Current Status and Evaluation of SpaceX Starship

The aim of this thesis is to evaluate the feasibility of SpaceX’s current mission plans, based on the current Starship system, assumptions made and selected technologies, which are established in a baseline scenario and defined in system designs of the subsystems of Starship and the ISRU system. Nuclear reactors are to provide the power supply on Mars. Finally, the feasibility analysis is carried out using previously determined key figures and proves that with 100 t payload and 125 t empty mass, the mission plans are feasible in terms of launch and propellant mass as well as the required velocity changes. However, the analysis shows that the propellant production system and the nuclear reactors of the ISRU system in particular, with their required and produced power respectively, clearly exceed the current technology readiness level, resulting in high costs and also critical mass and volume. It is expected that it will take ten years before the required reactors are flight-ready, which is why the scope and timeframe of SpaceX’s mission plans are ultimately considered unfeasible. The elevators, whose development has not yet begun, and Starship’s electrical power system for 100 people in the future, which would require huge solar arrays, are also seen as major hurdles

DNA methylation-based classification of central nervous system tumours

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging - with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

DNA methylation-based classification of central nervous system tumours

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challengingwith substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology