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128 results for “IgM”
FIGURE 16. A. IGM 100 in Osteology of a New Late Cretaceous Troodontid Specimen from Ukhaa Tolgod, Ömnögovi Aimag, Mongolia
FIGURE 16. A. IGM 100/1323, arrow indicates sampled eggshell fragment. Caliper edges are 5 mm apart. B. IGM 100/1323, eggshell under SEM. Line indicates boundary between mammillary (below line) and prismatic (above line) layers. C. IGM 100/1323, eggshell thin section under plane polarized light. Line indicates boundary between mammillary (below line) and prismatic (above line) layers. D. AMNH FARB 6631, clutch of 18 eggs, prepared with their bottom surfaces upwards. E. AMNH FARB 6631, eggshell under SEM. Line indicates boundary between mammillary (below line) and prismatic (above line) layers. F. AMNH FARB 6631, eggshell thin section under plane polarized light. Line indicates boundary between mammillary (below line) and prismatic (above line) layers.
FIGURE 15. IGM 100 in The Osteology Of Haya Griva (Dinosauria: Ornithischia) From The Late Cretaceous Of Mongolia
FIGURE 15. IGM 100/3672, containing the remains of two Haya griva individuals (labeled 1 and 2), represented by three pedes in A, dorsal and B, oblique lateral view. The arrows in B indicate the pedes of the two individuals pointing in opposite directions.
FIG. 6. A. IGM 100 in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds
FIG. 6. A. IGM 100/3181.Two carpals in unknown orientation. B. Metacarpal I and manual phalanx I-1 in dorsal view. C. Metacarpal II or III and manual phalanx in dorsal view. D. Manual phalanx in dorsal view. E. Three ungual phalanges in partial dorsal view.
Single-cell naïve IgM VH:VL sequence data from 22 Kymice
<p>Single-cell VH:VL sequencing data derived from naïve B-cells isolated from 22 Kymice. This dataset is published as part of the review process for the following preprint: https://www.biorxiv.org/content/10.1101/2022.06.27.497709v1. </p>
CLAMATO DR2: IGM Lyman-Alpha Forest Tomography Survey Public Data Release of Spectra and Maps
<p> </p><p><strong>CLAMATO Data Release 2</strong></p><p>Updated 2023 Jan 11th by Khee-Gan Lee (<a href="mailto:kglee@ipmu.jp">kglee@ipmu.jp</a>)</p><p>Supporting paper has been published in <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..263...27H/abstract">ApJS</a> (arXiv:2109.09660)</p><p>These are data products associated with the second data release (DR2) of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field. This is an updated version of the original DR2 repository (https://doi.org/10.5281/zenodo.5842842), which had accidentally left out several files. The other files that was in that repository are duplicated here.</p><p>The following is the summary of the main products:</p><ul><li>Source catalog (cl2020_valueadded_release_20200602.txt)</li><li>Reduced spectra, in spec_v0 .tar.gz tarball</li><li>Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data_v0.bin)</li><li>Wiener-reconstructed 3D absorption map (map_2020_v0.bin)</li><li>Reconstructed 3D matter density field using the TARDIS-II algorithm (TARDIS_CLAMATO_DR2_v0.4_smoothed.npy)</li><li>Eigenvalues of the pseudo-deformation tensor calculated from the density field (TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy)</li></ul><p><strong> Redshift Catalog and Spectra</strong></p><p>We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS-Blue.</p><p>The source catalog is provided in the ASCII file cl2020_valueadded_release_20200602.txt, with the following columns:</p><ul><li>SPECFIL: Filename of spectrum (within spec_v0.tar.gz)</li><li>TOMO_ID: CLAMATO ID number</li><li>GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog</li><li>CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894</li><li>ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum</li><li>QSO: QSO flag (1 if QSO, 0 if non-QSO)</li><li>RA: R.A. in degrees (J2000)</li><li>DEC: Dec in degrees (J2000)</li><li>S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate</li><li>S/N_RED: Estimated S/N over restframe 1250 Å < \(\lambda\) < 1350 Å, -9.0 denotes no estimate</li><li>TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)</li><li>EXPTIME: Exposure time on the spectrum, in seconds (aggregate)</li></ul><p>The tarball spec_v0.tar.gz include all the reduced spectra from LRIS-Blue, with the respective filename indicated by the first column of the catalog. We decided not to make available the LRIS-Red spectra.</p><p>The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:</p><ul><li>HDU0: Object spectral flux density, in units of \(10^{-17}\,\mathrm{erg\,s^{-1}\,cm^{-2}\,angstrom^{-1}}\)</li><li>HDU1: Noise standard deviation</li><li>HDU2: Pixel Wavelengths in angstroms</li></ul><p><strong>Pixel Data</strong></p><p>The binary file PIXEL_DATA_v0.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. </p><p>The first value in the binary is a 32-bit integer specifying the number of pixels (84608), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.</p><p>An example python script to read pixel_data is as follows:</p><p>import numpy as np with open('pixel_data_v0.bin','r') as f: npix = np.fromfile(f, dtype=np.int32, count=1) f.seek(4) pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))</p><p>LIST_TOMO_INPUT_2020.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.</p><p>The origin of the map coordinates is at [RA=149.89150 deg, Dec=2.0915050 deg], where x is increasing in the R.A. dimension and y is increasing in the Dec.</p><p><strong>Tomographic Map</strong></p><p>The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the "dachshund" algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). </p><p>The reconstructed map is MAP_2020_V0.BIN, which is a 68x56x876 = 3335808 pixel double-precision binary file. The dimension that changes fastest is the z-dimension along the line-of-sight (876 pixels per dimension), followed by the y-dimension in increasing Declination (56 pixels per dimension) and x-dimension along the direction of increasing R.A. (68 pixels per dimension).</p><p>Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].</p><p>Note that no additional smoothing has been applied in this binary, whereas most of the visualizations in the paper have had Gaussian smoothing applied.</p><p><strong>Reconstructed Density Field and Cosmic Web</strong></p><p>We also release the underlying matter density field in the CLAMATO volume, estimated using the TARDIS-II algorithm (https://arxiv.org/abs/2007.15994). This is in numpy format, and can be read directly into a 3D array using Python as follows:</p><p>>>> import numpy as np >>> den=np.load('TARDIS_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(den) (34, 28, 438) >>> eigen = np.load('TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(eigen) (34, 28, 438, 3)</p><p>The 3 dimensions correspond to the R.A., Declination, and line-of-sight directions, respectively. </p><p>Note that unlike the Wiener-filtered absorption map<i>, the TARDIS-II reconstruction outputs are in 1Mpc/h comoving voxels. </i>The coordinate zero-point sand line-of-sight comoving distance-redshift relationship are otherwise the same as the Wiener map. </p><p>The eigenvalues of the pseudo-deformation tensor are in TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy, with the same array shape as the matter density, but with an additional array dimension storing the 3 sorted eigenvalues at each point in the volume. </p><p>For reference, we have included an iPython notebook (Tomographic_Maps.ipynb) that plots the Wiener-filtered absorption alongside the TARDIS-II densities and eigenvalues (e.g. Figure 8 of the main paper).</p>
FIGURE 31. IGM 100 in A Second Specimen of Citipati osmolskae Associated with a Nest of Eggs from Ukhaa Tolgod, Omnogov Aimag, Mongolia
FIGURE 31. IGM 100/1125, an unassociated pair of oviraptorid eggs from Ukhaa Tolgod.
FIGURE 30. IGM 100 in A Second Specimen of Citipati osmolskae Associated with a Nest of Eggs from Ukhaa Tolgod, Omnogov Aimag, Mongolia
FIGURE 30. IGM 100/3505, a clutch of oviraptorid eggs from Ukhaa Tolgod.
FIGURE 9. IGM 100 in A Second Specimen of Citipati osmolskae Associated with a Nest of Eggs from Ukhaa Tolgod, Omnogov Aimag, Mongolia
FIGURE 9. IGM 100/1004 in right lateral view. Anterior is to the right.
FIGURE 3. IGM 100 in A Second Specimen of Citipati osmolskae Associated with a Nest of Eggs from Ukhaa Tolgod, Omnogov Aimag, Mongolia
FIGURE 3. IGM 100/979 in dorsal view after preparation.
FIGURE 11. IGM 100 in A Second Specimen of Citipati osmolskae Associated with a Nest of Eggs from Ukhaa Tolgod, Omnogov Aimag, Mongolia
FIGURE 11. IGM 100/1004 in anterior view.
FIGURE 12. IGM 100 in A Second Specimen of Citipati osmolskae Associated with a Nest of Eggs from Ukhaa Tolgod, Omnogov Aimag, Mongolia
FIGURE 12. IGM 100/1004 in posterior view.
FIGURE 10. IGM 100 in A Second Specimen of Citipati osmolskae Associated with a Nest of Eggs from Ukhaa Tolgod, Omnogov Aimag, Mongolia
FIGURE 10. IGM 100/1004 in left lateral view. Anterior is to the left.
IGM Variant Oncogenicity Classifications
<p>IGM Variant oncogenicity classification data curated using the Variation Categorizer. </p>
FIG. 2. IGM 100 in A new specimen of the ornithischian dinosaur Haya griva, cross-Gobi geologic correlation, and the age of the Zos Canyon beds
FIG. 2. IGM 100/3181, a partial skeleton of Haya griva.
Fig. 3. Pinacosaurus grangeri. IGM 100 in A New Specimen of Pinacosaurus grangeri (Dinosauria: Ornithischia) from the Late Cretaceous of Mongolia: Ontogeny and Phylogeny of Ankylosaurs
Fig. 3. Pinacosaurus grangeri. IGM 100/1014. Stereopairs of skull in occipital view. See appendix
Fig. 6. Pinacosaurus grangeri. IGM 100 in A New Specimen of Pinacosaurus grangeri (Dinosauria: Ornithischia) from the Late Cretaceous of Mongolia: Ontogeny and Phylogeny of Ankylosaurs
Fig. 6. Pinacosaurus grangeri. IGM 100/
Fig. 2. IGM 100 in A Small Derived Theropod from Öösh, Early Cretaceous, Baykhangor Mongolia
Fig. 2. IGM 100/1119 in right lateral view. Anatomical labels in appendix 3.
Glacier catalogue for IGM physics-informed deep-learning emulator pretraining
<p>This dataset was created with the iceflow glacier model CfsFlow to generate glacier extent and retreat in the Alps and New zealand with the goal to generate realistic and diverse glacier states for pretraining the physics-informed deep-learning emulator of IGM (https://github.com/jouvetg/igm).</p> <p>The data consists of distributed surface topography (usurf) and ice thickness (thk) of 8 snapshots of 37 glaciers in different stages (advance and retreat). The data is organized glacier-wise: each folder corresponds to one glacier, which contains a unique NetCDF file with 2D distributed raster data of surface elevation and ice thickness.</p>
ACALA-R In Predominantly Demyelinating IgM Mediated Neuropathy
ClinicalTrials.gov study NCT05065554. IPD Sharing: YES. Countries: 1. Publications: 1.
IgM and IgY CDR3 sequences from naive, vaccinated and/or infected chickens
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