[After the pandemic is before the pandemic: And how interdisciplinary research can help here]. / Nach der Pandemie ist vor der Pandemie: Und wie interdisziplinäre Forschung hier helfen kann.

Aviation is among the social sectors most impacted by the COVID-19 pandemic, and at the same time has contributed to the rapid global spread of the SARS-CoV-2 virus. SARS-CoV-2 is one of the coronaviruses that have led to outbreaks such as MERS-CoV in the past. This group of pathogens, as well as others that may be unknown at this time, will continue to challenge our society in the future. In order to be able to react better, a research training group was established at DLR in cooperation with 6 institutes, which will develop interdisciplinary approaches to researching and combating current and future pandemics. Engineers, physicists, software developers, biologists and physicians are working closely together on new concepts and the development of interdisciplinary knowledge in order to better control and contain future pandemics and to be able to react in a more targeted manner. One focus is the reduction of germ contamination in airplanes but also in other means of public transport such as buses and trains. In this review, we provide an overview of the baseline situation and possible approaches to address future pandemic challenges.

CutFEM forward modeling for EEG source analysis.

Introduction: Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction effects in the human head, represented by a partial differential equation which can be solved using the finite element method (FEM). FEM offers flexibility when modeling anisotropic tissue conductivities but requires a volumetric discretization, a mesh, of the head domain. Structured hexahedral meshes are easy to create in an automatic fashion, while tetrahedral meshes are better suited to model curved geometries. Tetrahedral meshes, thus, offer better accuracy but are more difficult to create. Methods: We introduce CutFEM for EEG forward simulations to integrate the strengths of hexahedra and tetrahedra. It belongs to the family of unfitted finite element methods, decoupling mesh and geometry representation. Following a description of the method, we will employ CutFEM in both controlled spherical scenarios and the reconstruction of somatosensory-evoked potentials. Results: CutFEM outperforms competing FEM approaches with regard to numerical accuracy, memory consumption, and computational speed while being able to mesh arbitrarily touching compartments. Discussion: CutFEM balances numerical accuracy, computational efficiency, and a smooth approximation of complex geometries that has previously not been available in FEM-based EEG forward modeling.

DUNEuro-A software toolbox for forward modeling in bioelectromagnetism.

Accurate and efficient source analysis in electro- and magnetoencephalography using sophisticated realistic head geometries requires advanced numerical approaches. This paper presents DUNEuro, a free and open-source C++ software toolbox for the numerical computation of forward solutions in bioelectromagnetism. Building upon the DUNE framework, it provides implementations of modern fitted and unfitted finite element methods to efficiently solve the forward problems of electro- and magnetoencephalography. The user can choose between a variety of different source models that are implemented. The software's aim is to provide interfaces that are extendable and easy-to-use. In order to enable a closer integration into existing analysis pipelines, interfaces to Python and MATLAB are provided. The practical use is demonstrated by a source analysis example of somatosensory evoked potentials using a realistic six-compartment head model. Detailed installation instructions and example scripts using spherical and realistic head models are appended.

Risk factors for delayed graft function after renal transplantation and their significance for long-term clinical outcome.

Abstract. Delayed graft function (DGF) remains a grieving complication after renal transplantation. In this study, we examined various factors related to organ donation, transport, and transplantation for their influence on the incidence of DGF and on long-term prognosis. The incidence of DGF, renal function after 5 years, and allograft survival were analyzed in 200 kidneys transplanted in Düsseldorf as well as in 193 partner kidneys transplanted at 43 other centers. The main risk factors for DGF were donor age, cold ischemia time (CIT) and organ shipment. DGF itself, as well as donor age, influenced the long term prognosis. A significant relationship between the partner organs regarding clinical outcome was demonstrated. Non-immunological factors strongly influence the clinical results after renal transplantation. Organs of older donors have a limited long-term prognosis. To minimize additional risks, prevention of DGF, especially by reducing CIT, should be regarded as of paramount importance.