TY - JOUR A1 - Redlich, Sarah A1 - Zhang, Jie A1 - Benjamin, Caryl A1 - Dhillon, Maninder Singh A1 - Englmeier, Jana A1 - Ewald, Jörg A1 - Fricke, Ute A1 - Ganuza, Cristina A1 - Haensel, Maria A1 - Hovestadt, Thomas A1 - Kollmann, Johannes A1 - Koellner, Thomas A1 - Kübert‐Flock, Carina A1 - Kunstmann, Harald A1 - Menzel, Annette A1 - Moning, Christoph A1 - Peters, Wibke A1 - Riebl, Rebekka A1 - Rummler, Thomas A1 - Rojas‐Botero, Sandra A1 - Tobisch, Cynthia A1 - Uhler, Johannes A1 - Uphus, Lars A1 - Müller, Jörg A1 - Steffan‐Dewenter, Ingolf T1 - Disentangling effects of climate and land use on biodiversity and ecosystem services—A multi‐scale experimental design JF - Methods in Ecology and Evolution N2 - Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs. KW - study design KW - biodiversity KW - climate change KW - ecosystem functioning KW - insect monitoring KW - land use KW - space-for-time approach KW - spatial scales Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-258270 VL - 13 IS - 2 ER - TY - JOUR A1 - Bousquet, Jean A1 - Anto, Josep M. A1 - Bachert, Claus A1 - Haahtela, Tari A1 - Zuberbier, Torsten A1 - Czarlewski, Wienczyslawa A1 - Bedbrook, Anna A1 - Bosnic‐Anticevich, Sinthia A1 - Walter Canonica, G. A1 - Cardona, Victoria A1 - Costa, Elisio A1 - Cruz, Alvaro A. A1 - Erhola, Marina A1 - Fokkens, Wytske J. A1 - Fonseca, Joao A. A1 - Illario, Maddalena A1 - Ivancevich, Juan‐Carlos A1 - Jutel, Marek A1 - Klimek, Ludger A1 - Kuna, Piotr A1 - Kvedariene, Violeta A1 - Le, LTT A1 - Larenas‐Linnemann, Désirée E. A1 - Laune, Daniel A1 - Lourenço, Olga M. A1 - Melén, Erik A1 - Mullol, Joaquim A1 - Niedoszytko, Marek A1 - Odemyr, Mikaëla A1 - Okamoto, Yoshitaka A1 - Papadopoulos, Nikos G. A1 - Patella, Vincenzo A1 - Pfaar, Oliver A1 - Pham‐Thi, Nhân A1 - Rolland, Christine A1 - Samolinski, Boleslaw A1 - Sheikh, Aziz A1 - Sofiev, Mikhail A1 - Suppli Ulrik, Charlotte A1 - Todo‐Bom, Ana A1 - Tomazic, Peter‐Valentin A1 - Toppila‐Salmi, Sanna A1 - Tsiligianni, Ioanna A1 - Valiulis, Arunas A1 - Valovirta, Erkka A1 - Ventura, Maria‐Teresa A1 - Walker, Samantha A1 - Williams, Sian A1 - Yorgancioglu, Arzu A1 - Agache, Ioana A1 - Akdis, Cezmi A. A1 - Almeida, Rute A1 - Ansotegui, Ignacio J. A1 - Annesi‐Maesano, Isabella A1 - Arnavielhe, Sylvie A1 - Basagaña, Xavier A1 - D. Bateman, Eric A1 - Bédard, Annabelle A1 - Bedolla‐Barajas, Martin A1 - Becker, Sven A1 - Bennoor, Kazi S. A1 - Benveniste, Samuel A1 - Bergmann, Karl C. A1 - Bewick, Michael A1 - Bialek, Slawomir A1 - E. Billo, Nils A1 - Bindslev‐Jensen, Carsten A1 - Bjermer, Leif A1 - Blain, Hubert A1 - Bonini, Matteo A1 - Bonniaud, Philippe A1 - Bosse, Isabelle A1 - Bouchard, Jacques A1 - Boulet, Louis‐Philippe A1 - Bourret, Rodolphe A1 - Boussery, Koen A1 - Braido, Fluvio A1 - Briedis, Vitalis A1 - Briggs, Andrew A1 - Brightling, Christopher E. A1 - Brozek, Jan A1 - Brusselle, Guy A1 - Brussino, Luisa A1 - Buhl, Roland A1 - Buonaiuto, Roland A1 - Calderon, Moises A. A1 - Camargos, Paulo A1 - Camuzat, Thierry A1 - Caraballo, Luis A1 - Carriazo, Ana‐Maria A1 - Carr, Warner A1 - Cartier, Christine A1 - Casale, Thomas A1 - Cecchi, Lorenzo A1 - Cepeda Sarabia, Alfonso M. A1 - H. Chavannes, Niels A1 - Chkhartishvili, Ekaterine A1 - Chu, Derek K. A1 - Cingi, Cemal A1 - Correia de Sousa, Jaime A1 - Costa, David J. A1 - Courbis, Anne‐Lise A1 - Custovic, Adnan A1 - Cvetkosvki, Biljana A1 - D'Amato, Gennaro A1 - da Silva, Jane A1 - Dantas, Carina A1 - Dokic, Dejan A1 - Dauvilliers, Yves A1 - De Feo, Giulia A1 - De Vries, Govert A1 - Devillier, Philippe A1 - Di Capua, Stefania A1 - Dray, Gerard A1 - Dubakiene, Ruta A1 - Durham, Stephen R. A1 - Dykewicz, Mark A1 - Ebisawa, Motohiro A1 - Gaga, Mina A1 - El‐Gamal, Yehia A1 - Heffler, Enrico A1 - Emuzyte, Regina A1 - Farrell, John A1 - Fauquert, Jean‐Luc A1 - Fiocchi, Alessandro A1 - Fink‐Wagner, Antje A1 - Fontaine, Jean‐François A1 - Fuentes Perez, José M. A1 - Gemicioğlu, Bilun A1 - Gamkrelidze, Amiran A1 - Garcia‐Aymerich, Judith A1 - Gevaert, Philippe A1 - Gomez, René Maximiliano A1 - González Diaz, Sandra A1 - Gotua, Maia A1 - Guldemond, Nick A. A1 - Guzmán, Maria‐Antonieta A1 - Hajjam, Jawad A1 - Huerta Villalobos, Yunuen R. A1 - Humbert, Marc A1 - Iaccarino, Guido A1 - Ierodiakonou, Despo A1 - Iinuma, Tomohisa A1 - Jassem, Ewa A1 - Joos, Guy A1 - Jung, Ki‐Suck A1 - Kaidashev, Igor A1 - Kalayci, Omer A1 - Kardas, Przemyslaw A1 - Keil, Thomas A1 - Khaitov, Musa A1 - Khaltaev, Nikolai A1 - Kleine‐Tebbe, Jorg A1 - Kouznetsov, Rostislav A1 - Kowalski, Marek L. A1 - Kritikos, Vicky A1 - Kull, Inger A1 - La Grutta, Stefania A1 - Leonardini, Lisa A1 - Ljungberg, Henrik A1 - Lieberman, Philip A1 - Lipworth, Brian A1 - Lodrup Carlsen, Karin C. A1 - Lopes‐Pereira, Catarina A1 - Loureiro, Claudia C. A1 - Louis, Renaud A1 - Mair, Alpana A1 - Mahboub, Bassam A1 - Makris, Michaël A1 - Malva, Joao A1 - Manning, Patrick A1 - Marshall, Gailen D. A1 - Masjedi, Mohamed R. A1 - Maspero, Jorge F. A1 - Carreiro‐Martins, Pedro A1 - Makela, Mika A1 - Mathieu‐Dupas, Eve A1 - Maurer, Marcus A1 - De Manuel Keenoy, Esteban A1 - Melo‐Gomes, Elisabete A1 - Meltzer, Eli O. A1 - Menditto, Enrica A1 - Mercier, Jacques A1 - Micheli, Yann A1 - Miculinic, Neven A1 - Mihaltan, Florin A1 - Milenkovic, Branislava A1 - Mitsias, Dimitirios I. A1 - Moda, Giuliana A1 - Mogica‐Martinez, Maria‐Dolores A1 - Mohammad, Yousser A1 - Montefort, Steve A1 - Monti, Ricardo A1 - Morais‐Almeida, Mario A1 - Mösges, Ralph A1 - Münter, Lars A1 - Muraro, Antonella A1 - Murray, Ruth A1 - Naclerio, Robert A1 - Napoli, Luigi A1 - Namazova‐Baranova, Leyla A1 - Neffen, Hugo A1 - Nekam, Kristoff A1 - Neou, Angelo A1 - Nordlund, Björn A1 - Novellino, Ettore A1 - Nyembue, Dieudonné A1 - O'Hehir, Robyn A1 - Ohta, Ken A1 - Okubo, Kimi A1 - Onorato, Gabrielle L. A1 - Orlando, Valentina A1 - Ouedraogo, Solange A1 - Palamarchuk, Julia A1 - Pali‐Schöll, Isabella A1 - Panzner, Peter A1 - Park, Hae‐Sim A1 - Passalacqua, Gianni A1 - Pépin, Jean‐Louis A1 - Paulino, Ema A1 - Pawankar, Ruby A1 - Phillips, Jim A1 - Picard, Robert A1 - Pinnock, Hilary A1 - Plavec, Davor A1 - Popov, Todor A. A1 - Portejoie, Fabienne A1 - Price, David A1 - Prokopakis, Emmanuel P. A1 - Psarros, Fotis A1 - Pugin, Benoit A1 - Puggioni, Francesca A1 - Quinones‐Delgado, Pablo A1 - Raciborski, Filip A1 - Rajabian‐Söderlund, Rojin A1 - Regateiro, Frederico S. A1 - Reitsma, Sietze A1 - Rivero‐Yeverino, Daniela A1 - Roberts, Graham A1 - Roche, Nicolas A1 - Rodriguez‐Zagal, Erendira A1 - Rolland, Christine A1 - Roller‐Wirnsberger, Regina E. A1 - Rosario, Nelson A1 - Romano, Antonino A1 - Rottem, Menachem A1 - Ryan, Dermot A1 - Salimäki, Johanna A1 - Sanchez‐Borges, Mario M. A1 - Sastre, Joaquin A1 - Scadding, Glenis K. A1 - Scheire, Sophie A1 - Schmid‐Grendelmeier, Peter A1 - Schünemann, Holger J. A1 - Sarquis Serpa, Faradiba A1 - Shamji, Mohamed A1 - Sisul, Juan‐Carlos A1 - Sofiev, Mikhail A1 - Solé, Dirceu A1 - Somekh, David A1 - Sooronbaev, Talant A1 - Sova, Milan A1 - Spertini, François A1 - Spranger, Otto A1 - Stellato, Cristiana A1 - Stelmach, Rafael A1 - Thibaudon, Michel A1 - To, Teresa A1 - Toumi, Mondher A1 - Usmani, Omar A1 - Valero, Antonio A. A1 - Valenta, Rudolph A1 - Valentin‐Rostan, Marylin A1 - Pereira, Marilyn Urrutia A1 - van der Kleij, Rianne A1 - Van Eerd, Michiel A1 - Vandenplas, Olivier A1 - Vasankari, Tuula A1 - Vaz Carneiro, Antonio A1 - Vezzani, Giorgio A1 - Viart, Frédéric A1 - Viegi, Giovanni A1 - Wallace, Dana A1 - Wagenmann, Martin A1 - Wang, De Yun A1 - Waserman, Susan A1 - Wickman, Magnus A1 - Williams, Dennis M. A1 - Wong, Gary A1 - Wroczynski, Piotr A1 - Yiallouros, Panayiotis K. A1 - Yusuf, Osman M. A1 - Zar, Heather J. A1 - Zeng, Stéphane A1 - Zernotti, Mario E. A1 - Zhang, Luo A1 - Shan Zhong, Nan A1 - Zidarn, Mihaela T1 - ARIA digital anamorphosis: Digital transformation of health and care in airway diseases from research to practice JF - Allergy N2 - Digital anamorphosis is used to define a distorted image of health and care that may be viewed correctly using digital tools and strategies. MASK digital anamorphosis represents the process used by MASK to develop the digital transformation of health and care in rhinitis. It strengthens the ARIA change management strategy in the prevention and management of airway disease. The MASK strategy is based on validated digital tools. Using the MASK digital tool and the CARAT online enhanced clinical framework, solutions for practical steps of digital enhancement of care are proposed. KW - ARIA KW - asthma KW - CARAT KW - digital transformation of health and care KW - MASK KW - rhinitis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-228339 VL - 76 IS - 1 SP - 168 EP - 190 ER - TY - JOUR A1 - Chopra, Martin A1 - Biehl, Marlene A1 - Steinfatt, Tim A1 - Brandl, Andreas A1 - Kums, Juliane A1 - Amich, Jorge A1 - Vaeth, Martin A1 - Kuen, Janina A1 - Holtappels, Rafaela A1 - Podlech, Jürgen A1 - Mottok, Anja A1 - Kraus, Sabrina A1 - Jordán-Garotte, Ana-Laura A1 - Bäuerlein, Carina A. A1 - Brede, Christian A1 - Ribechini, Eliana A1 - Fick, Andrea A1 - Seher, Axel A1 - Polz, Johannes A1 - Ottmueller, Katja J. A1 - Baker, Jeannette A1 - Nishikii, Hidekazu A1 - Ritz, Miriam A1 - Mattenheimer, Katharina A1 - Schwinn, Stefanie A1 - Winter, Thorsten A1 - Schäfer, Viktoria A1 - Krappmann, Sven A1 - Einsele, Hermann A1 - Müller, Thomas D. A1 - Reddehase, Matthias J. A1 - Lutz, Manfred B. A1 - Männel, Daniela N. A1 - Berberich-Siebelt, Friederike A1 - Wajant, Harald A1 - Beilhack, Andreas T1 - Exogenous TNFR2 activation protects from acute GvHD via host T reg cell expansion JF - Journal of Experimental Medicine N2 - Donor CD4\(^+\)Foxp3\(^+\) regulatory T cells (T reg cells) suppress graft-versus-host disease (GvHD) after allogeneic hematopoietic stem cell transplantation (HCT allo-HCT]). Current clinical study protocols rely on the ex vivo expansion of donor T reg cells and their infusion in high numbers. In this study, we present a novel strategy for inhibiting GvHD that is based on the in vivo expansion of recipient T reg cells before allo-HCT, exploiting the crucial role of tumor necrosis factor receptor 2 (TNFR2) in T reg cell biology. Expanding radiation-resistant host T reg cells in recipient mice using a mouse TNFR2-selective agonist before allo-HCT significantly prolonged survival and reduced GvHD severity in a TNFR2-and T reg cell-dependent manner. The beneficial effects of transplanted T cells against leukemia cells and infectious pathogens remained unaffected. A corresponding human TNFR2-specific agonist expanded human T reg cells in vitro. These observations indicate the potential of our strategy to protect allo-HCT patients from acute GvHD by expanding T reg cells via selective TNFR2 activation in vivo. KW - Tumor-necrosis-factor KW - Regulatory-cells KW - Bone marrow transplantantation KW - Graft-versus-leukemia KW - Rheumatoid arthritis KW - Autoimmune diseases KW - Factor receptor KW - Alpha therapy KW - Expression KW - Suppression Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-187640 VL - 213 IS - 9 ER - TY - JOUR A1 - Cecil, Alexander A1 - Rikanovic, Carina A1 - Ohlsen, Knut A1 - Liang, Chunguang A1 - Bernhardt, Jorg A1 - Oelschlaeger, Tobias A. A1 - Gulder, Tanja A1 - Bringmann, Gerd A1 - Holzgrabe, Ulrike A1 - Unger, Matthias A1 - Dandekar, Thomas T1 - Modeling antibiotic and cytotoxic effects of the dimeric isoquinoline IQ-143 on metabolism and its regulation in Staphylococcus aureus, Staphylococcus epidermidis and human cells N2 - Background: Xenobiotics represent an environmental stress and as such are a source for antibiotics, including the isoquinoline (IQ) compound IQ-143. Here, we demonstrate the utility of complementary analysis of both host and pathogen datasets in assessing bacterial adaptation to IQ-143, a synthetic analog of the novel type N,C-coupled naphthyl-isoquinoline alkaloid ancisheynine. Results: Metabolite measurements, gene expression data and functional assays were combined with metabolic modeling to assess the effects of IQ-143 on Staphylococcus aureus, Staphylococcus epidermidis and human cell lines, as a potential paradigm for novel antibiotics. Genome annotation and PCR validation identified novel enzymes in the primary metabolism of staphylococci. Gene expression response analysis and metabolic modeling demonstrated the adaptation of enzymes to IQ-143, including those not affected by significant gene expression changes. At lower concentrations, IQ-143 was bacteriostatic, and at higher concentrations bactericidal, while the analysis suggested that the mode of action was a direct interference in nucleotide and energy metabolism. Experiments in human cell lines supported the conclusions from pathway modeling and found that IQ-143 had low cytotoxicity. Conclusions: The data suggest that IQ-143 is a promising lead compound for antibiotic therapy against staphylococci. The combination of gene expression and metabolite analyses with in silico modeling of metabolite pathways allowed us to study metabolic adaptations in detail and can be used for the evaluation of metabolic effects of other xenobiotics. KW - Staphylococcus aureus Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-68802 ER - TY - JOUR A1 - Pietro-Garcia, Christian A1 - Hartmann, Oliver A1 - Reissland, Michaela A1 - Fischer, Thomas A1 - Maier, Carina R. A1 - Rosenfeldt, Mathias A1 - Schülein-Völk, Christina A1 - Klann, Kevin A1 - Kalb, Reinhard A1 - Dikic, Ivan A1 - Münch, Christian A1 - Diefenbacher, Markus E. T1 - Inhibition of USP28 overcomes Cisplatin-resistance of squamous tumors by suppression of the Fanconi anemia pathway JF - Cell Death and Differentiation N2 - Squamous cell carcinomas (SCC) frequently have an exceptionally high mutational burden. As consequence, they rapidly develop resistance to platinum-based chemotherapy and overall survival is limited. Novel therapeutic strategies are therefore urgently required. SCC express ∆Np63, which regulates the Fanconi Anemia (FA) DNA-damage response in cancer cells, thereby contributing to chemotherapy-resistance. Here we report that the deubiquitylase USP28 is recruited to sites of DNA damage in cisplatin-treated cells. ATR phosphorylates USP28 and increases its enzymatic activity. This phosphorylation event is required to positively regulate the DNA damage repair in SCC by stabilizing ∆Np63. Knock-down or inhibition of USP28 by a specific inhibitor weakens the ability of SCC to cope with DNA damage during platin-based chemotherapy. Hence, our study presents a novel mechanism by which ∆Np63 expressing SCC can be targeted to overcome chemotherapy resistance. Limited treatment options and low response rates to chemotherapy are particularly common in patients with squamous cancer. The SCC specific transcription factor ∆Np63 enhances the expression of Fanconi Anemia genes, thereby contributing to recombinational DNA repair and Cisplatin resistance. Targeting the USP28-∆Np63 axis in SCC tones down this DNA damage response pathways, thereby sensitizing SCC cells to cisplatin treatment. KW - USP28 KW - Cisplatin KW - squamous tumors Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-273014 SN - 1476-5403 VL - 29 IS - 3 ER - TY - JOUR A1 - Hartmann, Oliver A1 - Reissland, Michaela A1 - Maier, Carina R. A1 - Fischer, Thomas A1 - Prieto-Garcia, Cristian A1 - Baluapuri, Apoorva A1 - Schwarz, Jessica A1 - Schmitz, Werner A1 - Garrido-Rodriguez, Martin A1 - Pahor, Nikolett A1 - Davies, Clare C. A1 - Bassermann, Florian A1 - Orian, Amir A1 - Wolf, Elmar A1 - Schulze, Almut A1 - Calzado, Marco A. A1 - Rosenfeldt, Mathias T. A1 - Diefenbacher, Markus E. T1 - Implementation of CRISPR/Cas9 Genome Editing to Generate Murine Lung Cancer Models That Depict the Mutational Landscape of Human Disease JF - Frontiers in Cell and Developmental Biology N2 - Lung cancer is the most common cancer worldwide and the leading cause of cancer-related deaths in both men and women. Despite the development of novel therapeutic interventions, the 5-year survival rate for non-small cell lung cancer (NSCLC) patients remains low, demonstrating the necessity for novel treatments. One strategy to improve translational research is the development of surrogate models reflecting somatic mutations identified in lung cancer patients as these impact treatment responses. With the advent of CRISPR-mediated genome editing, gene deletion as well as site-directed integration of point mutations enabled us to model human malignancies in more detail than ever before. Here, we report that by using CRISPR/Cas9-mediated targeting of Trp53 and KRas, we recapitulated the classic murine NSCLC model Trp53fl/fl:lsl-KRasG12D/wt. Developing tumors were indistinguishable from Trp53fl/fl:lsl-KRasG12D/wt-derived tumors with regard to morphology, marker expression, and transcriptional profiles. We demonstrate the applicability of CRISPR for tumor modeling in vivo and ameliorating the need to use conventional genetically engineered mouse models. Furthermore, tumor onset was not only achieved in constitutive Cas9 expression but also in wild-type animals via infection of lung epithelial cells with two discrete AAVs encoding different parts of the CRISPR machinery. While conventional mouse models require extensive husbandry to integrate new genetic features allowing for gene targeting, basic molecular methods suffice to inflict the desired genetic alterations in vivo. Utilizing the CRISPR toolbox, in vivo cancer research and modeling is rapidly evolving and enables researchers to swiftly develop new, clinically relevant surrogate models for translational research. KW - non-small cell lung cancer KW - CRISPR-Cas9 KW - mouse model KW - lung cancer KW - MYC KW - JUN Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-230949 SN - 2296-634X VL - 9 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kuebert-Flock, Carina A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape JF - Frontiers in Remote Sensing N2 - The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R 2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops. KW - crop modeling KW - random forest KW - machine learning KW - NDVI KW - satellite KW - landsat KW - sentinel-2 KW - winter wheat Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-301462 SN - 2673-6187 VL - 3 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Kübert-Flock, Carina A1 - Dahms, Thorsten A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: a case study using STARFM NDVI in Bavaria, Germany JF - Remote Sensing N2 - The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km\(^2\)) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R\(^2\) = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R\(^2\) = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R\(^2\) = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R\(^2\) = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. KW - MODIS KW - Sentinel-2 KW - Landsat 8 KW - sustainable agriculture KW - decision-making KW - winter wheat KW - oil seed rape KW - resolution Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311132 SN - 2072-4292 VL - 15 IS - 7 ER - TY - JOUR A1 - Martin, Tamara A1 - Rommel, Kathrin A1 - Thomas, Carina A1 - Eymann, Jutta A1 - Kretschmer, Tanita A1 - Berner, Reinhard A1 - Lee-Kirsch, Min Ae A1 - Hebestreit, Helge T1 - Seltene Erkrankungen in den Daten sichtbar machen – Kodierung JF - Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz N2 - Seltene Erkrankungen (SE) werden durch die im deutschen Gesundheitssystem verwendete Diagnosenklassifikation ICD-10-GM (International Statistical Classification of Diseases and Related Health problems, 10th Revision, German Modification) nur zu einem kleinen Teil eindeutig erfasst. Daher sind Aussagen zur Häufigkeit von SE sowie zum speziellen Versorgungs- und Finanzierungsbedarf nicht möglich, was zu einer lückenhaften Datenlage als Entscheidungsgrundlage für Krankenkassen, Leistungserbringer und Gesundheitspolitik führt. Das Fehlen exakter Informationen behindert auch die wissenschaftliche Arbeit. Daher wird deutschlandweit ab 2023 die Verwendung der Alpha-ID-SE-Datei und der ORPHAcodes für die spezifische Erfassung von SE bei stationären Fällen verpflichtend. Die Alpha-ID-SE-Datei verknüpft die ICD-10-GM-Kodes mit den international anerkannten ORPHAcodes für die Diagnose von SE. Kommerzielle Anbieter stellen zunehmend die benötigten IT-Tools zur Kodierung von SE zur Verfügung. An mehreren Universitätskliniken mit Zentren für SE wurden Lösungen etabliert, die eine vollständige Kodierung gewährleisten sollen. Hierzu gehören finanzielle Anreize für die kodierenden Bereiche, konkrete Nachfragen nach dem Vorliegen einer SE beim Kodiervorgang und eine semiautomatische Kodierung bei Patient*innen, die schon einmal mit einer SE an der Einrichtung betreut worden waren. Eine Kombination der verschiedenen Ansätze verspricht die höchste Wahrscheinlichkeit einer vollständigen Kodierung. Für ein umfängliches Bild der SE im Gesundheitssystem und um dem speziellen Versorgungs- und Finanzierungsbedarf besser Rechnung tragen zu können, wäre auch im ambulanten Bereich eine möglichst spezifische und eindeutige Kodierung wünschenswert. Für komplexe SE und bisher undiagnostizierte Patient*innen wird zusätzlich eine strukturierte Erfassung des Phänotyps benötigt. N2 - The ICD-10-GM coding system used in the German healthcare system only captures a minority of rare disease diagnoses. Therefore, information on the incidence and prevalence of rare diseases as well as necessary (financial) resources for the expert care required for evidence-based decisions by health insurers, care providers, and politicians are lacking. Furthermore, the missing information complicates and sometimes even precludes the generation of scientific knowledge on rare diseases. Therefore, starting in 2023, all in-patient cases in Germany with a rare disease diagnosis must be coded by an ORPHAcode using the Alpha-ID-SE file. The file Alpha-ID-SE links the ICD-10-GM codes to the internationally established ORPHAcodes for rare diseases. Commercially available software tools progressively support the coding of rare diseases. In several centers for rare diseases linked to university hospitals, IT tools and procedures were established to realize a complete coding of rare diseases. These include financial incentives for the institutions providing rare disease codes, systematic queries asking for rare disease codes during the coding process, and a semi-automated coding process for all patients with a rare disease previously seen at the institution. A combination of the different approaches probably results in the most complete coding. To get the complete picture of rare disease epidemiology and care requirements, a specific and unique coding of out-patient cases is also desirable. Furthermore, a structured reporting of phenotype is required, especially for complex rare diseases and for yet undiagnosed cases. KW - Seltene Erkrankung KW - ORPHAcode KW - Alpha-ID-SE KW - Human Phenotype Ontology KW - Diagnose KW - rare diseases KW - ORPHAcode KW - Alpha-ID-SE KW - human phenotype ontology KW - diagnosis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324275 VL - 65 IS - 11 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kübert-Flock, Carina A1 - Liepa, Adomas A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany JF - Remote Sensing N2 - Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km\(^2\)), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R\(^2\) of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R\(^2\) of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R\(^2\) of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively. KW - MOD13Q1 KW - precision agriculture KW - fusion KW - sustainable agriculture KW - decision making KW - winter wheat KW - oil-seed rape KW - crop models Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311092 SN - 2072-4292 VL - 15 IS - 6 ER -