@article{FoerstnerBoettgerMoldavskietal.2023,
  author    = {F{\"o}rstner, Bernd Rainer and B{\"o}ttger, Sarah Jane and Moldavski, Alexander and Bajbouj, Malek and Pfennig, Andrea and Manook, Andre and Ising, Marcus and Pittig, Andre and Heinig, Ingmar and Heinz, Andreas and Mathiak, Klaus and Schulze, Thomas G. and Schneider, Frank and Kamp-Becker, Inge and Meyer-Lindenberg, Andreas and Padberg, Frank and Banaschewski, Tobias and Bauer, Michael and Rupprecht, Rainer and Wittchen, Hans-Ulrich and Rapp, Michael A. and Tschorn, Mira},
  title     = {The associations of positive and negative valence systems, cognitive systems and social processes on disease severity in anxiety and depressive disorders},
  series = {Frontiers in psychiatry},
  volume    = {14},
  journal   = {Frontiers in psychiatry},
  publisher = {Frontiers Research Foundation},
  address   = {Lausanne},
  issn      = {1664-0640},
  doi       = {10.3389/fpsyt.2023.1161097},
  pages     = {10},
  year      = {2023},
  abstract  = {Background Anxiety and depressive disorders share common features of mood dysfunctions. This has stimulated interest in transdiagnostic dimensional research as proposed by the Research Domain Criteria (RDoC) approach by the National Institute of Mental Health (NIMH) aiming to improve the understanding of underlying disease mechanisms. The purpose of this study was to investigate the processing of RDoC domains in relation to disease severity in order to identify latent disorder-specific as well as transdiagnostic indicators of disease severity in patients with anxiety and depressive disorders. Methods Within the German research network for mental disorders, 895 participants (n = 476 female, n = 602 anxiety disorder, n = 257 depressive disorder) were recruited for the Phenotypic, Diagnostic and Clinical Domain Assessment Network Germany (PD-CAN) and included in this cross-sectional study. We performed incremental regression models to investigate the association of four RDoC domains on disease severity in patients with affective disorders: Positive (PVS) and Negative Valance System (NVS), Cognitive Systems (CS) and Social Processes (SP). Results The results confirmed a transdiagnostic relationship for all four domains, as we found significant main effects on disease severity within domain-specific models (PVS: \& beta; = -0.35; NVS: \& beta; = 0.39; CS: \& beta; = -0.12; SP: \& beta; = -0.32). We also found three significant interaction effects with main diagnosis showing a disease-specific association. Limitations The cross-sectional study design prevents causal conclusions. Further limitations include possible outliers and heteroskedasticity in all regression models which we appropriately controlled for. Conclusion Our key results show that symptom burden in anxiety and depressive disorders is associated with latent RDoC indicators in transdiagnostic and disease-specific ways.},
  language  = {en}
}
@article{ZeuschnerParpiievPezeriletal.2019,
  author    = {Zeuschner, Steffen Peer and Parpiiev, Tymur and Pezeril, Thomas and Hillion, Arnaud and Dumesnil, Karine and Anane, Abdelmadjid and Pudell, Jan-Etienne and Willig, Lisa and R{\"o}ssle, Matthias and Herzog, Marc and Reppert, Alexander von and Bargheer, Matias},
  title     = {Tracking picosecond strain pulses in heterostructures that exhibit giant magnetostriction},
  series = {Structural Dynamics},
  volume    = {6},
  journal   = {Structural Dynamics},
  number    = {2},
  publisher = {AIP Publishing LLC},
  address   = {Melville, NY},
  issn      = {2329-7778},
  doi       = {10.1063/1.5084140},
  pages     = {9},
  year      = {2019},
  abstract  = {We combine ultrafast X-ray diffraction (UXRD) and time-resolved Magneto-Optical Kerr Effect (MOKE) measurements to monitor the strain pulses in laser-excited TbFe2/Nb heterostructures. Spatial separation of the Nb detection layer from the laser excitation region allows for a background-free characterization of the laser-generated strain pulses. We clearly observe symmetric bipolar strain pulses if the excited TbFe2 surface terminates the sample and a decomposition of the strain wavepacket into an asymmetric bipolar and a unipolar pulse, if a SiO2 glass capping layer covers the excited TbFe2 layer. The inverse magnetostriction of the temporally separated unipolar strain pulses in this sample leads to a MOKE signal that linearly depends on the strain pulse amplitude measured through UXRD. Linear chain model simulations accurately predict the timing and shape of UXRD and MOKE signals that are caused by the strain reflections from multiple interfaces in the heterostructure.},
  language  = {en}
}
@article{BorchertMockTomczaketal.2021,
  author    = {Borchert, Florian and Mock, Andreas and Tomczak, Aurelie and H{\"u}gel, Jonas and Alkarkoukly, Samer and Knurr, Alexander and Volckmar, Anna-Lena and Stenzinger, Albrecht and Schirmacher, Peter and Debus, J{\"u}rgen and J{\"a}ger, Dirk and Longerich, Thomas and Fr{\"o}hling, Stefan and Eils, Roland and Bougatf, Nina and Sax, Ulrich and Schapranow, Matthieu-Patrick},
  title     = {Knowledge bases and software support for variant interpretation in precision oncology},
  series = {Briefings in bioinformatics},
  volume    = {22},
  journal   = {Briefings in bioinformatics},
  number    = {6},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1467-5463},
  doi       = {10.1093/bib/bbab134},
  pages     = {17},
  year      = {2021},
  abstract  = {Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.},
  language  = {en}
}