@article{BauerBanaschewskiHeinzetal.2016, author = {Bauer, M. and Banaschewski, Tobias and Heinz, A. and Kamp-Becker, I. and Meyer-Lindenberg, A. and Padberg, F. and Rapp, Michael A. and Rupprecht, R. and Schneider, F. and Schulze, T. G. and Wittchen, Hans-Ulrich}, title = {The German Research Network for mental Disorders}, series = {Der Nervenarzt : Organ der Deutschen Gesellschaft f{\~A}¼r Psychiatrie, Psychotherapie und Nervenheilkunde ; Mitteilungsblatt der Deutschen Gesellschaft f{\~A}¼r Neurologie}, volume = {87}, journal = {Der Nervenarzt : Organ der Deutschen Gesellschaft f{\~A}¼r Psychiatrie, Psychotherapie und Nervenheilkunde ; Mitteilungsblatt der Deutschen Gesellschaft f{\~A}¼r Neurologie}, publisher = {Springer}, address = {New York}, issn = {0028-2804}, doi = {10.1007/s00115-016-0169-y}, pages = {989 -- 1010}, year = {2016}, abstract = {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.}, language = {de} } @article{VaidSomaniRussaketal.2020, author = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.}, title = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation}, series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, volume = {22}, journal = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, number = {11}, publisher = {Healthcare World}, address = {Richmond, Va.}, issn = {1439-4456}, doi = {10.2196/24018}, pages = {19}, year = {2020}, abstract = {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.}, language = {en} } @article{HerbstBaalmannBykovetal.2022, author = {Herbst, Konstantin and Baalmann, Lennart R. and Bykov, Andrei and Engelbrecht, N. Eugene and Ferreira, Stefan E. S. and Izmodenov, Vladislav V. and Korolkov, Sergey D. and Levenfish, Ksenia P. and Linsky, Jeffrey L. and Meyer, Dominique M. -A. and Scherer, Klaus and Strauss, R. Du Toit}, title = {Astrospheres of planet-hosting cool stars and beyond when modeling meets observations}, series = {Space science reviews}, volume = {218}, journal = {Space science reviews}, number = {4}, publisher = {Springer Nature}, address = {Dordrecht}, issn = {0038-6308}, doi = {10.1007/s11214-022-00894-3}, pages = {46}, year = {2022}, abstract = {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.}, language = {en} } @article{MeyerKreplinKrausetal.2019, author = {Meyer, Dominique M.-A. and Kreplin, Alexander and Kraus, S. and Vorobyov, E. I. and Haemmerl{\´e}, Lionel and Eisl{\"o}ffel, Jochen}, title = {On the ALMA observability of nascent massive multiple systems formed by gravitational instability}, series = {Monthly notices of the Royal Astronomical Society}, volume = {487}, journal = {Monthly notices of the Royal Astronomical Society}, number = {4}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0035-8711}, doi = {10.1093/mnras/stz1585}, pages = {4473 -- 4491}, year = {2019}, abstract = {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.}, language = {en} } @article{Meyer2021, author = {Meyer, Dominique M.-A.}, title = {On the bipolarity of Wolf-Rayet nebulae}, series = {Monthly notices of the Royal Astronomical Society}, volume = {507}, journal = {Monthly notices of the Royal Astronomical Society}, number = {4}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0035-8711}, doi = {10.1093/mnras/stab2426}, pages = {4697 -- 4714}, year = {2021}, abstract = {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.}, language = {en} } @article{MeyerPetrovPohl2020, author = {Meyer, Dominique M.-A. and Petrov, Mykola and Pohl, Martin}, title = {Wind nebulae and supernova remnants of very massive stars}, series = {Monthly notices of the Royal Astronomical Society}, volume = {493}, journal = {Monthly notices of the Royal Astronomical Society}, number = {3}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0035-8711}, doi = {10.1093/mnras/staa554}, pages = {3548 -- 3564}, year = {2020}, abstract = {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.}, language = {en} } @phdthesis{Meyer2023, author = {Meyer, Dominique M.-A.}, title = {The circumstellar medium of massive stars}, school = {Universit{\"a}t Potsdam}, pages = {318}, year = {2023}, language = {en} } @article{MeyerPohlPetrovetal.2023, author = {Meyer, Dominique M.-A. and Pohl, Martin and Petrov, Miroslav and Egberts, Kathrin}, title = {Mixing of materials in magnetized core-collapse supernova remnants}, series = {Monthly notices of the Royal Astronomical Society}, volume = {521}, journal = {Monthly notices of the Royal Astronomical Society}, number = {4}, publisher = {Oxford Univ. Press}, address = {Oxford}, issn = {0035-8711}, doi = {10.1093/mnras/stad906}, pages = {5354 -- 5371}, year = {2023}, abstract = {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.}, language = {en} }