@article{BohnertGeorgiadesMonoranuetal.2021, author = {Bohnert, Simone and Georgiades, Kosmas and Monoranu, Camelia-Maria and Bohnert, Michael and B{\"u}ttner, Andreas and Ondruschka, Benjamin}, title = {Quantitative evidence of suppressed TMEM119 microglial immunohistochemistry in fatal morphine intoxications}, series = {International Journal of Legal Medicine}, volume = {135}, journal = {International Journal of Legal Medicine}, number = {6}, issn = {1437-1596}, doi = {10.1007/s00414-021-02699-5}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-266934}, pages = {2315-2322}, year = {2021}, abstract = {The aim of this pilot study was to investigate the diagnostic potential of TMEM119 as a useful microglia-specific marker in combination with immunostainings for phagocytic function and infiltrating capacity of monocytes in cases of lethal monosubstance intoxications by morphine (MOR), methamphetamine (METH), and of ethanol-associated death (ETH) respectively. Human brain tissue samples were obtained from forensic autopsies of cases with single substance abuse (MOR, n = 8; ETH, n = 10; METH, n = 9) and then compared to a cohort of cardiovascular fatalities as controls (n = 9). Brain tissue samples of cortex, white matter, and hippocampus were collected and stained immunohistochemically with antibodies against TMEM119, CD68KiM1P, and CCR2. We could document the lowest density of TMEM119-positive cells in MOR deaths with highly significant differences to the control densities in all three regions investigated. In ETH and METH deaths, the expression of TMEM119 was comparable to cell densities in controls. The results indicate that the immunoreaction in brain tissue is different in these groups depending on the drug type used for abuse.}, language = {en} } @article{ReulChristHarteltetal.2019, author = {Reul, Christian and Christ, Dennis and Hartelt, Alexander and Balbach, Nico and Wehner, Maximilian and Springmann, Uwe and Wick, Christoph and Grundig, Christine and B{\"u}ttner, Andreas and Puppe, Frank}, title = {OCR4all—An open-source tool providing a (semi-)automatic OCR workflow for historical printings}, series = {Applied Sciences}, volume = {9}, journal = {Applied Sciences}, number = {22}, issn = {2076-3417}, doi = {10.3390/app9224853}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193103}, pages = {4853}, year = {2019}, abstract = {Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years, great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout analysis and segmentation, character recognition, and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper, we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. While a variety of materials can already be processed fully automatically, books with more complex layouts require manual intervention by the users. This is mostly due to the fact that the required ground truth for training stronger mixed models (for segmentation, as well as text recognition) is not available, yet, neither in the desired quantity nor quality. To deal with this issue in the short run, OCR4all offers a comfortable GUI that allows error corrections not only in the final output, but already in early stages to minimize error propagations. In the long run, this constant manual correction produces large quantities of valuable, high quality training material, which can be used to improve fully automatic approaches. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. During experiments, the fully automated application on 19th Century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. Furthermore, on very complex early printed books, even users with minimal or no experience were able to capture the text with manageable effort and great quality, achieving excellent Character Error Rates (CERs) below 0.5\%. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.}, language = {en} }