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1,051 results for “ring”
Patient-derived Organoids and Xenografts Uncover Therapeutic Vulnerabilities in Colorectal Signet Ring Cell Carcinomas
GEO Series GSE279979. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Muscle Ring Finger-1 regulation of right-sided heart transcriptional responses to hypoxia in vivo.
GEO Series GSE82345. Mus musculus. 17 samples. Type: Expression profiling by array.
Coordinated mis-splicing of TMEM14C and ABCB7 causes ring sideroblast formation in SF3B1-mutant myelodysplastic syndrome
GEO Series GSE189546. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing; Other.
PfAP2-G ChIP-seq in committed schizonts, sexual rings, and stage I gametocytes
GEO Series GSE120448. Plasmodium falciparum. 12 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Transcriptomic profilling of cisplatin sensitive and resistance in signet ring KATOIII gastric cancer cells
GEO Series GSE186205. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Regulatory mechanisms of the Hippo/YAP axis by G-protein coupled estrogen receptor in gastric signet-ring cell carcinoma
GEO Series GSE283111. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Impact of the menstrual cycle and ethinyl estradiol/etonogestrel contraceptive vaginal ring on granulysin and other mucosal immune mediators
GEO Series GSE142058. Homo sapiens. 76 samples. Type: Expression profiling by array.
De novo variant in RING finger protein 213 causes systemic vasculopathy.
GEO Series GSE282828. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Stacked Dense Denoise-Segmentation CGLS synthetic reconstruction from 91 projection without ring artefacts
<p>The CGLS recontruction without ring artefacts from the 91 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p> <div> </div>
Stacked Dense Denoise-Segmentation CGLS synthetic reconstruction from 91 projection with ring artefacts
<p>The CGLS recontruction with ring artefacts from the 91 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
Stacked Dense Denoise-Segmentation FBP synthetic reconstruction from 3601 projection with ring artefacts
<p>The FBP recontruction with ring artefacts from the 3601 projection of the synthetic dataset used in the Stacked Dense Denoise-Segmentation Network. For the reconstruction the TomoPhantom software was used (<a href="https://doi.org/10.5281/zenodo.2546856">https://doi.org/10.5281/zenodo.2546856</a>). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
Figure 4 in Ringing studies of the turtle dove Streptopelia turtur (Aves: Columbidae during passage through Antikythera Island, southwestern Greece
Figure 4. Histograms of the body mass of the turtle dove in spring and autumn.
Ring light gold
If you want to reduce the dimensions of the model, I will be happy to help you STL file has been fixed and ready for the print -STL File has been checked Software -No any bugs during printing The 3dm file is not suitable for printing and I just provided it for you to make changes. And I can help you if needed, but the rest of the files are ready to print If you need to make little changes to this model I'll do it for you no extra charge. If you have any questions I will be happy to answer them -If you are satisfied with the model, thank you for rating it[LIST] Source: Objaverse 1.0 / Sketchfab
Ring of the Kraków's mayors
ID no. MHK 683/II "Ring of the Kraków's mayors" Museum: Historical Museum of the City of Kraków https://muzea.malopolska.pl/en/objects-list/43 Digitalisation: RDW MIC, Małopolska's Virtual Museums project Source: Objaverse 1.0 / Sketchfab
Ring
Inventory number: MT-E/3684 District Museum in Tarnów https://muzea.malopolska.pl/en/objects-list/1287 Source: Objaverse 1.0 / Sketchfab
Detection of interstellar 1-cyanopyrene: a four-ring polycyclic aromatic hydrocarbon
<div>This is the dataset for the article:</div> <div>Detection of interstellar 1-cyanopyrene: a four-ring polycyclic aromatic hydrocarbon</div> <div> </div> <div>Authors:</div> <div>GOTHAM Collaboration</div> <div> </div> <div>DOI:</div> <div>DOI: 10.1126/science.adq6391</div> <div> </div> <div>Journal:</div> <div>Science</div> <div> </div> <div>Corresponding authors:</div> <div>Gabi Wenzel, Ilsa R. Cooke, Brett A. McGuire</div> <div> </div> <div>To cite this:</div> <div>Gabi Wenzel et al., Detection of interstellar 1-cyanopyrene: A four-ring polycyclic aromatic hydrocarbon. Science 386, 810-813 (2024). DOI:10.1126/science.adq6391</div> <div> </div> <div>#--------------------------------------------------------------------------------------</div> <div> </div> <div>Folders:</div> <div> - DATA_1-cyanopyrene_spectroscopy contains all SPCAT/SPFIT files for 1-cyanopyrene</div> <div>(Figs. 2, 3, S3, S5, S7) including the partition function in the .qpart file</div> <div> - DATA_Fig2 contains the unsmoothed GOTHAM data as presented in Fig. 2</div> <div> - DATA_SNRLinesinStack contains the unsmoothed GOTHAM data centered around each of the</div> <div>150 brightest SNR lines that are contained in the stack (Figs. 3, S5, S6, S7)</div> <div> </div> <div>#--------------------------------------------------------------------------------------</div> <div> </div>
Randomised material parameter impedance dataset of piezoelectric rings (RaPIDring)
<h1>Version notes</h1> <p>This updated version uses larger ranges for the material parameters to account for a wider range of samples. Further, an erroneous boundary condition in the simulation model used to generate the data set in the previous version has been fixed.</p> <h1>Description of the dataset</h1> <p>This dataset contains the results of 301008 finite-element simulations of the complex, frequency-dependent electrical impedance of a piezoelectric ring with randomised material parameters. Each impedance consists of 2000 samples in the frequency domain up to 8 MHz. We assume the sample to be dielectric, thus the impedance a frequency 0 Hz is infinite. The piezoelectric ring has an outer radius of 6.35 mm an inner radius of 2.6 mm and a thickness of 1 mm. The transverse isotropic material parameters are sampled from independent uniform distributions with ranges that are intended to represent the behaviour of different piezoceramic materials. The parameters of the Rayleigh damping model (alpha_M and alpha_K) are sampled from a logarithmic distribution to account for the larger parameter range.</p> <table> <tbody> <tr> <td><strong>Parameter </strong></td> <td><strong>Min</strong></td> <td><strong>Max</strong></td> <td><strong>Unit </strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>c11</td> <td>110</td> <td>165</td> <td>GPa</td> <td>Elastic stiffness</td> </tr> <tr> <td>c12</td> <td>60 </td> <td>150</td> <td>GPa</td> <td>Elastic stiffness</td> </tr> <tr> <td>c13</td> <td>60 </td> <td>100 </td> <td>GPa</td> <td>Elastic stiffness</td> </tr> <tr> <td>c33</td> <td>110 </td> <td>140</td> <td>GPa</td> <td>Elastic stiffness</td> </tr> <tr> <td>c44</td> <td>18 </td> <td>30 </td> <td>GPa</td> <td>Elastic stiffness</td> </tr> <tr> <td>eps11 </td> <td>3</td> <td>12</td> <td>nF/m</td> <td>Dielectric permittivity</td> </tr> <tr> <td>eps33 </td> <td>4</td> <td> 8</td> <td>nF/m</td> <td>Dielectric permittivity</td> </tr> <tr> <td>e15</td> <td>6</td> <td>20</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>e31</td> <td>1</td> <td>7.5</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>e33</td> <td>8</td> <td>20</td> <td>C/m^2</td> <td>Piezoelectric coupling</td> </tr> <tr> <td>alpha_M</td> <td>0.2</td> <td>150 </td> <td>1/ms</td> <td>Mass-proportional damping</td> </tr> <tr> <td>alpha_K</td> <td>10</td> <td>1000</td> <td>ps </td> <td>Stiffness-proportional damping</td> </tr> <tr> <td>density</td> <td>7600</td> <td>7850</td> <td>kg/m^3</td> <td>Density</td> </tr> </tbody> </table> <h1>Files</h1> <p>The dataset contains the following files:</p> <ul> <li><strong>dataset.hdf5</strong>: The main dataset file in HDF5 format. Refer to the next section on how to load the dataset.</li> <li><strong>simulation.xml</strong>: The file describing the parameters of the finite element simulation. This file can be used with <a href="https://www.opencfs.org/">openCFS</a> along with the mesh and material file to simulate the behaviour of the piezoelectric ceramic in the frequency domain. This simulation file was used with randomised material parameters to generate the dataset.</li> <li><strong>material.xml</strong>: The material file used for the simulation with exemplary material parameters values.</li> <li><strong>ring.geo</strong>: The gmsh geometry file with the axisymmetric representation of the piezoelectric ring.</li> <li><strong>ring.msh</strong>: The mesh file generated with gmsh using the ring.geo file.</li> </ul> <h1>Loading the dataset</h1> <p>The dataset is stored as a HDF5 file, which can be opened with all libraries that support that format, e.g. in Python using the h5py library:</p> <p><code>import h5py</code></p> <p><code># Open the dataset in read mode.</code><br><code>file = h5py.File("dataset.hdf5", "r")</code></p> <p><code># Impedances as a 301008 x 2000 array of complex numbers.</code><br><code>impedances = file["impedances"]</code><br><code># Material parameter values as a 301008 x 13 array of real numbers.</code><br><code>parameters = file["parameters"]</code><br><code># Frequency vector of the impedance with length 2000.</code><br><code>frequencies = file["meta"]["frequencies"]</code><br><code># 13 strings with the identifiers of the material parameters.</code><br><code>parameter_labels = file["meta"]["parameter_labels"]</code></p> <h1>Simulating impedances</h1> <p>To generate a result for the electrical impedance using the supplied simulation files, download and install openCFS and call the executable with the simulation.xml, but omit the file extension, e.g.:</p> <p><code>cfsbin.exe simulation</code></p> <p>The path to the executable of openCFS will depend on your operating system and installation. Running the simulation will result in the creation of several files and folders. Among those files will be the result for the electric charge on one of the electrodes of the sample, which will be placed in the 'history' subfolder. We can determine the current by taking the time derivative of the charge and already know the voltage because we excited the piezoceramic with an electric potential of 1 V in the simulation. Because the simulation is conducted in the frequency regime, all we have to do is to divide voltage by current to get the frequency dependent electrical impedance. The loading of the result and calculation of the impedance is implemented in the following Python script as an example:</p> <p><code>import numpy as np</code></p> <p><code># Load result file for electric charge</code><br><code>result_path = 'history/simulation-elecCharge-surfRegion-ground.hist'</code><br><code>data = np.loadtxt(result_path)</code></p> <p><code>frequency = data[:, 0]</code><br><code># Convert polar representation from file to complex numbers.</code><br><code>charge = data[:, 1] * np.exp(1j * 2 * np. pi / 360 * data[:, 2])</code></p> <p><code># Excitation potential is 1 V in simulation.</code><br><code>potential = 1</code><br><code># Determine impedance by applying Z = V / I = V / (j omega Q).</code><br><code>impedance = potential / (1j * 2 * np.pi * frequency * charge)</code></p>
Figure 6 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 6 Linearity of the developed RP-HPLC D_5d stability indicating method.
Figure 2 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 2 Chromatogram of standard solution of the analyzed hydrazone D-5d (tR = 6.110).
Figure 1 from: Tzankova D, Peikova L, Vladimirova S, Georgieva M (2019) Development and validation of RP-HPLC method for stability evaluation of model hydrazone, containing a pyrrole ring. Pharmacia 66(3): 127-134. https://doi.org/10.3897/pharmacia.66.e47035
Figure 1 Structure of the selected model compound.
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.