>The mesa input files are contained in</p> <p>- mesa_models/sun: For the solar model used.</p> <p>- mesa_models/LMXB: For the low mass X-ray binary model used.</p>
Dual NMR for probing intrinsic bone structure and a potential gut-bone axis in ovarectomized rats
<p>Dual NMR for probing intrinsic bone structure and a potential gut-bone axis in ovarectomized rats</p>
Wide-range viscoelastic compression forces in microfluidics to probe cell-dependent nuclear structural and mechanobiological responses
<p><span>The cell nucleus plays a critical role in mechanosensing and mechanotransduction processes, by adaptive changes of its envelope composition to external biophysical stimuli such as substrate rigidity and tensile forces. Current measurement approaches lack of precise control in stress application on nuclei, thus significantly impairing a complete mechanobiological study of cells. Here, we present a contactless microfluidic approach capable to exert a wide-range of viscoelastic compression forces (10-103 </span>μ<span>N) -as an alternative to adhesion-related techniques- to induce cell-specific mechano-structural and biomolecular changes. We succeed in monitoring substantial nuclear modifications in Lamin A/C expression and coverage, diffusion processes of probing molecules, YAP shuttling, chromatin re-organization and cGAS pathway activation. As a result, high compression forces lead to a nuclear reinforcement (e.g. up to +20% in Lamin A/C coverage) or deconstruction (e.g. down to -45% in Lamin A/C coverage with a 30% reduction of chromatin condensation state parameter) up to cell death. We demonstrate how wide-range compression on suspended cells can be used as a tool to investigate nuclear mechanobiology and to define specific nuclear signatures for cell mechanical phenotyping. </span></p>
Investigating the interplay between RNA structural dynamics and RNA chemical probing experiments
<p>Normalized SHAPE reactivity for pre-miR20b and CDE2GG as reported in 'Investigating the interplay between RNA structural dynamics and RNA chemical probing experiments'.</p>
Photo-activatable Ub-PCNA probes reveal new structural features of the S. cerevisiae Polη/PCNA complex
<p><span><span><span><span><span><span><span><span><span><span><span>The Y-family DNA polymerase η (Polη) is critical for the synthesis past damaged DNA nucleotides in yeast through translesion DNA synthesis (TLS). TLS is initiated by monoubiquitination of proliferating cell nuclear antigen (PCNA) and the subsequent recruitment of TLS polymerases. Although individual structures of the Polη catalytic core and PCNA have been solved, a high-resolution structure of the complex of Polη/PCNA or Polη/monoubiquitinated PCNA (Ub-PCNA) still remains elusive, partly due to the disordered Polη C-terminal region and the flexibility of ubiquitin on PCNA. To circumvent these obstacles and obtain structural insights into this important TLS polymerase complex, we developed photo-activatable PCNA and Ub-PCNA probes containing a <i>p</i>-benzoyl-L-phenylalanine (<i>p</i>Bpa) crosslinker at selected positions on PCNA. By photo-crosslinking the probes with full-length Polη, specific crosslinking sites were identified following tryptic digestion and tandem mass spectrometry analysis. We discovered direct interactions of the Polη catalytic core and its C-terminal region with both sides of the PCNA ring. Model building using the crosslinking site information as a restraint revealed multiple conformations of Polη in the polymerase complex. Availability of the photo-activatable PCNA and Ub-PCNA probes will also facilitate investigations into other PCNA-containing complexes important for DNA replication, repair and damage tolerance. </span></span></span></span></span></span></span></span></span></span></span></p>
Wide-range viscoelastic compression forces in microfluidics to probe cell-dependent nuclear structural and mechanobiological responses
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Photo-activatable Ub-PCNA probes reveal new structural features of the S. cerevisiae Polη/PCNA complex
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Dataset From: Structuring of colloidal silica nanoparticle suspensions near water–silica interfaces probed by specular neutron reflectivity
<p>The dataset for the publication "Structuring of colloidal silica nanoparticle suspensions near water–silica interfaces probed by specular neutron reflectivity". DOI: 10.1039/d0cp00465k.</p> <p>Files containing data have .dat extension and are in text format.</p>
Single molecule microscopy as a tool to probe multiscale food structures
<p>Single molecule microscopy as a tool to probe multiscale food structures</p>
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International Brain Laboratory public data
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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.