IRAS and ground-based observations of star formation regions in the Magellanic clouds

The Infrared Astronomy Satellite (IRAS) detected several hundred individual regions of star formation in the Large and Small Magellanic Clouds. Nearly two dozen of the brightest such sources were searched for from the ground at 10 microns; most of these were located and measured at wavelengths from 0.6 to 20 microns. Three principle results emerge from this study: First, the IRAS data show that star formation is considerably less active in the SMC than in the LMC, relative either to mass, luminosity, or H I content. The reduced activity in the SMC is consistent with the smaller amount of molecular material inferred from CO observations. Second, the sizes of the objects range from less than a few arcsecs (objects which look like extremely compact HII regions, with little or no extended radio, optical, or infrared emission) to some tens of arcsecs across (giant HII regions, of which the largest and brightest is 30 Doradus). Third, there are no obvious differences in the characteristics of the central portions of the LMC and SMC sources; all look like Galactic H II regions of similar luminosity

Bolometric luminosities and infrared properties of carbon stars in the Magellanic Clouds and the Galaxy

Broad band J, H, K photometry and narrow band CO and H_2O indices have been obtained for 89 luminous red stars in the Large Magellanic Cloud (LMC) and 21 in the Small Magellanic Cloud (SMC), chosen largely from the sample of Blanco, McCarthy, and Blanco. Most are known to be carbon stars, and their infrared properties are compared with new observations of 33 galactic carbon stars. The bolometric luminosity distributions of an unbiased sample of Magellanic Cloud carbon stars are compared with those predicted from evolutionary calculations by Renzini and Voli for double shell burning stars undergoing He shell flashes. The observed and theoretical distributions disagree markedly: nearly all the observed stars have lower luminosities than even the faintest theoretical carbon star. In addition, we find many fewer than expected high luminosity stars with initial mass greater than 3 M_⊙. Possible explanations for this include a steep initial mass function for intermediate mass stars, a star formation rate significantly higher in the past than at present, or a neglected physical effect, such as underestimation of the importance of mass loss. Nevertheless, it appears that the hypothesis that He shell flashes lead to a dredge-up of carbon into the envelope, which results in a carbon star, can be maintained, if dredging occurs after fewer shell flashes than are predicted by presently available stellar evolutionary calculations. The colors and indices of the late M giants in the LMC field are similar to those of late M giants in the Galaxy. The narrow band infrared data are interpreted qualitatively in terms of the effects of molecular band absorption, which also strongly influences the infrared broad band colors of carbon stars. The small differences in the color-color relationships of the SMC and LMC samples are consistent with the differences in heavy metal abundance between the LMC, SMC, and Galaxy

Bolometric luminosities and infrared properties of carbon stars in the Magellanic Clouds and the Galaxy

Broad band J, H, K photometry and narrow band CO and H_2O indices have been obtained for 89 luminous red stars in the Large Magellanic Cloud (LMC) and 21 in the Small Magellanic Cloud (SMC), chosen largely from the sample of Blanco, McCarthy, and Blanco. Most are known to be carbon stars, and their infrared properties are compared with new observations of 33 galactic carbon stars. The bolometric luminosity distributions of an unbiased sample of Magellanic Cloud carbon stars are compared with those predicted from evolutionary calculations by Renzini and Voli for double shell burning stars undergoing He shell flashes. The observed and theoretical distributions disagree markedly: nearly all the observed stars have lower luminosities than even the faintest theoretical carbon star. In addition, we find many fewer than expected high luminosity stars with initial mass greater than 3 M_⊙. Possible explanations for this include a steep initial mass function for intermediate mass stars, a star formation rate significantly higher in the past than at present, or a neglected physical effect, such as underestimation of the importance of mass loss. Nevertheless, it appears that the hypothesis that He shell flashes lead to a dredge-up of carbon into the envelope, which results in a carbon star, can be maintained, if dredging occurs after fewer shell flashes than are predicted by presently available stellar evolutionary calculations. The colors and indices of the late M giants in the LMC field are similar to those of late M giants in the Galaxy. The narrow band infrared data are interpreted qualitatively in terms of the effects of molecular band absorption, which also strongly influences the infrared broad band colors of carbon stars. The small differences in the color-color relationships of the SMC and LMC samples are consistent with the differences in heavy metal abundance between the LMC, SMC, and Galaxy

Natural climate solutions

Our thanks for inputs by L. Almond, A. Baccini, A. Bowman, S. CookPatton, J. Evans, K. Holl, R. Lalasz, A. Nassikas, M. Spalding, M. Wolosin, and expert elicitation respondents. Our thanks for datasets developed by the Hansen lab and the NESCent grasslands working group (C. Lehmann, D. Griffith, T. M. Anderson, D. J. Beerling, W. Bond, E. Denton, E. Edwards, E. Forrestel, D. Fox, W. Hoffmann, R. Hyde, T. Kluyver, L. Mucina, B. Passey, S. Pau, J. Ratnam, N. Salamin, B. Santini, K. Simpson, M. Smith, B. Spriggs, C. Still, C. Strömberg, and C. P. Osborne). This study was made possible by funding from the Doris Duke Charitable Foundation. Woodbury was supported in part by USDA-NIFA Project 2011-67003-30205 Data deposition: A global spatial dataset of reforestation opportunities has been deposited on Zenodo (https://zenodo.org/record/883444). This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1710465114/-/DCSupplemental.Peer reviewedPublisher PD

The Impact of Climate Change on Virginia\u27s Coastal Areas

As part of HJ47/SJ47 (2020), the Virginia General Assembly directed the Joint Commission on Technology and Science (JCOTS) to study the “safety, quality of life, and economic consequences of weather and climate-related events on coastal areas in Virginia.” In pursuit of this goal, the commission was to “accept any scientific and technical assistance provided by the nonpartisan, volunteer Virginia Academy of Science, Engineering, and Medicine (VASEM). VASEM convened an expert study board with representation from the Office of the Governor, planning district commissions in coastal Virginia, The Port of Virginia, the Virginia Economic Development Partnership, state universities, private industry, and law firms. In producing the report, the board followed methods similar to those used by the National Academies of Science, Engineering, and Medicine by convening an expert committee tasked with studying and reporting on the topic. As a result, the report represents the views and perspectives of the study board members but was not submitted for public review or comment. This report is the product of those efforts. It finds that climate change will have an increasingly disruptive effect on people living in Virginia’s coastal areas during the 21st century — and that these disruptions will have repercussions across the Commonwealth. It includes an explanation of the physical forces driving climate change, an analysis of the current and projected effects of climate change on the Commonwealth, perspectives that legislators might consider as they face these challenges, and recommendations that could help Virginia implement more productive and effective strategies to address them

Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality

Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-operative mortality. Methods. In a derivation cohort of 45,969 pre-operative patients (age 59+- 19 years, 55 percent women), a deep learning algorithm was developed to leverage waveform signals from pre-operative ECGs to discriminate post-operative mortality. Model performance was assessed in a holdout internal test dataset and in two external hospital cohorts and compared with the Revised Cardiac Risk Index (RCRI) score. Results. In the derivation cohort, there were 1,452 deaths. The algorithm discriminates mortality with an AUC of 0.83 (95% CI 0.79-0.87) surpassing the discrimination of the RCRI score with an AUC of 0.67 (CI 0.61-0.72) in the held out test cohort. Patients determined to be high risk by the deep learning model's risk prediction had an unadjusted odds ratio (OR) of 8.83 (5.57-13.20) for post-operative mortality as compared to an unadjusted OR of 2.08 (CI 0.77-3.50) for post-operative mortality for RCRI greater than 2. The deep learning algorithm performed similarly for patients undergoing cardiac surgery with an AUC of 0.85 (CI 0.77-0.92), non-cardiac surgery with an AUC of 0.83 (0.79-0.88), and catherization or endoscopy suite procedures with an AUC of 0.76 (0.72-0.81). The algorithm similarly discriminated risk for mortality in two separate external validation cohorts from independent healthcare systems with AUCs of 0.79 (0.75-0.83) and 0.75 (0.74-0.76) respectively. Conclusion. The findings demonstrate how a novel deep learning algorithm, applied to pre-operative ECGs, can improve discrimination of post-operative mortality

Epigenomic Mapping Reveals Distinct B Cell Acute Lymphoblastic Leukemia Chromatin Architectures and Regulators

B cell lineage acute lymphoblastic leukemia (B-ALL) is composed of diverse molecular subtypes, and while transcriptional and DNA methylation profiling has been extensively examined, the chromatin landscape is not well characterized for many subtypes. We therefore mapped chromatin accessibility using ATAC-seq in primary B-ALL cells from 156 patients spanning ten molecular subtypes and present this dataset as a resource. Differential chromatin accessibility and transcription factor (TF) footprint profiling were employed and identified B-ALL cell of origin, TF-target gene interactions enriched in B-ALL, and key TFs associated with accessible chromatin sites preferentially active in B-ALL. We further identified over 20% of accessible chromatin sites exhibiting strong subtype enrichment and candidate TFs that maintain subtype-specific chromatin architectures. Over 9,000 genetic variants were uncovered, contributing to variability in chromatin accessibility among patient samples. Our data suggest that distinct chromatin architectures are driven by diverse TFs and inherited genetic variants that promote unique gene-regulatory networks

PRN OPINION PAPER: Application of precision medicine across pharmacy specialty areas

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149551/1/jac51107_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149551/2/jac51107.pd

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