An Empirical Characterization of Extended Cool Gas Around Galaxies Using MgII Absorption Features

We report results from a survey of MgII absorbers in the spectra of background QSOs that are within close angular distances to a foreground galaxy at z<0.5, using the Magellan Echellette Spectrograph. We have established a spectroscopic sample of 94 galaxies at a median redshift of = 0.24 in fields around 70 distant background QSOs (z_QSO>0.6), 71 of which are in an 'isolated' environment with no known companions and located at rho <~ 120 h^-1 kpc from the line of sight of a background QSO. The rest-frame absolute B-band magnitudes span a range from M_B-5log h=-16.4 to M_B-5log h=-21.4 and rest-frame B_AB-R_AB colors range from B_AB-R_AB~0 to B_AB-R_AB~1.5. Of these 'isolated' galaxies, we find that 47 have corresponding MgII absorbers in the spectra of background QSOs and rest-frame absorption equivalent width W_r(2796)=0.1-2.34 A, and 24 do not give rise to MgII absorption to sensitive upper limits. Our analysis shows that (1) Wr(2796) declines with increasing distance from 'isolated' galaxies but shows no clear trend in 'group' environments; (2) more luminous galaxies possess more extended MgII absorbing halos with the gaseous radius scaled by B-band luminosity according to R_gas=75x(L_B/L_B*)^(0.35+/-0.03) h^{-1} kpc; (3) there is little dependence between the observed absorber strength and galaxy intrinsic colors; and (4) within R_gas, we find a mean covering fraction of ~70% for absorbers of Wr(2796)>=0.3 A and ~80% for absorbers of Wr(2796)>=0.1 A. The lack of correlation between Wr(2796) and galaxy colors suggests a lack of physical connection between the origin of extended MgII halos and recent star formation history of the galaxies. Finally, we discuss the total gas mass in galactic halos as traced by MgII absorbers. We also compare our results with previous studies.Comment: 20 pages, 13 figures; to appear in the Astrophysical Journal 2010 May 10 issue; a version with higher resolution figures can be found at http://lambda.uchicago.edu/public/tmp/mage_apj.pd

Ubiquitous outflows in DEEP2 spectra of star-forming galaxies at z=1.4

Galactic winds are a prime suspect for the metal enrichment of the intergalactic medium and may have a strong influence on the chemical evolution of galaxies and the nature of QSO absorption line systems. We use a sample of 1406 galaxy spectra at z~1.4 from the DEEP2 redshift survey to show that blueshifted Mg II 2796, 2803 A absorption is ubiquitous in starforming galaxies at this epoch. This is the first detection of frequent outflowing galactic winds at z~1. The presence and depth of absorption are independent of AGN spectral signatures or galaxy morphology; major mergers are not a prerequisite for driving a galactic wind from massive galaxies. Outflows are found in coadded spectra of galaxies spanning a range of 30x in stellar mass and 10x in star formation rate (SFR), calibrated from K-band and from MIPS IR fluxes. The outflows have column densities of order N_H ~ 10^20 cm^-2 and characteristic velocities of ~ 300-500 km/sec, with absorption seen out to 1000 km/sec in the most massive, highest SFR galaxies. The velocities suggest that the outflowing gas can escape into the IGM and that massive galaxies can produce cosmologically and chemically significant outflows. Both the Mg II equivalent width and the outflow velocity are larger for galaxies of higher stellar mass and SFR, with V_wind ~ SFR^0.3, similar to the scaling in low redshift IR-luminous galaxies. The high frequency of outflows in the star-forming galaxy population at z~1 indicates that galactic winds occur in the progenitors of massive spirals as well as those of ellipticals. The increase of outflow velocity with mass and SFR constrains theoretical models of galaxy evolution that include feedback from galactic winds, and may favor momentum-driven models for the wind physics.Comment: Accepted by ApJ. 25 pages, 17 figures. Revised to add discussions of intervening absorbers and AGN-driven outflows; conclusions unchange

The Reading Palaeofire Database: an expanded global resource to document changes in fire regimes from sedimentary charcoal records

Sedimentary charcoal records are widely used to reconstruct regional changes in fire regimes through time in the geological past. Existing global compilations are not geographically comprehensive and do not provide consistent metadata for all sites. Furthermore, the age models provided for these records are not harmonised and many are based on older calibrations of the radiocarbon ages. These issues limit the use of existing compilations for research into past fire regimes. Here, we present an expanded database of charcoal records, accompanied by new age models based on recalibration of radiocarbon ages using IntCal20 and Bayesian age-modelling software. We document the structure and contents of the database, the construction of the age models, and the quality control measures applied. We also record the expansion of geographical coverage relative to previous charcoal compilations and the expansion of metadata that can be used to inform analyses. This first version of the Reading Palaeofire Database contains 1676 records (entities) from 1480 sites worldwide. The database (RPDv1b – Harrison et al., 2021) is available at https://doi.org/10.17864/1947.000345

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Increasing fire and the decline of fire adapted black spruce in the boreal forest

Intensifying wildfire activity and climate change can drive rapid forest compositional shifts. In boreal North America, black spruce shapes forest flammability and depends on fire for regeneration. This relationship has helped black spruce maintain its dominance through much of the Holocene. However, with climate change and more frequent and severe fires, shifts away from black spruce dominance to broadleaf or pine species are emerging, with implications for ecosystem functions including carbon sequestration, water and energy fluxes, and wildlife habitat. Here, we predict that such reductions in black spruce after fire may already be widespread given current trends in climate and fire. To test this, we synthesize data from 1,538 field sites across boreal North America to evaluate compositional changes in tree species following 58 recent fires (1989 to 2014). While black spruce was resilient following most fires (62%), loss of resilience was common, and spruce regeneration failed completely in 18% of 1,140 black spruce sites. In contrast, postfire regeneration never failed in forests dominated by jack pine, which also possesses an aerial seed bank, or broad-leaved trees. More complete combustion of the soil organic layer, which often occurs in better-drained landscape positions and in dryer duff, promoted compositional changes throughout boreal North America. Forests in western North America, however, were more vulnerable to change due to greater long-term climate moisture deficits. While we find considerable remaining resilience in black spruce forests, predicted increases in climate moisture deficits and fire activity will erode this resilience, pushing the system toward a tipping point that has not been crossed in several thousand years

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships