@article{SchichorAlbrechtKorteetal.2012,
  author    = {Schichor, Christian and Albrecht, Valerie and Korte, Benjamin and Buchner, Alexander and Riesenberg, Rainer and Mysliwietz, Josef and Paron, Igor and Motaln, Helena and Turnsek, Tamara Lah and Juerchott, Kathrin and Selbig, Joachim and Tonn, J{\"o}rg-Christian},
  title     = {Mesenchymal stem cells and glioma cells form a structural as well as a functional syncytium in vitro},
  series = {Experimental neurology},
  volume    = {234},
  journal   = {Experimental neurology},
  number    = {1},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {0014-4886},
  doi       = {10.1016/j.expneurol.2011.12.033},
  pages     = {208 -- 219},
  year      = {2012},
  abstract  = {The interaction of human mesenchymal stem cells (hMSCs) and tumor cells has been investigated in various contexts. HMSCs are considered as cellular treatment vectors based on their capacity to migrate towards a malignant lesion. However, concerns about unpredictable behavior of transplanted hMSCs are accumulating. In malignant gliomas, the recruitment mechanism is driven by glioma-secreted factors which lead to accumulation of both, tissue specific stem cells as well as bone marrow derived hMSCs within the tumor. The aim of the present work was to study specific cellular interactions between hMSCs and glioma cells in vitro. We show, that glioma cells as well as hMSCs differentially express connexins. and that they interact via gap-junctional coupling. Besides this so-called functional syncytium formation, we also provide evidence of cell fusion events (structural syncytium). These complex cellular interactions led to an enhanced migration and altered proliferation of both, tumor and mesenchymal stem cell types in vitro. The presented work shows that glioma cells display signs of functional as well as structural syncytium formation with hMSCs in vitro. The described cellular phenomena provide new insight into the complexity of interaction patterns between tumor cells and host cells. Based on these findings, further studies are warranted to define the impact of a functional or structural syncytium formation on malignant tumors and cell based therapies in vivo.},
  language  = {en}
}
@book{AlbrechtNaumann2012,
  author    = {Albrecht, Alexander and Naumann, Felix},
  title     = {Understanding cryptic schemata in large extract-transform-load systems},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-201-8},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-61257},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {19},
  year      = {2012},
  abstract  = {Extract-Transform-Load (ETL) tools are used for the creation, maintenance, and evolution of data warehouses, data marts, and operational data stores. ETL workflows populate those systems with data from various data sources by specifying and executing a DAG of transformations. Over time, hundreds of individual workflows evolve as new sources and new requirements are integrated into the system. The maintenance and evolution of large-scale ETL systems requires much time and manual effort. A key problem is to understand the meaning of unfamiliar attribute labels in source and target databases and ETL transformations. Hard-to-understand attribute labels lead to frustration and time spent to develop and understand ETL workflows. We present a schema decryption technique to support ETL developers in understanding cryptic schemata of sources, targets, and ETL transformations. For a given ETL system, our recommender-like approach leverages the large number of mapped attribute labels in existing ETL workflows to produce good and meaningful decryptions. In this way we are able to decrypt attribute labels consisting of a number of unfamiliar few-letter abbreviations, such as UNP_PEN_INT, which we can decrypt to UNPAID_PENALTY_INTEREST. We evaluate our schema decryption approach on three real-world repositories of ETL workflows and show that our approach is able to suggest high-quality decryptions for cryptic attribute labels in a given schema.},
  language  = {en}
}
@phdthesis{Albrecht2013,
  author    = {Albrecht, Alexander},
  title     = {Understanding and managing extract-transform-load systems},
  pages     = {107},
  year      = {2013},
  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}
}
@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     = {Correction to: 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/bbab246},
  pages     = {1},
  year      = {2021},
  language  = {en}
}