TY - CHAP A1 - Radivoievych, Aleksandar A1 - Kolp, Benjamin A1 - Grebinyk, Sergii A1 - Prylutska, Svitlana A1 - Ritter, Uwe A1 - Zolk, Oliver A1 - Glökler, Jörn A1 - Frohme, Marcus A1 - Grebinyk, Anna T1 - Prestine C60 fullerene as a novel agent in sonodynamic treatment of cancer cells T2 - FEBS Open Bio Y1 - 2022 U6 - https://doi.org/10.1002/2211-5463.13440 SN - 2211-5463 VL - 12 IS - Supplement 1 SP - 74 EP - 74 PB - Wiley CY - Hoboken, NJ ER - TY - CHAP A1 - Lindsay, Richard J. A1 - Stelzl, Lukas S. A1 - Pietrek, Lisa A1 - Hummer, Gerhard A1 - Wigge, Philip Anthony A1 - Hanson, Sonya M. T1 - Helical region near poly-Q tract in prion-like domain of Arabidopsis ELF3 plays role in temperature-sensing mechanism T2 - Biophysical journal Y1 - 2022 U6 - https://doi.org/10.1016/j.bpj.2021.11.964 SN - 0006-3495 SN - 1542-0086 VL - 121 IS - 3 SP - 355A EP - 356A PB - Cell Press CY - Cambridge, Mass. ER - TY - CHAP A1 - Hartmann, Anika M. A1 - Kandil, Farid I. A1 - Steckhan, Nico A1 - Häupl, Thomas A1 - Kessler, Christian S. A1 - Michalsen, Andreas A1 - Koppold-Liebscher, Daniela A. T1 - Rheumatoid arthritis benefits from fasting and plant-based diet: an exploratory randomized controlled trial (NUTRIFAST) T2 - Annals of the rheumatic diseases Y1 - 2022 U6 - https://doi.org/10.1136/annrheumdis-2022-eular.452 SN - 0003-4967 SN - 1468-2060 VL - 81 SP - 558 EP - 559 PB - BMJ Publishing Group CY - London ER - TY - CHAP A1 - Masanneck, Lars A1 - Räuber, S. A1 - Gieseler, Pauline A1 - Ruck, T. A1 - Stern, Ariel Dora A1 - Meuth, S. G. A1 - Pawlitzki, M. T1 - Geography and a changing technology landscape: analysing coverage of German multiple sclerosis care networks and digital health technology adoption in multiple sclerosis trials T2 - Multiple sclerosis journal Y1 - 2022 U6 - https://doi.org/10.1177/13524585221123687 SN - 1352-4585 SN - 1477-0970 VL - 28 IS - Supplement 3 SP - 492 EP - 493 PB - Sage CY - London ER - TY - CHAP A1 - Gießmann, Nico A1 - Bender, Benedict ED - Kö, Andrea ED - Kotsis, Gabriele ED - Tjoa, A. Min ED - Khalil, Ismail T1 - Towards a unified framework for evaluating user satisfaction with mobile government apps BT - insights from user reviews T2 - Electronic Government and the Information Systems Perspective : 13th International Conference, EGOVIS 2024, Naples, Italy, August 26–28, 2024, Proceedings N2 - This study aims to bring together scattered research findings on user satisfaction with mobile government apps into a unified framework. The researchers analyzed 70 high-quality papers from leading journals and conferences and systematically integrated different frameworks and case studies to reflect the importance of the field over time while also highlighting methodological and geographical research gaps. The study achieved a significant methodological advance by developing codebooks for empirical analysis utilizing the App Store. This approach validated the framework’s dimensions on 8,524 reviews, demonstrating the framework’s applicability to platform-based apps and identifying critical areas for future research. Combining academic insights with practical findings, this research provides comprehensive guidance for developing and evaluating user-centered mobile government apps, facilitating improved service delivery and alignment with user expectations. Y1 - 2024 SN - 978-3-031-68210-0 SN - 978-3-031-68211-7 U6 - https://doi.org/10.1007/978-3-031-68211-7_10 SP - 122 EP - 129 PB - Springer CY - Cham ER - TY - CHAP A1 - Hiort, Pauline A1 - Hugo, Julian A1 - Zeinert, Justus A1 - Müller, Nataniel A1 - Kashyap, Spoorthi A1 - Rajapakse, Jagath C. A1 - Azuaje, Francisco A1 - Renard, Bernhard Y. A1 - Baum, Katharina T1 - DrDimont: explainable drug response prediction from differential analysis of multi-omics networks T2 - Bioinformatics N2 - Motivation: While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. Results: We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response. Y1 - 2022 U6 - https://doi.org/10.1093/bioinformatics/btac477 SN - 1367-4803 SN - 1367-4811 VL - 38 SP - ii113 EP - ii119 PB - Oxford Univ. Press CY - Oxford ER - TY - CHAP A1 - Grum, Marcus ED - Shishkov, Boris T1 - Managing multi-site artificial neural networks’ activation rates and activation cycles T2 - Business modeling and software design : 14th International Symposium, BMSD 2024, Luxembourg City, Luxembourg, July 1–3, 2024, proceedings N2 - Traditionally, business models and software designs used to model the usage of artificial intelligence (AI) at a very specific point in the process or rather fix implemented application. Since applications can be based on AI, such as networked artificial neural networks (ANN) on top of which applications are installed, these on-top applications can be instructed directly from their underlying ANN compartments [1]. However, with the integration of several AI-based systems, their coordination is a highly relevant target factor for the operation and improvement of networked processes, such as they can be found in cross-organizational production contexts spanning multiple distributed locations. This work aims to extend prior research on managing artificial knowledge transfers among interlinked AIs as coordination instrument by examining effects of different activation types (respective activation rates and cycles) on by ANN-instructed production machines. In a design-science-oriented way, this paper conceptualizes rhythmic state descriptions for dynamic systems and associated 14 experiment designs. Two experiments have been realized, analyzed and evaluated thereafter in regard with their activities and processes induced. Findings show that the simulator [2] used and experiments designed and realized, here, (I) enable research on ANN activation types, (II) illustrate ANN-based production networks disrupted by activation types and clarify the need for harmonizing them. Further, (III) management interventions are derived for harmonizing interlinked ANNs. This study establishes the importance of site-specific coordination mechanisms and novel forms of management interventions as drivers of efficient artificial knowledge transfer. Y1 - 2024 SN - 978-3-031-64072-8 SN - 978-3-031-64073-5 U6 - https://doi.org/10.1007/978-3-031-64073-5_17 SP - 258 EP - 269 PB - Springer CY - Cham ER - TY - CHAP A1 - Grum, Marcus ED - Shishkov, Boris T1 - Researching multi-site artificial neural networks’ activation rates and activation cycles T2 - Business modeling and software design : 14th International Symposium, BMSD 2024, Luxembourg City, Luxembourg, July 1–3, 2024, proceedings N2 - With the further development of more and more production machines into cyber-physical systems, and their greater integration with artificial intelligence (AI) techniques, the coordination of intelligent systems is a highly relevant target factor for the operation and improvement of networked processes, such as they can be found in cross-organizational production contexts spanning multiple distributed locations. This work aims to extend prior research on managing their artificial knowledge transfers as coordination instrument by examining effects of different activation types (respective activation rates and cycles) on by Artificial Neural Network (ANN)-instructed production machines. For this, it provides a new integration type of ANN-based cyber-physical production system as a tool to research artificial knowledge transfers: In a design-science-oriented way, a prototype of a simulation system is constructed as Open Source information system which will be used in on-building research to (I) enable research on ANN activation types in production networks, (II) illustrate ANN-based production networks disrupted by activation types and clarify the need for harmonizing them, and (III) demonstrate conceptual management interventions. This simulator shall establish the importance of site-specific coordination mechanisms and novel forms of management interventions as drivers of efficient artificial knowledge transfer. Y1 - 2024 SN - 978-3-031-64072-8 SN - 978-3-031-64073-5 U6 - https://doi.org/10.1007/978-3-031-64073-5_12 SP - 186 EP - 206 PB - Springer CY - Cham ER - TY - CHAP A1 - Fayyaz, Susann A1 - Hartmann, Bolette A1 - Hanack, Katja A1 - Michelchen, Sophia A1 - Kreiling, Reinhard T1 - Development of a hematopoietic stem cell (murine system) based system as an alternative for the in vivo T-cell-dependent antibody response (TDAR) assay within the EOGRTS: case-study with Parabens T2 - Toxicology letters Y1 - 2022 U6 - https://doi.org/10.1016/j.toxlet.2022.07.483 SN - 0378-4274 SN - 1879-3169 VL - 368 SP - S175 EP - S176 PB - Elsevier Science CY - Amsterdam [u.a.] ER - TY - CHAP A1 - Gleiß, Alexander T1 - The patient will see you now-towards an understanding of on-demand healthcare T2 - 2020 IEEE 22nd Conference on Business Informatics (CBI) N2 - The increasing prevalence and ubiquity of digital technologies is changing the needs and expectations of patients towards healthcare services. As a result, a plethora of patient-centered services edges into the healthcare market. Since digital technologies bear the potential to surmount barriers in time and space, patients increasingly demand real-time or near-time healthcare services. Amongst a cloud of related concepts in the context of digital health, one term increasingly typifies this impulse: on-demand healthcare. While this term can be noticeably found in practice, there is hardly some theoretical foundation so far. Against this background, the aim of this paper is to address this research gap and to explore the phenomenon of on-demand healthcare. Based on a design-science approach including a literature review and analysis of in-depth interviews and empirical cases, the outcome of this paper is twofold: (1) a conceptual framework and (2) a proposal for a definition of on-demand healthcare. KW - on-demand healthcare KW - digital health KW - mHealth KW - smart health KW - connected health KW - healthcare technologies Y1 - 2020 SN - 978-1-7281-9926-9 SN - 978-1-7281-9927-6 U6 - https://doi.org/10.1109/CBI49978.2020.00024 SP - 154 EP - 161 PB - IEEE CY - Piscataway ER -