TY  - JOUR
A1  - Abdalla, Hassan E.
A1  - Abramowski, Attila
A1  - Aharonian, Felix A.
A1  - Benkhali, Faical  Ait
A1  - Akhperjanian, A. G.
A1  - Andersson, T.
A1  - Anguner, Ekrem Oǧuzhan
A1  - Arakawa, M.
A1  - Arrieta, M.
A1  - Aubert, Pierre
A1  - Backes, Michael
A1  - Balzer, Arnim
A1  - Barnard, Michelle
A1  - Becherini, Yvonne
A1  - Tjus, J. Becker
A1  - Berge, David
A1  - Bernhard, Sabrina
A1  - Bernlöhr, K.
A1  - Blackwell, R.
A1  - Böttcher, Markus
A1  - Boisson, Catherine
A1  - Bolmont, J.
A1  - Bonnefoy, S.
A1  - Bordas, Pol
A1  - Bregeon, Johan
A1  - Brun, Francois
A1  - Brun, Pierre
A1  - Bryan, Mark
A1  - Buechele, M.
A1  - Bulik, Tomasz
A1  - Capasso, M.
A1  - Carr, John
A1  - Casanova, Sabrina
A1  - Cerruti, M.
A1  - Chakraborty, N.
A1  - Chaves, Ryan C. G.
A1  - Chen, Andrew
A1  - Chevalier, J.
A1  - Coffaro, M.
A1  - Colafrancesco, Sergio
A1  - Cologna, Gabriele
A1  - Condon, B.
A1  - Conrad, Jan
A1  - Cui, Y.
A1  - Davids, I. D.
A1  - Decock, J.
A1  - Degrange, B.
A1  - Deil, C.
A1  - Devin, J.
A1  - de Wilt, P.
A1  - Dirson, L.
A1  - Djannati-Atai, A.
A1  - Domainko, W.
A1  - Donath, A.
A1  - Dutson, K.
A1  - Dyks, J.
A1  - Edwards, T.
A1  - Egberts, Kathrin
A1  - Eger, P.
A1  - Ernenwein, J. -P.
A1  - Eschbach, S.
A1  - Farnier, C.
A1  - Fegan, S.
A1  - Fernandes, M. V.
A1  - Fiasson, A.
A1  - Fontaine, G.
A1  - Foerster, A.
A1  - Funk, S.
A1  - Fuessling, M.
A1  - Gabici, S.
A1  - Gallant, Y. A.
A1  - Garrigoux, T.
A1  - Giavitto, G.
A1  - Giebels, B.
A1  - Glicenstein, J. F.
A1  - Gottschall, D.
A1  - Goyal, A.
A1  - Grondin, M. -H.
A1  - Hahn, J.
A1  - Haupt, M.
A1  - Hawkes, J.
A1  - Heinzelmann, G.
A1  - Henri, G.
A1  - Hermann, G.
A1  - Hinton, James Anthony
A1  - Hofmann, W.
A1  - Hoischen, Clemens
A1  - Holch, Tim Lukas
A1  - Holler, M.
A1  - Horns, D.
A1  - Ivascenko, A.
A1  - Iwasaki, H.
A1  - Jacholkowska, A.
A1  - Jamrozy, M.
A1  - Janiak, M.
A1  - Jankowsky, D.
A1  - Jankowsky, F.
A1  - Jingo, M.
A1  - Jogler, T.
A1  - Jouvin, L.
A1  - Jung-Richardt, I.
A1  - Kastendieck, M. A.
A1  - Katarzynski, K.
A1  - Katsuragawa, M.
A1  - Katz, U.
A1  - Kerszberg, D.
A1  - Khangulyan, D.
A1  - Khelifi, B.
A1  - King, J.
A1  - Klepser, S.
A1  - Klochkov, D.
A1  - Kluzniak, W.
A1  - Kolitzus, D.
A1  - Komin, Nu.
A1  - Kosack, K.
A1  - Krakau, S.
A1  - Kraus, M.
A1  - Kruger, P. P.
A1  - Laffon, H.
A1  - Lamanna, G.
A1  - Lau, J.
A1  - Lees, J. -P.
A1  - Lefaucheur, J.
A1  - Lefranc, V.
A1  - Lemiere, A.
A1  - Lemoine-Goumard, M.
A1  - Lenain, J. -P.
A1  - Leser, Eva
A1  - Lohse, T.
A1  - Lorentz, M.
A1  - Liu, R.
A1  - Lopez-Coto, R.
A1  - Lypova, I.
A1  - Marandon, V.
A1  - Marcowith, Alexandre
A1  - Mariaud, C.
A1  - Marx, R.
A1  - Maurin, G.
A1  - Maxted, N.
A1  - Mayer, M.
A1  - Meintjes, P. J.
A1  - Meyer, M.
A1  - Mitchell, A. M. W.
A1  - Moderski, R.
A1  - Mohamed, M.
A1  - Mohrmann, L.
A1  - Mora, K.
A1  - Moulin, Emmanuel
A1  - Murach, T.
A1  - Nakashima, S.
A1  - de Naurois, M.
A1  - Niederwanger, F.
A1  - Niemiec, J.
A1  - Oakes, L.
A1  - Odaka, H.
A1  - Ohm, S.
A1  - Ostrowski, M.
A1  - Oya, I.
A1  - Padovani, M.
A1  - Panter, M.
A1  - Parsons, R. D.
A1  - Pekeur, N. W.
A1  - Pelletier, G.
A1  - Perennes, C.
A1  - Petrucci, P. -O.
A1  - Peyaud, B.
A1  - Piel, Q.
A1  - Pita, S.
A1  - Poon, H.
A1  - Prokhorov, D.
A1  - Prokoph, H.
A1  - Puehlhofer, G.
A1  - Punch, M.
A1  - Quirrenbach, A.
A1  - Raab, S.
A1  - Rauth, R.
A1  - Reimer, A.
A1  - Reimer, O.
A1  - Renaud, M.
A1  - de los Reyes, R.
A1  - Richter, S.
A1  - Rieger, F.
A1  - Romoli, C.
A1  - Rowell, G.
A1  - Rudak, B.
A1  - Rulten, C. B.
A1  - Sahakian, V.
A1  - Saito, S.
A1  - Salek, D.
A1  - Sanchez, David M.
A1  - Santangelo, A.
A1  - Sasaki, M.
A1  - Schlickeiser, R.
A1  - Schussler, F.
A1  - Schulz, A.
A1  - Schwanke, U.
A1  - Schwemmer, S.
A1  - Seglar-Arroyo, M.
A1  - Settimo, M.
A1  - Seyffert, A. S.
A1  - Shafi, N.
A1  - Shilon, I.
A1  - Simoni, R.
A1  - Sol, H.
A1  - Spanier, F.
A1  - Spengler, G.
A1  - Spies, F.
A1  - Stawarz, L.
A1  - Steenkamp, R.
A1  - Stegmann, Christian
A1  - Stycz, K.
A1  - Sushch, Iurii
A1  - Takahashi, T.
A1  - Tavernet, J. -P.
A1  - Tavernier, T.
A1  - Taylor, A. M.
A1  - Terrier, R.
A1  - Tibaldo, L.
A1  - Tiziani, D.
A1  - Tluczykont, M.
A1  - Trichard, C.
A1  - Tsuji, N.
A1  - Tuffs, R.
A1  - Uchiyama, Y.
A1  - van der Walt, D. J.
A1  - van Eldik, C.
A1  - van Rensburg, C.
A1  - van Soelen, B.
A1  - Vasileiadis, G.
A1  - Veh, J.
A1  - Venter, C.
A1  - Viana, A.
A1  - Vincent, P.
A1  - Vink, J.
A1  - Voisin, F.
A1  - Voelk, H. J.
A1  - Vuillaume, T.
A1  - Wadiasingh, Z.
A1  - Wagner, S. J.
A1  - Wagner, P.
A1  - Wagner, R. M.
A1  - White, R.
A1  - Wierzcholska, A.
A1  - Willmann, P.
A1  - Woernlein, A.
A1  - Wouters, D.
A1  - Yang, R.
A1  - Zaborov, D.
A1  - Zacharias, M.
A1  - Zanin, R.
A1  - Zdziarski, A. A.
A1  - Zech, Alraune
A1  - Zefi, F.
A1  - Ziegler, A.
A1  - Zywucka, N.
T1  - Measurement of the EBL spectral energy distribution using the VHE gamma-ray spectra of HESS blazars
JF  - Astronomy and astrophysics : an international weekly journal
N2  - 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.
KW  - gamma rays: galaxies
KW  - BL Lacertae objects: general
KW  - cosmic background radiation
KW  - infrared: diffuse background
Y1  - 2017
U6  - https://doi.org/10.1051/0004-6361/201731200
SN  - 1432-0746
VL  - 606
PB  - EDP Sciences
CY  - Les Ulis
ER  - 
TY  - GEN
A1  - Hinz, Carsten
A1  - Löffler, Robert
A1  - Deeken, Johannes
A1  - Hansen, Barbara
A1  - Huhn, Nicola
A1  - Klitsch, Constantin
A1  - Kost, André
A1  - Penning, Isabelle
A1  - Richter, Christin
A1  - Schäfer, David
A1  - Schulz, Oliver
A1  - Simon, Veronika
A1  - Tuncel, Teresa
T1  - #Politik Wirtschaft – Nordrhein-Westfalen. Band 7/8
BT  - Wirtschaft für die Realschule, Gesamtschule und Sekundarschule
N2  - Seit dem Schuljahr 2020/21 gilt in Nordrhein-Westfalen ein neuer Kernlehrplan für die Realschule, Gesamtschule und Sekundarschule. Dafür haben wir gemeinsam mit Fachkräften aus dem Bundesland die #-Schulbuchreihen entwickelt.

Mit #Politik Wirtschaft – Nordrhein-Westfalen bieten wir Ihnen innovative und aktuelle Produkte für einen modernen Politik- und Wirtschaftsunterricht. Neben dem neuen Lehrplan sind die Vorgaben des Medienkompetenzrahmens und die besonderen Herausforderungen heterogener Lerngruppen berücksichtigt.


Wir bieten Ihnen einen problemorientierten und schülernahen Unterricht. Die Rubrik ”Gemeinsam aktiv“ ermöglicht ein selbstgesteuertes Lernen. Die Schülerinnen und Schüler erarbeiten sich projektartig größere Einheiten eines Kapitels. Sie können Ihren Unterricht einfach und schnell besonders vielfältig und spannend gestalten.

Durch Fallbeispiele werden die Schülerinnen und Schüler direkt angesprochen. Eine kreative Vielfalt aus Bild-, Grafik- und Textmaterial, aktivierende Aufgaben, Methoden-und Grundwissenseiten und ein Kompetenzcheck zum Abschluss der Großkapitel vervollständigen 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 Verfügung und kann von Ihnen nach individuellen Bedürfnissen für einzelne digitale Schulbücher click & study freigeschaltet werden.
Y1  - 2021
SN  - 978-3-661-70077-9
PB  - Buchner
CY  - Bamberg
ER  - 
TY  - BOOK
A1  - Deeken, Johannes
A1  - Hinz, Carsten
A1  - Klitsch, Constantin
A1  - Löffler, Robert
A1  - Penning, Isabelle
A1  - Richter, Christin
A1  - Schäfer, David
ED  - Kirchner, Vera
T1  - #Wirtschaft – Nordrhein-Westfalen
BT  - #Wirtschaft NRW 7/8 : Wirtschaft für die Realschule, Gesamtschule und Sekundarschule
Y1  - 2022
SN  - 978-3-661-82252-5
IS  - 7/8
PB  - Buchner
CY  - Bamberg
ER  - 
TY  - BOOK
A1  - Kirchner, Vera
A1  - Deeken, Johannes
A1  - Hinz, Carsten
A1  - Klitsch, Constantin
A1  - Löffler, Robert
A1  - Penning, Isabelle
A1  - Richter, Christin
A1  - Schäfer, David
T1  - Differenzierungsheft
BT  - Materialien für einen differenzierenden und sprachsensiblen Unterricht
T3  - #Wirtschaft Band 7/8 - Nordrhein-Westfalen
Y1  - 2022
SN  - 978-3-66182-249-5
PB  - Buchner
CY  - Bamberg
ER  - 
TY  - JOUR
A1  - Vaid, Akhil
A1  - Somani, Sulaiman
A1  - Russak, Adam J.
A1  - De Freitas, Jessica K.
A1  - Chaudhry, Fayzan F.
A1  - Paranjpe, Ishan
A1  - Johnson, Kipp W.
A1  - Lee, Samuel J.
A1  - Miotto, Riccardo
A1  - Richter, Felix
A1  - Zhao, Shan
A1  - Beckmann, Noam D.
A1  - Naik, Nidhi
A1  - Kia, Arash
A1  - Timsina, Prem
A1  - Lala, Anuradha
A1  - Paranjpe, Manish
A1  - Golden, Eddye
A1  - Danieletto, Matteo
A1  - Singh, Manbir
A1  - Meyer, Dara
A1  - O'Reilly, Paul F.
A1  - Huckins, Laura
A1  - Kovatch, Patricia
A1  - Finkelstein, Joseph
A1  - Freeman, Robert M.
A1  - Argulian, Edgar
A1  - Kasarskis, Andrew
A1  - Percha, Bethany
A1  - Aberg, Judith A.
A1  - Bagiella, Emilia
A1  - Horowitz, Carol R.
A1  - Murphy, Barbara
A1  - Nestler, Eric J.
A1  - Schadt, Eric E.
A1  - Cho, Judy H.
A1  - Cordon-Cardo, Carlos
A1  - Fuster, Valentin
A1  - Charney, Dennis S.
A1  - Reich, David L.
A1  - Böttinger, Erwin
A1  - Levin, Matthew A.
A1  - Narula, Jagat
A1  - Fayad, Zahi A.
A1  - Just, Allan C.
A1  - Charney, Alexander W.
A1  - Nadkarni, Girish N.
A1  - Glicksberg, Benjamin S.
T1  - Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation
JF  - Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR
N2  - 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.
KW  - machine learning
KW  - COVID-19
KW  - electronic health record
KW  - TRIPOD
KW  - clinical
KW  - informatics
KW  - prediction
KW  - mortality
KW  - EHR
KW  - cohort
KW  - hospital
KW  - performance
Y1  - 2020
U6  - https://doi.org/10.2196/24018
SN  - 1439-4456
SN  - 1438-8871
VL  - 22
IS  - 11
PB  - Healthcare World
CY  - Richmond, Va.
ER  - 
TY  - JOUR
A1  - Chan, Lili
A1  - Chaudhary, Kumardeep
A1  - Saha, Aparna
A1  - Chauhan, Kinsuk
A1  - Vaid, Akhil
A1  - Zhao, Shan
A1  - Paranjpe, Ishan
A1  - Somani, Sulaiman
A1  - Richter, Felix
A1  - Miotto, Riccardo
A1  - Lala, Anuradha
A1  - Kia, Arash
A1  - Timsina, Prem
A1  - Li, Li
A1  - Freeman, Robert
A1  - Chen, Rong
A1  - Narula, Jagat
A1  - Just, Allan C.
A1  - Horowitz, Carol
A1  - Fayad, Zahi
A1  - Cordon-Cardo, Carlos
A1  - Schadt, Eric
A1  - Levin, Matthew A.
A1  - Reich, David L.
A1  - Fuster, Valentin
A1  - Murphy, Barbara
A1  - He, John C.
A1  - Charney, Alexander W.
A1  - Böttinger, Erwin
A1  - Glicksberg, Benjamin
A1  - Coca, Steven G.
A1  - Nadkarni, Girish N.
T1  - AKI in hospitalized patients with COVID-19
JF  - Journal of the American Society of Nephrology : JASN
N2  - 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.
KW  - acute renal failure
KW  - clinical nephrology
KW  - dialysis
KW  - COVID-19
Y1  - 2021
U6  - https://doi.org/10.1681/ASN.2020050615
SN  - 1046-6673
SN  - 1533-3450
VL  - 32
IS  - 1
SP  - 151
EP  - 160
PB  - American Society of Nephrology
CY  - Washington
ER  - 
TY  - GEN
A1  - Benecke, Karin
A1  - Deeken, Johannes
A1  - Hammer, Carolin
A1  - Hinz, Carsten
A1  - Löffler, Robert
A1  - Penning, Isabelle
A1  - Richter, Christin
A1  - Schäfer, David
A1  - Scherer, Hubertus
ED  - Kirchner, Vera
T1  - #Wirtschaft – Niedersachsen
BT  - Wirtschaft für die Haupt-, Real-, Ober- und Gesamtschule : Gesamtband für die Jahrgangsstufen 7–10
Y1  - 2020
SN  - 978-3-661-82241-9
PB  - Buchner
CY  - Bamberg
ER  - 
TY  - RPRT
A1  - Adriaans, Jule
A1  - Griese, Florian
A1  - Auspurg, Katrin
A1  - Bledow, Nona
A1  - Bohmann, Sandra
A1  - Busemeyer, Marius R.
A1  - Delhey, Jan
A1  - Goebel, Jan
A1  - Groh-Samberg, Olaf
A1  - Heckhausen, Jutta
A1  - Hinz, Thomas
A1  - Kroh, Martin
A1  - Lengfeld, Holger
A1  - Lersch, Philipp M.
A1  - Liebig, Stefan
A1  - Richter, David
A1  - Sachweh, Patrick
A1  - Schupp, Jürgen
A1  - Schwerdt, Guido
A1  - Verwiebe, Roland
T1  - Dokumentation zum Entwicklungsprozess des Moduls Einstellungen zu sozialer Ungleichheit im SOEP (v38)
T2  - SOEP survey papers, series B - survey reports (Methodenberichte)
N2  - Im Rahmen eines einjährigen 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 Mobilität, 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) für 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.
KW  - Einstellungen zu sozialer Ungleichheit
KW  - Fragebogenentwicklung
KW  - Pretest
KW  - SOEP
KW  - SOEP-IS
Y1  - 2021
UR  - https://www.diw.de/documents/publikationen/73/diw_01.c.829765.de/diw_ssp1071.pdf
SN  - 2193-5580
VL  - 1071
PB  - Deutsches Institut für Wirtschaftsforschung (DIW)
CY  - Berlin
ER  -