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Arsenic-containing fatty acids are a group of fat-soluble arsenic species (arsenolipids) which are present in marine fish and other seafood. Recently, it has been shown that arsenic-containing hydrocarbons, another group of arsenolipids, exert toxicity in similar concentrations comparable to arsenite although the toxic modes of action differ. Hence, a risk assessment of arsenolipids is urgently needed. In this study the cellular toxicity of a saturated (AsFA 362) and an unsaturated (AsFA 388) arsenic-containing fatty acid and three of their proposed metabolites (DMA(V), DMAPr and thio-DMAPr) were investigated in human liver cells (HepG2). Even though both arsenic-containing fatty acids were less toxic as compared to arsenic-containing hydrocarbons and arsenite, significant effects were observable at mu M concentrations. DMA(V) causes effects in a similar concentration range and it could be seen that it is metabolised to its highly toxic thio analogue thio-DMA(V) in HepG2 cells. Nevertheless, DMAPr and thio-DMAPr did not exert any cytotoxicity. In summary, our data indicate that risks to human health related to the presence of arsenic-containing fatty acids in marine food cannot be excluded. This stresses the need for a full in vitro and in vivo toxicological characterisation of these arsenolipids.
We prove the hitherto hypothesized sequential dissociation of Fe(CO)(5) in the gas phase upon photoexcitation at 266 nm via a singlet pathway with time-resolved valence and core-level photoelectron spectroscopy with an x-ray free-electron laser. Valence photoelectron spectra are used to identify free CO molecules and to determine the time constants of stepwise dissociation to Fe(CO)(4) within the temporal resolution of the experiment and further to Fe(CO)(3) within 3 ps. Fe 3p core-level photoelectron spectra directly reflect the singlet spin state of the Fe center in Fe(CO)(5), Fe(CO)(4), and Fe(CO)(3) showing that the dissociation exclusively occurs along a singlet pathway without triplet-state contribution. Our results are important for assessing intra- and intermolecular relaxation processes in the photodissociation dynamics of the prototypical Fe(CO)(5) complex in the gas phase and in solution, and they establish time-resolved core-level photoelectron spectroscopy as a powerful tool for determining the multiplicity of transition metals in photochemical reactions of coordination complexes. Published by AIP Publishing.
Ventral striatum and amygdala activity as convergence sites for early adversity and conduct disorder
(2017)
Childhood family adversity (CFA) increases the risk for conduct disorder (CD) and has been associated with alterations in regions of affective processing like ventral striatum (VS) and amygdala. However, no study so far has demonstrated neural converging effects of CFA and CD in the same sample. At age 25 years, functional MRI data during two affective tasks, i.e. a reward (N = 171) and a face-matching paradigm (N = 181) and anatomical scans (N = 181) were acquired in right-handed currently healthy participants of an epidemiological study followed since birth. CFA during childhood was determined using a standardized parent interview. Disruptive behaviors and CD diagnoses during childhood and adolescence were obtained by diagnostic interview (2–19 years), temperamental reward dependence was assessed by questionnaire (15 and 19 years).
CFA predicted increased CD and amygdala volume. Both exposure to CFA and CD were associated with a decreased VS response during reward anticipation and blunted amygdala activity during face-matching. CD mediated the effect of CFA on brain activity. Temperamental reward dependence was negatively correlated with CFA and CD and positively with VS activity. These findings underline the detrimental effects of CFA on the offspring's affective processing and support the importance of early postnatal intervention programs aiming to reduce childhood adversity factors.
Ecological regime shifts and carbon cycling in aquatic systems have both been subject to increasing attention in recent years, yet the direct connection between these topics has remained poorly understood. A four-fold increase in sedimentation rates was observed within the past 50 years in a shallow eutrophic lake with no surface in-or outflows. This change coincided with an ecological regime shift involving the complete loss of submerged macrophytes, leading to a more turbid, phytoplankton-dominated state. To determine whether the increase in carbon (C) burial resulted from a comprehensive transformation of C cycling pathways in parallel to this regime shift, we compared the annual C balances (mass balance and ecosystem budget) of this turbid lake to a similar nearby lake with submerged macrophytes, a higher transparency, and similar nutrient concentrations. C balances indicated that roughly 80% of the C input was permanently buried in the turbid lake sediments, compared to 40% in the clearer macrophyte-dominated lake. This was due to a higher measured C burial efficiency in the turbid lake, which could be explained by lower benthic C mineralization rates. These lower mineralization rates were associated with a decrease in benthic oxygen availability coinciding with the loss of submerged macrophytes. In contrast to previous assumptions that a regime shift to phytoplankton dominance decreases lake heterotrophy by boosting whole-lake primary production, our results suggest that an equivalent net metabolic shift may also result from lower C mineralization rates in a shallow, turbid lake. The widespread occurrence of such shifts may thus fundamentally alter the role of shallow lakes in the global C cycle, away from channeling terrestrial C to the atmosphere and towards burying an increasing amount of C.
The Low Earth Orbit (LEO) experiment Biology and Mars Experiment (BIOMEX) is an interdisciplinary and international space research project selected by ESA. The experiment will be accommodated on the space exposure facility EXPOSE-R2 on the International Space Station (ISS) and is foreseen to be launched in 2013. The prime objective of BIOMEX is to measure to what extent biomolecules, such as pigments and cellular components, are resistant to and able to maintain their stability under space and Mars-like conditions. The results of BIOMEX will be relevant for space proven biosignature definition and for building a biosignature data base (e.g. the proposed creation of an international Raman library). The library will be highly relevant for future space missions such as the search for life on Mars. The secondary scientific objective is to analyze to what extent terrestrial extremophiles are able to survive in space and to determine which interactions between biological samples and selected minerals (including terrestrial, Moon- and Mars analogs) can be observed under space and Mars-like conditions. In this context, the Moon will be an additional platform for performing similar experiments with negligible magnetic shielding and higher solar and galactic irradiation compared to LEO. Using the Moon as an additional astrobiological exposure platform to complement ongoing astrobiological LEO investigations could thus enhance the chances of detecting organic traces of life on Mars. We present a lunar lander mission with two related objectives: a lunar lander equipped with Raman and PanCam instruments which can analyze the lunar surface and survey an astrobiological exposure platform. This dual use of testing mission technology together with geo- and astrobiological analyses will significantly increase the science return, and support the human preparation objectives. It will provide knowledge about the Moon's surface itself and, in addition, monitor the stability of life-markers, such as cells, cell components and pigments, in an extraterrestrial environment with much closer radiation properties to the surface of Mars. The combination of a Raman data base of these data together with data from LEO and space simulation experiments, will lead to further progress on the analysis and interpretation of data that we will obtain from future Moon and Mars exploration missions.
This study examines a natural, rapid, fivefold increase in dissolved organic carbon (DOC) concentrations in a temperate shallow lake, describing the processes by which increased DOC resulted in anoxic conditions and altered existing carbon cycling pathways. High precipitation for two consecutive years led to rising water levels and the flooding of adjacent degraded peatlands. Leaching from the flooded soils provided an initial increase in DOC concentrations (from a 2010 mean of 12 +/- 1 mg L-1 to a maximum concentration of 53 mg L-1 by June 2012). Increasing water levels, DOC, and phytoplankton concentrations reduced light reaching the sediment surface, eliminating most benthic primary production and promoting anoxia in the hypolimnion. From January to June 2012 there was a sudden increase in total phosphorus (from 57 mg L-1 to 216 mg L-1), DOC (from 24.6 mg L-1 to 53 mg L-1), and iron (from 0.12 mg L-1 to 1.07 mg L-1) concentrations, without any further large fluxes in water levels. We suggest that anoxic conditions at the sediment surface and flooded soils produced a dramatic release of these chemicals that exacerbated brownification and eutrophication, creating anoxic conditions that persisted roughly 6 months below a water depth of 1 m and extended periodically to the water surface. This brownification-anoxia feedback loop resulted in a near-complete loss of macroinvertebrate and fish populations, and increased surface carbon dioxide (CO2) emissions by an order of magnitude relative to previous years.
Improving our understanding of biodiversity and ecosystem functioning and our capacity to inform ecosystem management requires an integrated framework for functional biodiversity research (FBR). However, adequate integration among empirical approaches (monitoring and experimental) and modelling has rarely been achieved in FBR. We offer an appraisal of the issues involved and chart a course towards enhanced integration. A major element of this path is the joint orientation towards the continuous refinement of a theoretical framework for FBR that links theory testing and generalization with applied research oriented towards the conservation of biodiversity and ecosystem functioning. We further emphasize existing decision-making frameworks as suitable instruments to practically merge these different aims of FBR and bring them into application. This integrated framework requires joint research planning, and should improve communication and stimulate collaboration between modellers and empiricists, thereby overcoming existing reservations and prejudices. The implementation of this integrative research agenda for FBR requires an adaptation in most national and international funding schemes in order to accommodate such joint teams and their more complex structures and data needs.
Mental disorders are among the greatest medical and social challenges facing us. They can occur at all stages of life and are among the most important commonly occurring diseases. In Germany 28 % of the population suffer from a mental disorder every year, while the lifetime risk of suffering from a mental disorder is almost 50 %. Mental disorders cause great suffering for those affected and their social network. Quantitatively speaking, they can be considered to be among those diseases creating the greatest burden for society due to reduced productivity, absence from work and premature retirement. The Federal Ministry of Education and Research is funding a new research network from 2015 to 2019 with up to 35 million euros to investigate mental disorders in order to devise and develop better therapeutic measures and strategies for this population by means of basic and translational clinical research. This is the result of a competitive call for research proposals entitled research network for mental diseases. It is a nationwide network of nine consortia with up to ten psychiatric and clinical psychology partner institutions from largely university-based research facilities for adults and/or children and adolescents. Furthermore, three cross-consortia platform projects will seek to identify shared causes of diseases and new diagnostic modalities for anxiety disorders, attention deficit hyperactivity disorders (ADHS), autism, bipolar disorders, depression, schizophrenia and psychotic disorders as well as substance-related and addictive disorders. The spectrum of therapeutic approaches to be examined ranges from innovative pharmacological and psychotherapeutic treatment to novel brain stimulation procedures. In light of the enormous burden such diseases represent for society as a whole, a sustainable improvement in the financial support for those researching mental disorders seems essential. This network aims to become a nucleus for long overdue and sustained support for a German center for mental disorders.
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.
Thanks to dedicated long-term missions like Voyager and GOES over the past 50 years, much insight has been gained on the activity of our Sun, the solar wind, its interaction with the interstellar medium, and, thus, about the formation, the evolution, and the structure of the heliosphere. Additionally, with the help of multi-wavelength observations by the Hubble Space Telescope, Kepler, and TESS, we not only were able to detect a variety of extrasolar planets and exomoons but also to study the characteristics of their host stars, and thus became aware that other stars drive bow shocks and astrospheres. Although features like, e.g., stellar winds, could not be measured directly, over the past years several techniques have been developed allowing us to indirectly derive properties like stellar mass-loss rates and stellar wind speeds, information that can be used as direct input to existing astrospheric modeling codes. In this review, the astrospheric modeling efforts of various stars will be presented. Starting with the heliosphere as a benchmark of astrospheric studies, investigating the paleo-heliospheric changes and the Balmer H alpha projections to 1 pc, we investigate the surroundings of cool and hot stars, but also of more exotic objects like neutron stars. While pulsar wind nebulae (PWNs) might be a source of high-energy galactic cosmic rays (GCRs), the astrospheric environments of cool and hot stars form a natural shield against GCRs. Their modulation within these astrospheres, and the possible impact of turbulence, are also addressed. This review shows that all of the presented modeling efforts are in excellent agreement with currently available observations.
On the ALMA observability of nascent massive multiple systems formed by gravitational instability
(2019)
Massive young stellar objects (MYSOs) form during the collapse of high-mass pre-stellar cores, where infalling molecular material is accreted through a centrifugally balanced accretion disc that is subject to efficient gravitational instabilities. In the resulting fragmented accretion disc of the MYSO, gaseous clumps and low-mass stellar companions can form, which will influence the future evolution of massive protostars in the Hertzsprung-Russell diagram. We perform dust continuum radiative transfer calculations and compute synthetic images of disc structures modelled by the gravito-radiation-hydrodynamics simulation of a forming MYSO, in order to investigate the Atacama Large Millimeter/submillimeter Array (alma) observability of circumstellar gaseous clumps and forming multiple systems. Both spiral arms and gaseous clumps located at similar or equal to a few from the protostar can be resolved by interferometric alma Cycle 7 C43-8 and C43-10 observations at band 6 (), using a maximal 0.015 aracsec beam angular resolution and at least exposure time for sources at distances of . Our study shows that substructures are observable regardless of their viewing geometry or can be inferred in the case of an edge-viewed disc. The observation probability of the clumps increases with the gradually increasing efficiency of gravitational instability at work as the disc evolves. As a consequence, large discs around MYSOs close to the zero-age-main-sequence line exhibit more substructures than at the end of the gravitational collapse. Our results motivate further observational campaigns devoted to the close surroundings of the massive protostars S255IR-NIRS3 and NGC 6334I-MM1, whose recent outbursts are a probable signature of disc fragmentation and accretion variability.
Wolf-Rayet stars are amongst the rarest but also most intriguing massive stars. Their extreme stellar winds induce famous multiwavelength circumstellar gas nebulae of various morphologies, spanning from circles and rings to bipolar shapes. This study is devoted to the investigation of the formation of young, asymmetric Wolf-Rayet gas nebulae and we present a 2.5-dimensional magneto-hydrodynamical toy model for the simulation of Wolf-Rayet gas nebulae generated by wind-wind interaction. Our method accounts for stellar wind asymmetries, rotation, magnetization, evolution, and mixing of materials. It is found that the morphology of the Wolf-Rayet nebulae of blue supergiant ancestors is tightly related to the wind geometry and to the stellar phase transition time interval, generating either a broadened peanut-like or a collimated jet-like gas nebula. Radiative transfer calculations of our Wolf-Rayet nebulae for dust infrared emission at 24 mu m show that the projected diffuse emission can appear as oblate, bipolar, ellipsoidal, or ring structures. Important projection effects are at work in shaping observed Wolf-Rayet nebulae. This might call a revision of the various classifications of Wolf-Rayet shells, which are mostly based on their observed shape. Particularly, our models question the possibility of producing pre-Wolf-Rayet wind asymmetries, responsible for bipolar nebulae like NGC 6888, within the single red supergiant evolution channel scenario. We propose that bipolar Wolf-Rayet nebulae can only be formed within the red supergiant scenario by multiple/merged massive stellar systems, or by single high-mass stars undergoing additional, e.g. blue supergiant, evolutionary stages prior to the Wolf-Rayet phase.
A very small fraction of (runaway) massive stars have masses exceeding 60-70 M-circle dot and are predicted to evolve as luminous blue variable and Wolf-Rayet stars before ending their lives as core-collapse supernovae. Our 2D axisymmetric hydrodynamical simulations explore how a fast wind (2000 km s(-1)) and high mass-loss rate (10(-5)M(circle dot) yr(-1)) can impact the morphology of the circumstellar medium. It is shaped as 100 pc-scale wind nebula that can be pierced by the driving star when it supersonically moves with velocity 20-40 km s(-1) through the interstellar medium (ISM) in the Galactic plane. The motion of such runaway stars displaces the position of the supernova explosion out of their bow shock nebula, imposing asymmetries to the eventual shock wave expansion and engendering Cygnus-loop-like supernova remnants. We conclude that the size (up to more than 200 pc) of the filamentary wind cavity in which the chemically enriched supernova ejecta expand, mixing efficiently the wind and ISM materials by at least 10 per cent in number density, can be used as a tracer of the runaway nature of the very massive progenitors of such 0.1Myr old remnants. Our results motivate further observational campaigns devoted to the bow shock of the very massive stars BD+43 degrees 3654 and to the close surroundings of the synchrotron-emitting Wolf-Rayet shell G2.4+1.4.
Core-collapse supernova remnants are structures of the interstellar medium (ISM) left behind the explosive death of most massive stars ( ?40 M-?). Since they result in the expansion of the supernova shock wave into the gaseous environment shaped by the star's wind history, their morphology constitutes an insight into the past evolution of their progenitor star. Particularly, fast-mo ving massiv e stars can produce asymmetric core-collapse superno va remnants. We inv estigate the mixing of materials in core-collapse supernova remnants generated by a moving massive 35 M-? star, in a magnetized ISM. Stellar rotation and the wind magnetic field are time-dependently included into the models which follow the entire evolution of the stellar surroundings from the zero-age main-sequence to 80 kyr after the supernova explosion. It is found that very little main-sequence material is present in remnants from moving stars, that the Wolf-Rayet wind mixes very efficiently within the 10 kyr after the explosion, while the red supergiant material is still unmixed by 30 per cent within 50 kyr after the supernova. Our results indicate that the faster the stellar motion, the more complex the internal organization of the supernova remnant and the more ef fecti ve the mixing of ejecta therein. In contrast, the mixing of stellar wind material is only weakly affected by progenitor motion, if at all.