Lay leadership dinner

Immanuel Janssen, master of ceremonies; R. Hopmann, L. Hempelmann, J. Klotz, R. Meyer, M. Miller, S. Nafzger, R. Bohlmann, speakers. Recorded May 7, 1981

TAMEP are brain tumor parenchymal cells controlling neoplastic angiogenesis and progression

Aggressive brain tumors like glioblastoma depend on support by their local environment and subsets of tumor parenchymal cells may promote specific phases of disease progression. We investigated the glioblastoma microenvironment with transgenic lineage-tracing models, intravital imaging, single-cell transcriptomics, immunofluorescence analysis as well as histopathology and characterized a previously unacknowledged population of tumor-associated cells with a myeloid-like expression profile (TAMEP) that transiently appeared during glioblastoma growth. TAMEP of mice and humans were identified with specific markers. Notably, TAMEP did not derive from microglia or peripheral monocytes but were generated by a fraction of CNS-resident, SOX2-positive progenitors. Abrogation of this progenitor cell population, by conditional Sox2-knockout, drastically reduced glioblastoma vascularization and size. Hence, TAMEP emerge as a tumor parenchymal component with a strong impact on glioblastoma progression

Alive in Christ

4th year class meeting, Concordia Seminary, St. Louis, May 23, 1985

4th Year Class Meeting

Alive in Chris

018. 10-1-81

Chapel Sermon by Immanuel Janssen on Thursday, October 1, 1981

087. 2-18-82

Chapel Sermon by Immanuel Janssen on Thursday, February 18, 1982

On the Challenges of Real World Data in Predictive Maintenance Scenarios: A Railway Application

Predictive maintenance is a challenging task, which aims at forecasting failure of a machine or one of its components. It allows companies to utilize just-in-time maintenance procedures instead of corrective or fixed-schedule ones. In order to achieve this goal, a complex and potentially error-prone process has to be completed successfully. Based on a real-world failure prediction example originated in the railway domain, we discuss a summary of the required processing steps in order to create a sound prediction process. Starting with the initial data acquisition and data fusion of three heterogeneous sources, the train diagnostic data, the workshop records and the failure report data, we identify and elaborate on the difficulties of finding a valid ground truth for the prediction of a compressor failure, caused by the integration of manually entered and potentially erroneous data. In further steps we point out the challenges of processing event-based diagnostic data to create useful features in order to train a classifier for the prediction task. Finally, we give an outlook on the tasks we yet have to accomplish and summarize the work we have done