TY - JOUR A1 - Kaya, Mustafa Yücel A1 - Dupont-Nivet, Guillaume A1 - Proust, Jean‐Noël A1 - Roperch, Pierrick A1 - Bougeois, Laurie A1 - Meijer, Niels A1 - Frieling, Joost A1 - Fioroni, Chiara A1 - Altiner, Sevinç Özkan A1 - Vardar, Ezgi A1 - Barbolini, Natasha A1 - Stoica, Marius A1 - Aminov, Jovid A1 - Mamtimin, Mehmut A1 - Zhaojie, Guo T1 - Paleogene evolution and demise of the proto-Paratethys Sea in Central Asia (Tarim and Tajik basins) BT - Role of intensified tectonic activity at ca. 41 Ma JF - Basin research N2 - The proto-Paratethys Sea covered a vast area extending from the Mediterranean Tethys to the Tarim Basin in western China during Cretaceous and early Paleogene. Climate modelling and proxy studies suggest that Asian aridification has been governed by westerly moisture modulated by fluctuations of the proto-Paratethys Sea. Transgressive and regressive episodes of the proto-Paratethys Sea have been previously recognized but their timing, extent and depositional environments remain poorly constrained. This hampers understanding of their driving mechanisms (tectonic and/or eustatic) and their contribution to Asian aridification. Here, we present a new chronostratigraphic framework based on biostratigraphy and magnetostratigraphy as well as a detailed palaeoenvironmental analysis for the Paleogene proto-Paratethys Sea incursions in the Tajik and Tarim basins. This enables us to identify the major drivers of marine fluctuations and their potential consequences on Asian aridification. A major regional restriction event, marked by the exceptionally thick (<= 400 m) shelf evaporites is assigned a Danian-Selandian age (ca. 63-59 Ma) in the Aertashi Formation. This is followed by the largest recorded proto-Paratethys Sea incursion with a transgression estimated as early Thanetian (ca. 59-57 Ma) and a regression within the Ypresian (ca. 53-52 Ma), both within the Qimugen Formation. The transgression of the next incursion in the Kalatar and Wulagen formations is now constrained as early Lutetian (ca. 47-46 Ma), whereas its regression in the Bashibulake Formation is constrained as late Lutetian (ca. 41 Ma) and is associated with a drastic increase in both tectonic subsidence and basin infilling. The age of the final and least pronounced sea incursion restricted to the westernmost margin of the Tarim Basin is assigned as Bartonian-Priabonian (ca. 39.7-36.7 Ma). We interpret the long-term westward retreat of the proto-Paratethys Sea starting at ca. 41 Ma to be associated with far-field tectonic effects of the Indo-Asia collision and Pamir/Tibetan plateau uplift. Short-term eustatic sea level transgressions are superimposed on this long-term regression and seem coeval with the transgression events in the other northern Peri-Tethyan sedimentary provinces for the 1st and 2nd sea incursions. However, the 3rd sea incursion is interpreted as related to tectonism. The transgressive and regressive intervals of the proto-Paratethys Sea correlate well with the reported humid and arid phases, respectively in the Qaidam and Xining basins, thus demonstrating the role of the proto-Paratethys Sea as an important moisture source for the Asian interior and its regression as a contributor to Asian aridification. KW - aridification KW - Asia KW - biostratigraphy KW - climate KW - magnetostratigraphy KW - microfossil KW - Paleogene KW - Pamir KW - proto-Paratethys KW - regression KW - Tajik Basin KW - Tarim Basin KW - tectonism KW - westerlies Y1 - 2018 U6 - https://doi.org/10.1111/bre.12330 SN - 0950-091X SN - 1365-2117 VL - 31 IS - 3 SP - 461 EP - 486 PB - Wiley CY - Hoboken ER - TY - GEN A1 - Hische, Manuela A1 - Larhlimi, Abdelhalim A1 - Schwarz, Franziska A1 - Fischer-Rosinský, Antje A1 - Bobbert, Thomas A1 - Assmann, Anke A1 - Catchpole, Gareth S. A1 - Pfeiffer, Andreas F. H. A1 - Willmitzer, Lothar A1 - Selbig, Joachim A1 - Spranger, Joachim T1 - A distinct metabolic signature predictsdevelopment of fasting plasma glucose T2 - Postprints der Universität Potsdam : Mathematisch Naturwissenschaftliche Reihe N2 - Background High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. Methods We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. Results We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. Conclusions We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 850 KW - prediction KW - fasting glucose KW - type 2 diabetes KW - metabolomics KW - plasma KW - random forest KW - metabolite KW - regression KW - biomarker Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427400 SN - 1866-8372 IS - 850 ER - TY - JOUR A1 - Hempel, Sabrina A1 - Adolphs, Julian A1 - Landwehr, Niels A1 - Willink, Dilya A1 - Janke, David A1 - Amon, Thomas T1 - Supervised machine learning to assess methane emissions of a dairy building with natural ventilation JF - Applied Sciences N2 - A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach. KW - greenhouse gas KW - on-farm evaluation KW - emission factor KW - regression KW - ensemble methods KW - gradient boosting KW - random forest KW - neural networks KW - support vector machines Y1 - 2020 U6 - https://doi.org/10.3390/app10196938 SN - 2076-3417 VL - 10 IS - 19 PB - MDPI CY - Basel ER - TY - JOUR A1 - Hempel, Sabrina A1 - Adolphs, Julian A1 - Landwehr, Niels A1 - Janke, David A1 - Amon, Thomas T1 - How the selection of training data and modeling approach affects the estimation of ammonia emissions from a naturally ventilated dairy barn—classical statistics versus machine learning JF - Sustainability N2 - Environmental protection efforts can only be effective in the long term with a reliable quantification of pollutant gas emissions as a first step to mitigation. Measurement and analysis strategies must permit the accurate extrapolation of emission values. We systematically analyzed the added value of applying modern machine learning methods in the process of monitoring emissions from naturally ventilated livestock buildings to the atmosphere. We considered almost 40 weeks of hourly emission values from a naturally ventilated dairy cattle barn in Northern Germany. We compared model predictions using 27 different scenarios of temporal sampling, multiple measures of model accuracy, and eight different regression approaches. The error of the predicted emission values with the tested measurement protocols was, on average, well below 20%. The sensitivity of the prediction to the selected training dataset was worse for the ordinary multilinear regression. Gradient boosting and random forests provided the most accurate and robust emission value predictions, accompanied by the second-smallest model errors. Most of the highly ranked scenarios involved six measurement periods, while the scenario with the best overall performance was: One measurement period in summer and three in the transition periods, each lasting for 14 days. KW - livestock KW - air pollutant KW - emission modeling KW - emission inventory KW - regression KW - artificial neural network KW - random forest KW - gradient boosting KW - Gaussian process KW - training sample Y1 - 2020 U6 - https://doi.org/10.3390/su12031030 SN - 2071-1050 VL - 12 IS - 3 PB - MDPI CY - Basel ER -