@misc{WuertzKozakRoszkowskiCambriaetal.2020, author = {Wuertz-Kozak, Karin and Roszkowski, Martin and Cambria, Elena and Block, Andrea and Kuhn, Gisela A. and Abele, Thea and Hitzl, Wolfgang and Drießlein, David and M{\"u}ller, Ralph and Rapp, Michael A. and Mansuy, Isabelle M. and Peters, Eva M. J. and Wippert, Pia-Maria}, title = {Effects of Early Life Stress on Bone Homeostasis in Mice and Humans}, series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, number = {670}, issn = {1866-8364}, doi = {10.25932/publishup-48532}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-485324}, pages = {26}, year = {2020}, abstract = {Bone pathology is frequent in stressed individuals. A comprehensive examination of mechanisms linking life stress, depression and disturbed bone homeostasis is missing. In this translational study, mice exposed to early life stress (MSUS) were examined for bone microarchitecture (μCT), metabolism (qPCR/ELISA), and neuronal stress mediator expression (qPCR) and compared with a sample of depressive patients with or without early life stress by analyzing bone mineral density (BMD) (DXA) and metabolic changes in serum (osteocalcin, PINP, CTX-I). MSUS mice showed a significant decrease in NGF, NPYR1, VIPR1 and TACR1 expression, higher innervation density in bone, and increased serum levels of CTX-I, suggesting a milieu in favor of catabolic bone turnover. MSUS mice had a significantly lower body weight compared to control mice, and this caused minor effects on bone microarchitecture. Depressive patients with experiences of childhood neglect also showed a catabolic pattern. A significant reduction in BMD was observed in depressive patients with childhood abuse and stressful life events during childhood. Therefore, future studies on prevention and treatment strategies for both mental and bone disease should consider early life stress as a risk factor for bone pathologies.}, language = {en} } @article{WuertzKozakRoszkowskiCambriaetal.2020, author = {Wuertz-Kozak, Karin and Roszkowski, Martin and Cambria, Elena and Block, Andrea and Kuhn, Gisela A. and Abele, Thea and Hitzl, Wolfgang and Drießlein, David and M{\"u}ller, Ralph and Rapp, Michael A. and Mansuy, Isabelle M. and Peters, Eva M. J. and Wippert, Pia-Maria}, title = {Effects of Early Life Stress on Bone Homeostasis in Mice and Humans}, series = {International Journal of Molecular Sciences}, volume = {21}, journal = {International Journal of Molecular Sciences}, number = {18}, publisher = {Molecular Diversity Preservation International}, address = {Basel}, issn = {1422-0067}, doi = {10.3390/ijms21186634}, pages = {24}, year = {2020}, abstract = {Bone pathology is frequent in stressed individuals. A comprehensive examination of mechanisms linking life stress, depression and disturbed bone homeostasis is missing. In this translational study, mice exposed to early life stress (MSUS) were examined for bone microarchitecture (μCT), metabolism (qPCR/ELISA), and neuronal stress mediator expression (qPCR) and compared with a sample of depressive patients with or without early life stress by analyzing bone mineral density (BMD) (DXA) and metabolic changes in serum (osteocalcin, PINP, CTX-I). MSUS mice showed a significant decrease in NGF, NPYR1, VIPR1 and TACR1 expression, higher innervation density in bone, and increased serum levels of CTX-I, suggesting a milieu in favor of catabolic bone turnover. MSUS mice had a significantly lower body weight compared to control mice, and this caused minor effects on bone microarchitecture. Depressive patients with experiences of childhood neglect also showed a catabolic pattern. A significant reduction in BMD was observed in depressive patients with childhood abuse and stressful life events during childhood. Therefore, future studies on prevention and treatment strategies for both mental and bone disease should consider early life stress as a risk factor for bone pathologies.}, language = {en} } @article{vanKleunenEsslPergletal.2018, author = {van Kleunen, Mark and Essl, Franz and Pergl, Jan and Brundu, Giuseppe and Carboni, Marta and Dullinger, Stefan and Early, Regan and Gonzalez-Moreno, Pablo and Groom, Quentin J. M. and Hulme, Philip E. and Kueffer, Christoph and K{\"u}hn, Ingolf and Maguas, Cristina and Maurel, Noelie and Novoa, Ana and Parepa, Madalin and Pysek, Petr and Seebens, Hanno and Tanner, Rob and Touza, Julia and Verbrugge, Laura and Weber, Ewald and Dawson, Wayne and Kreft, Holger and Weigelt, Patrick and Winter, Marten and Klonner, Guenther and Talluto, Matthew V. and Dehnen-Schmutz, Katharina}, title = {The changing role of ornamental horticulture in alien plant invasions}, series = {Biological reviews}, volume = {93}, journal = {Biological reviews}, number = {3}, publisher = {Wiley}, address = {Hoboken}, issn = {1464-7931}, doi = {10.1111/brv.12402}, pages = {1421 -- 1437}, year = {2018}, abstract = {The number of alien plants escaping from cultivation into native ecosystems is increasing steadily. We provide an overview of the historical, contemporary and potential future roles of ornamental horticulture in plant invasions. We show that currently at least 75\% and 93\% of the global naturalised alien flora is grown in domestic and botanical gardens, respectively. Species grown in gardens also have a larger naturalised range than those that are not. After the Middle Ages, particularly in the 18th and 19th centuries, a global trade network in plants emerged. Since then, cultivated alien species also started to appear in the wild more frequently than non-cultivated aliens globally, particularly during the 19th century. Horticulture still plays a prominent role in current plant introduction, and the monetary value of live-plant imports in different parts of the world is steadily increasing. Historically, botanical gardens - an important component of horticulture - played a major role in displaying, cultivating and distributing new plant discoveries. While the role of botanical gardens in the horticultural supply chain has declined, they are still a significant link, with one-third of institutions involved in retail-plant sales and horticultural research. However, botanical gardens have also become more dependent on commercial nurseries as plant sources, particularly in North America. Plants selected for ornamental purposes are not a random selection of the global flora, and some of the plant characteristics promoted through horticulture, such as fast growth, also promote invasion. Efforts to breed non-invasive plant cultivars are still rare. Socio-economical, technological, and environmental changes will lead to novel patterns of plant introductions and invasion opportunities for the species that are already cultivated. We describe the role that horticulture could play in mediating these changes. We identify current research challenges, and call for more research efforts on the past and current role of horticulture in plant invasions. This is required to develop science-based regulatory frameworks to prevent further plant invasions.}, language = {en} } @article{BeyeOebergXinetal.2016, author = {Beye, Martin and {\"O}berg, Henrik and Xin, Hongliang and Dakovski, Georgi L. and F{\"o}hlisch, Alexander and Gladh, Jorgen and Hantschmann, Markus and Hieke, Florian and Kaya, Sarp and K{\"u}hn, Danilo and LaRue, Jerry and Mercurio, Giuseppe and Minitti, Michael P. and Mitra, Ankush and Moeller, Stefan P. and Ng, May Ling and Nilsson, Anders and Nordlund, Dennis and Norskov, Jens and {\"O}str{\"o}m, Henrik and Ogasawara, Hirohito and Persson, Mats and Schlotter, William F. and Sellberg, Jonas A. and Wolf, Martin and Abild-Pedersen, Frank and Pettersson, Lars G. M. and Wurth, Wilfried}, title = {Chemical Bond Activation Observed with an X-ray Laser}, series = {The journal of physical chemistry letters}, volume = {7}, journal = {The journal of physical chemistry letters}, publisher = {American Chemical Society}, address = {Washington}, issn = {1948-7185}, doi = {10.1021/acs.jpclett.6b01543}, pages = {3647 -- 3651}, year = {2016}, abstract = {The concept of bonding and antibonding orbitals is fundamental in chemistry. The population of those orbitals and the energetic difference between the two reflect the strength of the bonding interaction. Weakening the bond is expected to reduce this energetic splitting, but the transient character of bond-activation has so far prohibited direct experimental access. Here we apply time-resolved soft X-ray spectroscopy at a free electron laser to directly observe the decreased bonding antibonding splitting following bond-activation using an ultrashort optical laser pulse.}, language = {en} } @article{KuehnScherbaumRiggelsen2009, author = {K{\"u}hn, Nicolas M. and Scherbaum, Frank and Riggelsen, Carsten}, title = {Deriving empirical ground-motion models : balancing data constraints and physical assumptions to optimize prediction capability}, issn = {0037-1106}, doi = {10.1785/0120080136}, year = {2009}, abstract = {Empirical ground-motion models used in seismic hazard analysis are commonly derived by regression of observed ground motions against a chosen set of predictor variables. Commonly, the model building process is based on residual analysis and/or expert knowledge and/or opinion, while the quality of the model is assessed by the goodness-of-fit to the data. Such an approach, however, bears no immediate relation to the predictive power of the model and with increasing complexity of the models is increasingly susceptible to the danger of overfitting. Here, a different, primarily data-driven method for the development of ground-motion models is proposed that makes use of the notion of generalization error to counteract the problem of overfitting. Generalization error directly estimates the average prediction error on data not used for the model generation and, thus, is a good criterion to assess the predictive capabilities of a model. The approach taken here makes only few a priori assumptions. At first, peak ground acceleration and response spectrum values are modeled by flexible, nonphysical functions (polynomials) of the predictor variables. The inclusion of a particular predictor and the order of the polynomials are based on minimizing generalization error. The approach is illustrated for the next generation of ground-motion attenuation dataset. The resulting model is rather complex, comprising 48 parameters, but has considerably lower generalization error than functional forms commonly used in ground-motion models. The model parameters have no physical meaning, but a visual interpretation is possible and can reveal relevant characteristics of the data, for example, the Moho bounce in the distance scaling. In a second step, the regression model is approximated by an equivalent stochastic model, making it physically interpretable. The resulting resolvable stochastic model parameters are comparable to published models for western North America. In general, for large datasets generalization error minimization provides a viable method for the development of empirical ground-motion models.}, language = {en} } @phdthesis{Kuehn2010, author = {K{\"u}hn, Nicolas M.}, title = {Empirical ground-motion models for probabilistic seismic hazard analysis : a graphical model perspective}, address = {Potsdam}, pages = {125 S.}, year = {2010}, language = {en} } @misc{KisslingDormannGroeneveldetal.2012, author = {Kissling, W. D. and Dormann, Carsten F. and Groeneveld, Juergen and Hickler, Thomas and K{\"u}hn, Ingolf and McInerny, Greg J. and Montoya, Jose M. and R{\"o}mermann, Christine and Schiffers, Katja and Schurr, Frank Martin and Singer, Alexander and Svenning, Jens-Christian and Zimmermann, Niklaus E. and O'Hara, Robert B.}, title = {Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents}, series = {Journal of biogeography}, volume = {39}, journal = {Journal of biogeography}, number = {12}, publisher = {Wiley-Blackwell}, address = {Hoboken}, issn = {0305-0270}, doi = {10.1111/j.1365-2699.2011.02663.x}, pages = {2163 -- 2178}, year = {2012}, abstract = {Aim Biotic interactions within guilds or across trophic levels have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of species interaction distribution models (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co-occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non-stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio-temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co-occurrence datasets across large-scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio-temporal data on biotic interactions in multispecies communities.}, language = {en} } @article{HermkesKuehnRiggelsen2014, author = {Hermkes, Marcel and K{\"u}hn, Nicolas M. and Riggelsen, Carsten}, title = {Simultaneous quantification of epistemic and aleatory uncertainty in GMPEs using Gaussian process regression}, series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, volume = {12}, journal = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, number = {1}, publisher = {Springer}, address = {Dordrecht}, issn = {1570-761X}, doi = {10.1007/s10518-013-9507-7}, pages = {449 -- 466}, year = {2014}, abstract = {This paper presents a Bayesian non-parametric method based on Gaussian Process (GP) regression to derive ground-motion models for peak-ground parameters and response spectral ordinates. Due to its non-parametric nature there is no need to specify any fixed functional form as in parametric regression models. A GP defines a distribution over functions, which implicitly expresses the uncertainty over the underlying data generating process. An advantage of GP regression is that it is possible to capture the whole uncertainty involved in ground-motion modeling, both in terms of aleatory variability as well as epistemic uncertainty associated with the underlying functional form and data coverage. The distribution over functions is updated in a Bayesian way by computing the posterior distribution of the GP after observing ground-motion data, which in turn can be used to make predictions. The proposed GP regression models is evaluated on a subset of the RESORCE data base for the SIGMA project. The experiments show that GP models have a better generalization error than a simple parametric regression model. A visual assessment of different scenarios demonstrates that the inferred GP models are physically plausible.}, language = {en} } @article{KuehnScherbaum2015, author = {K{\"u}hn, Nico M. and Scherbaum, Frank}, title = {Ground-motion prediction model building: a multilevel approach}, series = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, volume = {13}, journal = {Bulletin of earthquake engineering : official publication of the European Association for Earthquake Engineering}, number = {9}, publisher = {Springer}, address = {Dordrecht}, issn = {1570-761X}, doi = {10.1007/s10518-015-9732-3}, pages = {2481 -- 2491}, year = {2015}, abstract = {A Bayesian ground-motion model is presented that directly estimates the coefficients of the model and the correlation between different ground-motion parameters of interest. The model is developed as a multi-level model with levels for earthquake, station and record terms. This separation allows to estimate residuals for each level and thus the estimation of the associated aleatory variability. In particular, the usually estimated within-event variability is split into a between-station and between-record variability. In addition, the covariance structure between different ground-motion parameters of interest is estimated for each level, i.e. directly the between-event, between-station and between-record correlation coefficients are available. All parameters of the model are estimated via Bayesian inference, which allows to assess their epistemic uncertainty in a principled way. The model is developed using a recently compiled European strong-motion database. The target variables are peak ground velocity, peak ground acceleration and spectral acceleration at eight oscillator periods. The model performs well with respect to its residuals, and is similar to other ground-motion models using the same underlying database. The correlation coefficients are similar to those estimated for other parts of the world, with nearby periods having a high correlation. The between-station, between-event and between-record correlations follow generally a similar trend.}, language = {en} } @article{KuehnRiggelsenScherbaum2011, author = {K{\"u}hn, Nicolas M. and Riggelsen, Carsten and Scherbaum, Frank}, title = {Modeling the joint probability of earthquake, site, and ground-motion parameters using bayesian networks}, series = {Bulletin of the Seismological Society of America}, volume = {101}, journal = {Bulletin of the Seismological Society of America}, number = {1}, publisher = {Seismological Society of America}, address = {El Cerrito}, issn = {0037-1106}, doi = {10.1785/0120100080}, pages = {235 -- 249}, year = {2011}, abstract = {Bayesian networks are a powerful and increasingly popular tool for reasoning under uncertainty, offering intuitive insight into (probabilistic) data-generating processes. They have been successfully applied to many different fields, including bioinformatics. In this paper, Bayesian networks are used to model the joint-probability distribution of selected earthquake, site, and ground-motion parameters. This provides a probabilistic representation of the independencies and dependencies between these variables. In particular, contrary to classical regression, Bayesian networks do not distinguish between target and predictors, treating each variable as random variable. The capability of Bayesian networks to model the ground-motion domain in probabilistic seismic hazard analysis is shown for a generic situation. A Bayesian network is learned based on a subset of the Next Generation Attenuation (NGA) dataset, using 3342 records from 154 earthquakes. Because no prior assumptions about dependencies between particular parameters are made, the learned network displays the most probable model given the data. The learned network shows that the ground-motion parameter (horizontal peak ground acceleration, PGA) is directly connected only to the moment magnitude, Joyner-Boore distance, fault mechanism, source-to-site azimuth, and depth to a shear-wave horizon of 2: 5 km/s (Z2.5). In particular, the effect of V-S30 is mediated by Z2.5. Comparisons of the PGA distributions based on the Bayesian networks with the NGA model of Boore and Atkinson (2008) show a reasonable agreement in ranges of good data coverage.}, language = {en} }