@article{AbdallaAbramowskiAharonianetal.2017,
  author    = {Abdalla, Hassan E. and Abramowski, Attila and Aharonian, Felix A. and Benkhali, Faical Ait and Akhperjanian, A. G. and Andersson, T. and Anguner, Ekrem Oǧuzhan and Arakawa, M. and Arrieta, M. and Aubert, Pierre and Backes, Michael and Balzer, Arnim and Barnard, Michelle and Becherini, Yvonne and Tjus, J. Becker and Berge, David and Bernhard, Sabrina and Bernl{\"o}hr, K. and Blackwell, R. and B{\"o}ttcher, Markus and Boisson, Catherine and Bolmont, J. and Bonnefoy, S. and Bordas, Pol and Bregeon, Johan and Brun, Francois and Brun, Pierre and Bryan, Mark and Buechele, M. and Bulik, Tomasz and Capasso, M. and Carr, John and Casanova, Sabrina and Cerruti, M. and Chakraborty, N. and Chaves, Ryan C. G. and Chen, Andrew and Chevalier, J. and Coffaro, M. and Colafrancesco, Sergio and Cologna, Gabriele and Condon, B. and Conrad, Jan and Cui, Y. and Davids, I. D. and Decock, J. and Degrange, B. and Deil, C. and Devin, J. and de Wilt, P. and Dirson, L. and Djannati-Atai, A. and Domainko, W. and Donath, A. and Dutson, K. and Dyks, J. and Edwards, T. and Egberts, Kathrin and Eger, P. and Ernenwein, J. -P. and Eschbach, S. and Farnier, C. and Fegan, S. and Fernandes, M. V. and Fiasson, A. and Fontaine, G. and Foerster, A. and Funk, S. and Fuessling, M. and Gabici, S. and Gallant, Y. A. and Garrigoux, T. and Giavitto, G. and Giebels, B. and Glicenstein, J. F. and Gottschall, D. and Goyal, A. and Grondin, M. -H. and Hahn, J. and Haupt, M. and Hawkes, J. and Heinzelmann, G. and Henri, G. and Hermann, G. and Hinton, James Anthony and Hofmann, W. and Hoischen, Clemens and Holch, Tim Lukas and Holler, M. and Horns, D. and Ivascenko, A. and Iwasaki, H. and Jacholkowska, A. and Jamrozy, M. and Janiak, M. and Jankowsky, D. and Jankowsky, F. and Jingo, M. and Jogler, T. and Jouvin, L. and Jung-Richardt, I. and Kastendieck, M. A. and Katarzynski, K. and Katsuragawa, M. and Katz, U. and Kerszberg, D. and Khangulyan, D. and Khelifi, B. and King, J. and Klepser, S. and Klochkov, D. and Kluzniak, W. and Kolitzus, D. and Komin, Nu. and Kosack, K. and Krakau, S. and Kraus, M. and Kruger, P. P. and Laffon, H. and Lamanna, G. and Lau, J. and Lees, J. -P. and Lefaucheur, J. and Lefranc, V. and Lemiere, A. and Lemoine-Goumard, M. and Lenain, J. -P. and Leser, Eva and Lohse, T. and Lorentz, M. and Liu, R. and Lopez-Coto, R. and Lypova, I. and Marandon, V. and Marcowith, Alexandre and Mariaud, C. and Marx, R. and Maurin, G. and Maxted, N. and Mayer, M. and Meintjes, P. J. and Meyer, M. and Mitchell, A. M. W. and Moderski, R. and Mohamed, M. and Mohrmann, L. and Mora, K. and Moulin, Emmanuel and Murach, T. and Nakashima, S. and de Naurois, M. and Niederwanger, F. and Niemiec, J. and Oakes, L. and Odaka, H. and Ohm, S. and Ostrowski, M. and Oya, I. and Padovani, M. and Panter, M. and Parsons, R. D. and Pekeur, N. W. and Pelletier, G. and Perennes, C. and Petrucci, P. -O. and Peyaud, B. and Piel, Q. and Pita, S. and Poon, H. and Prokhorov, D. and Prokoph, H. and Puehlhofer, G. and Punch, M. and Quirrenbach, A. and Raab, S. and Rauth, R. and Reimer, A. and Reimer, O. and Renaud, M. and de los Reyes, R. and Richter, S. and Rieger, F. and Romoli, C. and Rowell, G. and Rudak, B. and Rulten, C. B. and Sahakian, V. and Saito, S. and Salek, D. and Sanchez, David M. and Santangelo, A. and Sasaki, M. and Schlickeiser, R. and Schussler, F. and Schulz, A. and Schwanke, U. and Schwemmer, S. and Seglar-Arroyo, M. and Settimo, M. and Seyffert, A. S. and Shafi, N. and Shilon, I. and Simoni, R. and Sol, H. and Spanier, F. and Spengler, G. and Spies, F. and Stawarz, L. and Steenkamp, R. and Stegmann, Christian and Stycz, K. and Sushch, Iurii and Takahashi, T. and Tavernet, J. -P. and Tavernier, T. and Taylor, A. M. and Terrier, R. and Tibaldo, L. and Tiziani, D. and Tluczykont, M. and Trichard, C. and Tsuji, N. and Tuffs, R. and Uchiyama, Y. and van der Walt, D. J. and van Eldik, C. and van Rensburg, C. and van Soelen, B. and Vasileiadis, G. and Veh, J. and Venter, C. and Viana, A. and Vincent, P. and Vink, J. and Voisin, F. and Voelk, H. J. and Vuillaume, T. and Wadiasingh, Z. and Wagner, S. J. and Wagner, P. and Wagner, R. M. and White, R. and Wierzcholska, A. and Willmann, P. and Woernlein, A. and Wouters, D. and Yang, R. and Zaborov, D. and Zacharias, M. and Zanin, R. and Zdziarski, A. A. and Zech, Alraune and Zefi, F. and Ziegler, A. and Zywucka, N.},
  title     = {Measurement of the EBL spectral energy distribution using the VHE gamma-ray spectra of HESS blazars},
  series = {Astronomy and astrophysics : an international weekly journal},
  volume    = {606},
  journal   = {Astronomy and astrophysics : an international weekly journal},
  publisher = {EDP Sciences},
  address   = {Les Ulis},
  organization    = {HESS Collaboration},
  issn      = {1432-0746},
  doi       = {10.1051/0004-6361/201731200},
  pages     = {11},
  year      = {2017},
  abstract  = {Very high-energy gamma rays (VHE, E greater than or similar to 100 GeV) propagating over cosmological distances can interact with the low-energy photons of the extragalactic background light (EBL) and produce electron-positron pairs. The transparency of the Universe to VHE gamma rays is then directly related to the spectral energy distribution (SED) of the EBL. The observation of features in the VHE energy spectra of extragalactic sources allows the EBL to be measured, which otherwise is very difficult. An EBL model-independent measurement of the EBL SED with the H.E.S.S. array of Cherenkov telescopes is presented. It was obtained by extracting the EBL absorption signal from the reanalysis of high-quality spectra of blazars. From H.E.S.S. data alone the EBL signature is detected at a significance of 9.5 sigma, and the intensity of the EBL obtained in different spectral bands is presented together with the associated gamma-ray horizon.},
  language  = {en}
}
@misc{HinzLoefflerDeekenetal.2021,
  author    = {Hinz, Carsten and L{\"o}ffler, Robert and Deeken, Johannes and Hansen, Barbara and Huhn, Nicola and Klitsch, Constantin and Kost, Andr{\´e} and Penning, Isabelle and Richter, Christin and Sch{\"a}fer, David and Schulz, Oliver and Simon, Veronika and Tuncel, Teresa},
  title     = {\#Politik Wirtschaft - Nordrhein-Westfalen. Band 7/8},
  publisher = {Buchner},
  address   = {Bamberg},
  isbn      = {978-3-661-70077-9},
  pages     = {400},
  year      = {2021},
  abstract  = {Seit dem Schuljahr 2020/21 gilt in Nordrhein-Westfalen ein neuer Kernlehrplan f{\"u}r die Realschule, Gesamtschule und Sekundarschule. Daf{\"u}r haben wir gemeinsam mit Fachkr{\"a}ften aus dem Bundesland die \#-Schulbuchreihen entwickelt. Mit \#Politik Wirtschaft - Nordrhein-Westfalen bieten wir Ihnen innovative und aktuelle Produkte f{\"u}r einen modernen Politik- und Wirtschaftsunterricht. Neben dem neuen Lehrplan sind die Vorgaben des Medienkompetenzrahmens und die besonderen Herausforderungen heterogener Lerngruppen ber{\"u}cksichtigt. Wir bieten Ihnen einen problemorientierten und sch{\"u}lernahen Unterricht. Die Rubrik "Gemeinsam aktiv" erm{\"o}glicht ein selbstgesteuertes Lernen. Die Sch{\"u}lerinnen und Sch{\"u}ler erarbeiten sich projektartig gr{\"o}ßere Einheiten eines Kapitels. Sie k{\"o}nnen Ihren Unterricht einfach und schnell besonders vielf{\"a}ltig und spannend gestalten. Durch Fallbeispiele werden die Sch{\"u}lerinnen und Sch{\"u}ler direkt angesprochen. Eine kreative Vielfalt aus Bild-, Grafik- und Textmaterial, aktivierende Aufgaben, Methoden-und Grundwissenseiten und ein Kompetenzcheck zum Abschluss der Großkapitel vervollst{\"a}ndigen das Angebot. Zu jeder Unterrichtseinheit wird passgenau zum Schulbuch unterschiedliches Differenzierungsmaterial (Texte in einfacher Sprache, Vorstrukturierung von Aufgaben u.v.m.) erstellt. Dieses steht Ihnen in unserem digitalen Lehrermaterial click \& teach zur Verf{\"u}gung und kann von Ihnen nach individuellen Bed{\"u}rfnissen f{\"u}r einzelne digitale Schulb{\"u}cher click \& study freigeschaltet werden.},
  language  = {de}
}
@book{DeekenHinzKlitschetal.2022,
  author    = {Deeken, Johannes and Hinz, Carsten and Klitsch, Constantin and L{\"o}ffler, Robert and Penning, Isabelle and Richter, Christin and Sch{\"a}fer, David},
  title     = {\#Wirtschaft - Nordrhein-Westfalen},
  number    = {7/8},
  editor    = {Kirchner, Vera},
  publisher = {Buchner},
  address   = {Bamberg},
  isbn      = {978-3-661-82252-5},
  pages     = {192},
  year      = {2022},
  language  = {de}
}
@book{KirchnerDeekenHinzetal.2022,
  author    = {Kirchner, Vera and Deeken, Johannes and Hinz, Carsten and Klitsch, Constantin and L{\"o}ffler, Robert and Penning, Isabelle and Richter, Christin and Sch{\"a}fer, David},
  title     = {Differenzierungsheft},
  series = {\#Wirtschaft Band 7/8 - Nordrhein-Westfalen},
  journal   = {\#Wirtschaft Band 7/8 - Nordrhein-Westfalen},
  publisher = {Buchner},
  address   = {Bamberg},
  isbn      = {978-3-66182-249-5},
  pages     = {56},
  year      = {2022},
  language  = {de}
}
@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}
}
@article{ChanChaudharySahaetal.2021,
  author    = {Chan, Lili and Chaudhary, Kumardeep and Saha, Aparna and Chauhan, Kinsuk and Vaid, Akhil and Zhao, Shan and Paranjpe, Ishan and Somani, Sulaiman and Richter, Felix and Miotto, Riccardo and Lala, Anuradha and Kia, Arash and Timsina, Prem and Li, Li and Freeman, Robert and Chen, Rong and Narula, Jagat and Just, Allan C. and Horowitz, Carol and Fayad, Zahi and Cordon-Cardo, Carlos and Schadt, Eric and Levin, Matthew A. and Reich, David L. and Fuster, Valentin and Murphy, Barbara and He, John C. and Charney, Alexander W. and B{\"o}ttinger, Erwin and Glicksberg, Benjamin and Coca, Steven G. and Nadkarni, Girish N.},
  title     = {AKI in hospitalized patients with COVID-19},
  series = {Journal of the American Society of Nephrology : JASN},
  volume    = {32},
  journal   = {Journal of the American Society of Nephrology : JASN},
  number    = {1},
  publisher = {American Society of Nephrology},
  address   = {Washington},
  organization    = {Mt Sinai COVID Informatics Ct},
  issn      = {1046-6673},
  doi       = {10.1681/ASN.2020050615},
  pages     = {151 -- 160},
  year      = {2021},
  abstract  = {Background: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associatedwith worse outcomes. However, AKI among hospitalized patients with COVID19 in the United States is not well described. Methods: This retrospective, observational study involved a review of data from electronic health records of patients aged >= 18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. Results: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46\%) patients; 347 (19\%) of the patientswith AKI required dialysis. The proportionswith stages 1, 2, or 3 AKIwere 39\%, 19\%, and 42\%, respectively. A total of 976 (24\%) patients were admitted to intensive care, and 745 (76\%) experienced AKI. Of the 435 patients with AKI and urine studies, 84\% had proteinuria, 81\% had hematuria, and 60\% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50\% among patients with AKI versus 8\% among those without AKI (aOR, 9.2; 95\% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35\% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36\%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. Conclusions: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30\% survived with recovery of kidney function by the time of discharge.},
  language  = {en}
}
@misc{BeneckeDeekenHammeretal.2020,
  author    = {Benecke, Karin and Deeken, Johannes and Hammer, Carolin and Hinz, Carsten and L{\"o}ffler, Robert and Penning, Isabelle and Richter, Christin and Sch{\"a}fer, David and Scherer, Hubertus},
  title     = {\#Wirtschaft - Niedersachsen},
  editor    = {Kirchner, Vera},
  publisher = {Buchner},
  address   = {Bamberg},
  isbn      = {978-3-661-82241-9},
  pages     = {320},
  year      = {2020},
  language  = {de}
}
@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}
}