@article{vanLoonBaileyTattonetal.2013,
  author    = {van Loon, Jacco Th. and Bailey, M. and Tatton, B. L. and Apellaniz, Jesus Maiz and Crowther, P. A. and de Koter, A. and Evans, C. J. and Henault-Brunet, V. and Howarth, I. D. and Richter, Philipp and Sana, Hugues and Simon D{\´i}az, Sergio and Taylor, W. and Walborn, N. R.},
  title     = {The VLT-FLAMES tarantula survey IX. - the interstellar medium seen through diffuse interstellar bands and neutral sodium},
  series = {Astronomy and astrophysics : an international weekly journal},
  volume    = {550},
  journal   = {Astronomy and astrophysics : an international weekly journal},
  number    = {9},
  publisher = {EDP Sciences},
  address   = {Les Ulis},
  issn      = {0004-6361},
  doi       = {10.1051/0004-6361/201220210},
  pages     = {21},
  year      = {2013},
  abstract  = {Context. The Tarantula Nebula (a.k.a. 30 Dor) is a spectacular star-forming region in the Large Magellanic Cloud (LMC), seen through gas in the Galactic disc and halo. Diffuse interstellar bands (DIBs) offer a unique probe of the diffuse, cool-warm gas in these regions. Aims. The aim is to use DIBs as diagnostics of the local interstellar conditions, whilst at the same time deriving properties of the yet-unknown carriers of these enigmatic spectral features. Methods. Spectra of over 800 early-type stars from the Very Large Telescope Flames Tarantula Survey (VFTS) were analysed. Maps were created, separately, for the Galactic and LMC absorption in the DIBs at 4428 and 6614 angstrom and - in a smaller region near the central cluster R 136 - neutral sodium (the Na ID doublet); we also measured the DIBs at 5780 and 5797 angstrom. Results. The maps show strong 4428 and 6614 angstrom DIBs in the quiescent cloud complex to the south of 30 Dor but weak absorption in the harsher environments to the north (bubbles) and near the OB associations. The Na maps show at least five kinematic components in the LMC and a shell-like structure surrounding R 136, and small-scale structure in the Milky Way. The strengths of the 4428, 5780, 5797 and 6614 angstrom DIBs are correlated, also with Na absorption and visual extinction. The strong 4428 angstrom DIB is present already at low Na column density but the 6614, 5780 and 5797 angstrom DIBs start to be detectable at subsequently larger Na column densities. Conclusions. The carriers of the 4428, 6614, 5780 and 5797 angstrom DIBs are increasingly prone to removal from irradiated gas. The relative strength of the 5780 and 5797 angstrom DIBs clearly confirm the Tarantula Nebula as well as Galactic high-latitude gas to represent a harsh radiation environment. The resilience of the 4428 angstrom DIB suggests its carrier is large, compact and neutral. Structure is detected in the distribution of cool-warm gas on scales between one and > 100 pc in the LMC and as little as 0.01 pc in the Sun's vicinity. Stellar winds from the central cluster R 136 have created an expanding shell; some infalling gas is also detected, reminiscent of a galactic "fountain".},
  language  = {en}
}
@article{VaidSomaniRussaketal.2020,
  author    = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.},
  title     = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation},
  series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  volume    = {22},
  journal   = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR},
  number    = {11},
  publisher = {Healthcare World},
  address   = {Richmond, Va.},
  issn      = {1439-4456},
  doi       = {10.2196/24018},
  pages     = {19},
  year      = {2020},
  abstract  = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.},
  language  = {en}
}
@techreport{AdriaansGrieseAuspurgetal.2021,
  author    = {Adriaans, Jule and Griese, Florian and Auspurg, Katrin and Bledow, Nona and Bohmann, Sandra and Busemeyer, Marius R. and Delhey, Jan and Goebel, Jan and Groh-Samberg, Olaf and Heckhausen, Jutta and Hinz, Thomas and Kroh, Martin and Lengfeld, Holger and Lersch, Philipp M. and Liebig, Stefan and Richter, David and Sachweh, Patrick and Schupp, J{\"u}rgen and Schwerdt, Guido and Verwiebe, Roland},
  title     = {Dokumentation zum Entwicklungsprozess des Moduls Einstellungen zu sozialer Ungleichheit im SOEP (v38)},
  series = {SOEP survey papers, series B - survey reports (Methodenberichte)},
  volume    = {1071},
  journal   = {SOEP survey papers, series B - survey reports (Methodenberichte)},
  publisher = {Deutsches Institut f{\"u}r Wirtschaftsforschung (DIW)},
  address   = {Berlin},
  issn      = {2193-5580},
  pages     = {35},
  year      = {2021},
  abstract  = {Im Rahmen eines einj{\"a}hrigen Entwicklungsprozesses wurde das Fragebogenmodul "Einstellungen zu sozialer Ungleichheit" unter der Leitung der Infrastruktureinrichtung SOEP entwickelt und in der 38. Welle der Haupterhebung des Sozio-oekonomischen Panels erstmalig erhoben. Das finale Fragebogenmodul umfasst 43 Items zu den Themenbereichen Soziale Vergleiche, Soziale Mobilit{\"a}t, Sozialstaat und Nicht-materielle Ungleichheit. In der Tradition des SOEP als forschungsbasierte Infrastruktureinrichtung erfolgte die Fragebogenentwicklung in enger Zusammenarbeit mit externen Forschenden aus dem Bereich der Einstellungs- und Ungleichheitsforschung. Neben der etablierten Nutzung des SOEP Innovation Samples (SOEP-IS) f{\"u}r quantitative Pretests neu entwickelter Fragen kam erstmals ein kognitiver Pretest zum Einsatz. Der vorliegende Bericht dokumentiert den Entwicklungsprozess von der Konzeption bis zum finalen Fragebogen.},
  language  = {de}
}