TY - JOUR A1 - Tausch, Simon H. A1 - Loka, Tobias P. A1 - Schulze, Jakob M. A1 - Andrusch, Andreas A1 - Klenner, Jeanette A1 - Dabrowski, Piotr Wojciech A1 - Lindner, Martin S. A1 - Nitsche, Andreas A1 - Renard, Bernhard Y. T1 - PathoLive-real-time pathogen identification from metagenomic illumina datasets JF - Life N2 - Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics. Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data. We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished. Based on real-time alignment with HiLive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms. The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles. For a real-world sample from Sudan, the results correctly indicated the presence of Crimean-Congo hemorrhagic fever virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS coronavirus as the most relevant hit without the novel virus reference genome being included in the database. For all samples, clinically irrelevant hits were correctly de-emphasized. Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance: PathoLive is open source and available on GitLab and BioConda. KW - NGS KW - metagenomics KW - viruses KW - infectious diseases KW - diagnostics KW - live sequencing Y1 - 2022 U6 - https://doi.org/10.3390/life12091345 SN - 2075-1729 VL - 12 IS - 9 PB - MDPI CY - Basel ER - TY - JOUR A1 - Harmsen, Mathijs A1 - Kriegler, Elmar A1 - van Vuuren, Detlef P. A1 - van der Wijst, Kaj-Ivar A1 - Luderer, Gunnar A1 - Cui, Ryna A1 - Dessens, Olivier A1 - Drouet, Laurent A1 - Emmerling, Johannes A1 - Morris, Jennifer Faye A1 - Fosse, Florian A1 - Fragkiadakis, Dimitris A1 - Fragkiadakis, Kostas A1 - Fragkos, Panagiotis A1 - Fricko, Oliver A1 - Fujimori, Shinichiro A1 - Gernaat, David A1 - Guivarch, Céline A1 - Iyer, Gokul A1 - Karkatsoulis, Panagiotis A1 - Keppo, Ilkka A1 - Keramidas, Kimon A1 - Köberle, Alexandre A1 - Kolp, Peter A1 - Krey, Volker A1 - Krüger, Christoph A1 - Leblanc, Florian A1 - Mittal, Shivika A1 - Paltsev, Sergey A1 - Rochedo, Pedro A1 - van Ruijven, Bas J. A1 - Sands, Ronald D. A1 - Sano, Fuminori A1 - Strefler, Jessica A1 - Arroyo, Eveline Vasquez A1 - Wada, Kenichi A1 - Zakeri, Behnam T1 - Integrated assessment model diagnostics BT - key indicators and model evolution JF - Environmental research letters N2 - Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend. KW - diagnostics KW - integrated assessment models KW - climate policy KW - Assessment Report IPCC KW - renewable energy KW - migration KW - AR6 Y1 - 2021 U6 - https://doi.org/10.1088/1748-9326/abf964 SN - 1748-9326 VL - 16 IS - 5 PB - IOP Publishing CY - Bristol ER - TY - JOUR A1 - Wietzke, Luzie M. A1 - Merz, Bruno A1 - Gerlitz, Lars A1 - Kreibich, Heidi A1 - Guse, Björn A1 - Castellarin, Attilio A1 - Vorogushyn, Sergiy T1 - Comparative analysis of scalar upper tail indicators JF - Hydrological sciences journal = Journal des sciences hydrologiques N2 - Different upper tail indicators exist to characterize heavy tail phenomena, but no comparative study has been carried out so far. We evaluate the shape parameter (GEV), obesity index, Gini index and upper tail ratio (UTR) against a novel benchmark of tail heaviness - the surprise factor. Sensitivity analyses to sample size and changes in scale-to-location ratio are carried out in bootstrap experiments. The UTR replicates the surprise factor best but is most uncertain and only comparable between records of similar length. For samples with symmetric Lorenz curves, shape parameter, obesity and Gini indices provide consistent indications. For asymmetric Lorenz curves, however, the first two tend to overestimate, whereas Gini index tends to underestimate tail heaviness. We suggest the use of a combination of shape parameter, obesity and Gini index to characterize tail heaviness. These indicators should be supported with calculation of the Lorenz asymmetry coefficients and interpreted with caution. KW - upper tail behaviour KW - heavy-tailed distributions KW - extremes KW - diagnostics KW - surprise Y1 - 2020 U6 - https://doi.org/10.1080/02626667.2020.1769104 SN - 0262-6667 SN - 2150-3435 VL - 65 IS - 10 SP - 1625 EP - 1639 PB - Routledge, Taylor & Francis Group CY - Abingdon ER -