The Frequency of Carbon Stars Among Extremely Metal-Poor Stars

We demonstrate that there are systematic scale errors in the [Fe/H] values determined by the Hamburg/ESO Survey (and by inference by the HK Survey in the past) for certain extremely metal poor highly C-enhanced giants. The consequences of these scale errors are that a) the fraction of carbon stars at extremely low metallicities has been overestimated in several papers in the recent literature b) the number of extremely metal poor stars known is somewhat lower than has been quoted in the recent literature c) the yield for extremely metal poor stars by the HES Survey is somewhat lower than is stated in the recent literature. A preliminary estimate for the frequency of Carbon stars among the giants in the HES sample with -4 < [Fe/H] < -2.0 dex is 7.4 +-2.9%; adding an estimate for the C-enhanced giants with [C/Fe] > 1.0 dex without detectable C2 bands raises the fraction to 14 +-4$%. We rely on the results of an extensive set of homogeneous detailed abundance analyses of stars expected to have [Fe/H] < -3.0 dex selected from the HES to establish these claims. We have found that the Fe-metallicity of the cooler (Teff < 5200K) C-stars as derived from spectra taken with HIRES at Keck are a factor of ~10 higher than those obtained via the algorithm used by the HES project to analyze the moderate resolution follow-up spectra, which is identical to that used until very recently by the HK Survey. This error in Fe-abundance estimate for C-stars arises from a lowering of the emitted flux in the continuum bandpasses of the KP (3933 A line of CaII) and particularly the HP2 (Hdelta) indices used to estimate [Fe/H] due to absorption from strong molecular bands.Comment: Accepted to the ApJL after a very lengthly duel with the 3 simultaneous referee

Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election

The 2020 United States presidential election was, and has continued to be, the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.Comment: Accepted for publication at ICWSM '2

The R-Process Alliance: Chemical Abundances for a Trio of R-Process-Enhanced Stars -- One Strong, One Moderate, One Mild

We present detailed chemical abundances of three new bright (V ~ 11), extremely metal-poor ([Fe/H] ~ -3.0), r-process-enhanced halo red giants based on high-resolution, high-S/N Magellan/MIKE spectra. We measured abundances for 20-25 neutron-capture elements in each of our stars. J1432-4125 is among the most r-process rich r-II stars, with [Eu/Fe]= +1.44+-0.11. J2005-3057 is an r-I star with [Eu/Fe] = +0.94+-0.07. J0858-0809 has [Eu/Fe] = +0.23+-0.05 and exhibits a carbon abundance corrected for evolutionary status of [C/Fe]_corr = +0.76, thus adding to the small number of known carbon-enhanced r-process stars. All three stars show remarkable agreement with the scaled solar r-process pattern for elements above Ba, consistent with enrichment of the birth gas cloud by a neutron star merger. The abundances for Sr, Y, and Zr, however, deviate from the scaled solar pattern. This indicates that more than one distinct r-process site might be responsible for the observed neutron-capture element abundance pattern. Thorium was detected in J1432-4125 and J2005-3057. Age estimates for J1432-4125 and J2005-3057 were adopted from one of two sets of initial production ratios each by assuming the stars are old. This yielded individual ages of 12+-6 Gyr and 10+-6 Gyr, respectively.Comment: 30 pages, includes a long table, 5 figure

Macrophages promote epithelial proliferation following infectious and non-infectious lung injury through a Trefoil factor 2-dependent mechanism.

Coordinated efforts between macrophages and epithelia are considered essential for wound healing, but the macrophage-derived molecules responsible for repair are poorly defined. This work demonstrates that lung macrophages rely upon Trefoil factor 2 to promote epithelial proliferation following damage caused by sterile wounding, Nippostrongylus brasiliensis or Bleomycin sulfate. Unexpectedly, the presence of T, B, or ILC populations was not essential for macrophage-driven repair. Instead, conditional deletion of TFF2 in myeloid-restricted CD11cCre TFF2 flox mice exacerbated lung pathology and reduced the proliferative expansion of CD45- EpCAM+ pro-SPC+ alveolar type 2 cells. TFF2 deficient macrophages had reduced expression of the Wnt genes Wnt4 and Wnt16 and reconstitution of hookworm-infected CD11cCre TFF2flox mice with rWnt4 and rWnt16 restored the proliferative defect in lung epithelia post-injury. These data reveal a previously unrecognized mechanism wherein lung myeloid phagocytes utilize a TFF2/Wnt axis as a mechanism that drives epithelial proliferation following lung injury

Model-based motion estimation for synthetic animations

One approach to performing motion estimation on syn-thetic animations is to treat them as video sequences and use standard image-based motion estimation meth-ods. Alternatively, we can take advantage of informa-tion used in rendering the animation to guide the motion estimation algorithm. This information includes the 3D movements of the objects in the scene and the projec-tion transformations from 3D world space into screen space. In this paper we examine how to use this high level object motion information to perform fast, accu-rate block-based motion estimation for synthetic anima-tions. The optical ow eld is a 2D vector eld describ-ing the translational motion of each pixel from frame to frame. Our motion estimation algorithm rst com-putes the optical ow eld, based on the object motion information. We then combine the per-pixel motion in-formation for a block of pixels to create a single 2D projective matrix that best encodes the motion of all the pixels in the block. The entries of the 2D matrix are determined using a least squares formulation. Our algo-rithms are more accurate and much faster in algorithmic complexity than many image-based motion estimation algorithms.