@article{OezsoyluAliaziziWagneretal.2024,
  author    = {{\"O}zsoylu, Dua and Aliazizi, Fereshteh and Wagner, Patrick and Sch{\"o}ning, Michael Josef},
  title     = {Template bacteria-free fabrication of surface imprinted polymer-based biosensor for E. coli detection using photolithographic mimics: Hacking bacterial adhesion},
  series = {Biosensors and Bioelectronics},
  volume    = {261},
  journal   = {Biosensors and Bioelectronics},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1873-4235 (eISSN)},
  doi       = {10.1016/j.bios.2024.116491},
  pages     = {11 Seiten},
  year      = {2024},
  abstract  = {As one class of molecular imprinted polymers (MIPs), surface imprinted polymer (SIP)-based biosensors show great potential in direct whole-bacteria detection. Micro-contact imprinting, that involves stamping the template bacteria immobilized on a substrate into a pre-polymerized polymer matrix, is the most straightforward and prominent method to obtain SIP-based biosensors. However, the major drawbacks of the method arise from the requirement for fresh template bacteria and often non-reproducible bacteria distribution on the stamp substrate. Herein, we developed a positive master stamp containing photolithographic mimics of the template bacteria (E. coli) enabling reproducible fabrication of biomimetic SIP-based biosensors without the need for the "real" bacteria cells. By using atomic force and scanning electron microscopy imaging techniques, respectively, the E. coli-capturing ability of the SIP samples was tested, and compared with non-imprinted polymer (NIP)-based samples and control SIP samples, in which the cavity geometry does not match with E. coli cells. It was revealed that the presence of the biomimetic E. coli imprints with a specifically designed geometry increases the sensor E. coli-capturing ability by an "imprinting factor" of about 3. These findings show the importance of geometry-guided physical recognition in bacterial detection using SIP-based biosensors. In addition, this imprinting strategy was employed to interdigitated electrodes and QCM (quartz crystal microbalance) chips. E. coli detection performance of the sensors was demonstrated with electrochemical impedance spectroscopy (EIS) and QCM measurements with dissipation monitoring technique (QCM-D).},
  language  = {en}
}
@article{BornheimGriegerBlanecketal.2024,
  author    = {Bornheim, Tobias and Grieger, Niklas and Blaneck, Patrick Gustav and Bialonski, Stephan},
  title     = {Speaker Attribution in German Parliamentary Debates with QLoRA-adapted Large Language Models},
  series = {Journal for language technology and computational linguistics : JLCL},
  volume    = {37},
  journal   = {Journal for language technology and computational linguistics : JLCL},
  number    = {1},
  publisher = {Gesellschaft f{\"u}r Sprachtechnologie und Computerlinguistik},
  address   = {Regensburg},
  issn      = {2190-6858},
  doi       = {10.21248/jlcl.37.2024.244},
  pages     = {13 Seiten},
  year      = {2024},
  abstract  = {The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.},
  language  = {en}
}