@article{BertzSchoeningMolinnusetal.2024,
  author    = {Bertz, Morten and Sch{\"o}ning, Michael Josef and Molinnus, Denise and Homma, Takayuki},
  title     = {Influence of temperature, light, and H₂O₂ concentration on microbial spore inactivation: in-situ Raman spectroscopy combined with optical trapping},
  series = {Physica status solidi (a) applications and materials science},
  journal   = {Physica status solidi (a) applications and materials science},
  number    = {Early View},
  publisher = {Wiley-VCH},
  address   = {Berlin},
  issn      = {1862-6319 (Online)},
  doi       = {10.1002/pssa.202300866},
  pages     = {8 Seiten},
  year      = {2024},
  abstract  = {To gain insight on chemical sterilization processes, the influence of temperature (up to 70 °C), intense green light, and hydrogen peroxide (H₂O₂) concentration (up to 30\% in aqueous solution) on microbial spore inactivation is evaluated by in-situ Raman spectroscopy with an optical trap. Bacillus atrophaeus is utilized as a model organism. Individual spores are isolated and their chemical makeup is monitored under dynamically changing conditions (temperature, light, and H₂O₂ concentration) to mimic industrially relevant process parameters for sterilization in the field of aseptic food processing. While isolated spores in water are highly stable, even at elevated temperatures of 70 °C, exposure to H₂O₂ leads to a loss of spore integrity characterized by the release of the key spore biomarker dipicolinic acid (DPA) in a concentration-dependent manner, which indicates damage to the inner membrane of the spore. Intensive light or heat, both of which accelerate the decomposition of H₂O₂ into reactive oxygen species (ROS), drastically shorten the spore lifetime, suggesting the formation of ROS as a rate-limiting step during sterilization. It is concluded that Raman spectroscopy can deliver mechanistic insight into the mode of action of H₂O₂-based sterilization and reveal the individual contributions of different sterilization methods acting in tandem.},
  language  = {en}
}
@article{EngelmannSimsekShalabyetal.2024,
  author    = {Engelmann, Ulrich M. and Simsek, Beril and Shalaby, Ahmed and Krause, Hans-Joachim},
  title     = {Key contributors to signal generation in frequency mixing magnetic detection (FMMD): an in silico study},
  series = {Sensors},
  volume    = {24},
  journal   = {Sensors},
  number    = {6},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s24061945},
  pages     = {Artikel 1945},
  year      = {2024},
  abstract  = {Frequency mixing magnetic detection (FMMD) is a sensitive and selective technique to detect magnetic nanoparticles (MNPs) serving as probes for binding biological targets. Its principle relies on the nonlinear magnetic relaxation dynamics of a particle ensemble interacting with a dual frequency external magnetic field. In order to increase its sensitivity, lower its limit of detection and overall improve its applicability in biosensing, matching combinations of external field parameters and internal particle properties are being sought to advance FMMD. In this study, we systematically probe the aforementioned interaction with coupled N{\´e}el-Brownian dynamic relaxation simulations to examine how key MNP properties as well as applied field parameters affect the frequency mixing signal generation. It is found that the core size of MNPs dominates their nonlinear magnetic response, with the strongest contributions from the largest particles. The drive field amplitude dominates the shape of the field-dependent response, whereas effective anisotropy and hydrodynamic size of the particles only weakly influence the signal generation in FMMD. For tailoring the MNP properties and parameters of the setup towards optimal FMMD signal generation, our findings suggest choosing large particles of core sizes dc > 25 nm nm with narrow size distributions (σ < 0.1) to minimize the required drive field amplitude. This allows potential improvements of FMMD as a stand-alone application, as well as advances in magnetic particle imaging, hyperthermia and magnetic immunoassays.},
  language  = {en}
}
@inproceedings{SimsekKrauseEngelmann2024,
  author    = {Simsek, Beril and Krause, Hans-Joachim and Engelmann, Ulrich M.},
  title     = {Magnetic biosensing with magnetic nanoparticles: Simulative approach to predict signal intensity in frequency mixing magnetic detection},
  series = {YRA MedTech Symposium (2024)},
  booktitle = {YRA MedTech Symposium (2024)},
  editor    = {Digel, Ilya and Staat, Manfred and Trzewik, J{\"u}rgen and Sielemann, Stefanie and Erni, Daniel and Zylka, Waldemar},
  publisher = {Universit{\"a}t Duisburg-Essen},
  address   = {Duisburg},
  organization    = {MedTech Symposium},
  isbn      = {978-3-940402-65-3},
  doi       = {10.17185/duepublico/81475},
  pages     = {27 -- 28},
  year      = {2024},
  abstract  = {Magnetic nanoparticles (MNP) are investigated with great interest for biomedical applications in diagnostics (e.g. imaging: magnetic particle imaging (MPI)), therapeutics (e.g. hyperthermia: magnetic fluid hyperthermia (MFH)) and multi-purpose biosensing (e.g. magnetic immunoassays (MIA)). What all of these applications have in common is that they are based on the unique magnetic relaxation mechanisms of MNP in an alternating magnetic field (AMF). While MFH and MPI are currently the most prominent examples of biomedical applications, here we present results on the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a simulation perspective. In general, we ask how the key parameters of MNP (core size and magnetic anisotropy) affect the FMMD signal: by varying the core size, we investigate the effect of the magnetic volume per MNP; and by changing the effective magnetic anisotropy, we study the MNPs' flexibility to leave its preferred magnetization direction. From this, we predict the most effective combination of MNP core size and magnetic anisotropy for maximum signal generation.},
  language  = {en}
}
@article{SchoenrockMuckeltHastermannetal.2024,
  author    = {Schoenrock, Britt and Muckelt, Paul E. and Hastermann, Maria and Albracht, Kirsten and MacGregor, Robert and Martin, David and Gunga, Hans-Christian and Salanova, Michele and Stokes, Maria J. and Warner, Martin B. and Blottner, Dieter},
  title     = {Muscle stiffness indicating mission crew health in space},
  series = {Scientific Reports},
  volume    = {14},
  journal   = {Scientific Reports},
  number    = {Article number: 4196},
  publisher = {Springer Nature},
  address   = {London},
  issn      = {2045-2322},
  doi       = {10.1038/s41598-024-54759-6},
  pages     = {13 Seiten},
  year      = {2024},
  abstract  = {Muscle function is compromised by gravitational unloading in space affecting overall musculoskeletal health. Astronauts perform daily exercise programmes to mitigate these effects but knowing which muscles to target would optimise effectiveness. Accurate inflight assessment to inform exercise programmes is critical due to lack of technologies suitable for spaceflight. Changes in mechanical properties indicate muscle health status and can be measured rapidly and non-invasively using novel technology. A hand-held MyotonPRO device enabled monitoring of muscle health for the first time in spaceflight (> 180 days). Greater/maintained stiffness indicated countermeasures were effective. Tissue stiffness was preserved in the majority of muscles (neck, shoulder, back, thigh) but Tibialis Anterior (foot lever muscle) stiffness decreased inflight vs. preflight (p < 0.0001; mean difference 149 N/m) in all 12 crewmembers. The calf muscles showed opposing effects, Gastrocnemius increasing in stiffness Soleus decreasing. Selective stiffness decrements indicate lack of preservation despite daily inflight countermeasures. This calls for more targeted exercises for lower leg muscles with vital roles as ankle joint stabilizers and in gait. Muscle stiffness is a digital biomarker for risk monitoring during future planetary explorations (Moon, Mars), for healthcare management in challenging environments or clinical disorders in people on Earth, to enable effective tailored exercise programmes.},
  language  = {en}
}
@article{PieronekKleefeld2024,
  author    = {Pieronek, Lukas and Kleefeld, Andreas},
  title     = {On trajectories of complex-valued interior transmission eigenvalues},
  series = {Inverse problems and imaging : IPI},
  volume    = {18},
  journal   = {Inverse problems and imaging : IPI},
  number    = {2},
  publisher = {AIMS},
  address   = {Springfield, Mo},
  issn      = {1930-8337 (Print)},
  doi       = {10.3934/ipi.2023041},
  pages     = {480 -- 516},
  year      = {2024},
  abstract  = {This paper investigates the interior transmission problem for homogeneous media via eigenvalue trajectories parameterized by the magnitude of the refractive index. In the case that the scatterer is the unit disk, we prove that there is a one-to-one correspondence between complex-valued interior transmission eigenvalue trajectories and Dirichlet eigenvalues of the Laplacian which turn out to be exactly the trajectorial limit points as the refractive index tends to infinity. For general simply-connected scatterers in two or three dimensions, a corresponding relation is still open, but further theoretical results and numerical studies indicate a similar connection.},
  language  = {en}
}
@unpublished{GriegerMehrkanoonBialonski2024,
  author    = {Grieger, Niklas and Mehrkanoon, Siamak and Bialonski, Stephan},
  title     = {Preprint: Data-efficient sleep staging with synthetic time series pretraining},
  series = {arXiv},
  journal   = {arXiv},
  pages     = {10 Seiten},
  year      = {2024},
  abstract  = {Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces.},
  language  = {en}
}
@book{ElsaesserKlebinggatKuhnetal.2024,
  author    = {Elsaesser, Evelyn and Klebinggat, Michael and Kuhn, Wilfried and Michielsens, Constant and Pauels, Willibert and Popkes, Enno E. and Schneider, Elke and Laack, Walter van and Warven, Rinus van},
  title     = {Schnittstelle Tod - Ist die Menschheit zu retten ohne Vertrauen auf ein Danach},
  editor    = {Laack, Walter van},
  publisher = {van Laack Buchverlag},
  address   = {Aachen},
  isbn      = {978-3-936624-58-8},
  pages     = {156 Seiten},
  year      = {2024},
  language  = {de}
}
@techreport{BarnatArntzBerneckeretal.2024,
  type      = {Working Paper},
  author    = {Barnat, Miriam and Arntz, Kristian and Bernecker, Andreas and Fissabre, Anke and Franken, Norbert and Goldbach, Daniel and H{\"u}ning, Felix and J{\"o}rissen, J{\"o}rg and Kirsch, Ansgar and Pettrak, J{\"u}rgen and Rexforth, Matthias and Josef, Rosenkranz and Terstegge, Andreas},
  title     = {Strategische Gestaltung von Studieng{\"a}ngen f{\"u}r die Zukunft: Ein kollaborativ entwickeltes Self-Assessment},
  series = {Hochschulforum Digitalisierung - Diskussionspapier},
  journal   = {Hochschulforum Digitalisierung - Diskussionspapier},
  publisher = {Stifterverband f{\"u}r die Deutsche Wissenschaft},
  address   = {Berlin},
  issn      = {2365-7081},
  pages     = {16 Seiten},
  year      = {2024},
  abstract  = {Das Diskussionspapier beschreibt einen Prozess an der FH Aachen zur Entwicklung und Implementierung eines Self-Assessment-Tools f{\"u}r Studieng{\"a}nge. Dieser Prozess zielte darauf ab, die Relevanz der Themen Digitalisierung, Internationalisierung und Nachhaltigkeit in Studieng{\"a}ngen zu st{\"a}rken. Durch Workshops und kollaborative Entwicklung mit Studiendekan:innen entstand ein Fragebogen, der zur Reflexion und strategischen Weiterentwicklung der Studieng{\"a}nge dient.},
  language  = {de}
}
@book{StaatDigelTrzewiketal.2024,
  author    = {Staat, Manfred and Digel, Ilya and Trzewik, J{\"u}rgen and Sielemann, Stefanie and Erni, Daniel and Zylka, Waldemar},
  title     = {Symposium Proceedings; 4th YRA MedTech Symposium 2024 : February 1 / 2024 / FH Aachen},
  publisher = {Universit{\"a}t Duisburg-Essen},
  address   = {Duisburg},
  organization    = {MedTech Symposium},
  isbn      = {978-3-940402-65-3},
  doi       = {10.17185/duepublico/81475},
  pages     = {40 Seiten},
  year      = {2024},
  language  = {en}
}