@article{KahmannRauschPluemeretal.2022,
  author    = {Kahmann, Stephanie L. and Rausch, Valentin and Pl{\"u}mer, Jonathan and M{\"u}ller, Lars P. and Pieper, Martin and Wegmann, Kilian},
  title     = {The automized fracture edge detection and generation of three-dimensional fracture probability heat maps},
  series = {Medical Engineering \& Physics},
  volume    = {2022},
  journal   = {Medical Engineering \& Physics},
  number    = {110},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1350-4533},
  pages     = {7 Seiten},
  year      = {2022},
  abstract  = {With proven impact of statistical fracture analysis on fracture classifications, it is desirable to minimize the manual work and to maximize repeatability of this approach. We address this with an algorithm that reduces the manual effort to segmentation, fragment identification and reduction. The fracture edge detection and heat map generation are performed automatically. With the same input, the algorithm always delivers the same output. The tool transforms one intact template consecutively onto each fractured specimen by linear least square optimization, detects the fragment edges in the template and then superimposes them to generate a fracture probability heat map. We hypothesized that the algorithm runs faster than the manual evaluation and with low (< 5 mm) deviation. We tested the hypothesis in 10 fractured proximal humeri and found that it performs with good accuracy (2.5 mm ± 2.4 mm averaged Euclidean distance) and speed (23 times faster). When applied to a distal humerus, a tibia plateau, and a scaphoid fracture, the run times were low (1-2 min), and the detected edges correct by visual judgement. In the geometrically complex acetabulum, at a run time of 78 min some outliers were considered acceptable. An automatically generated fracture probability heat map based on 50 proximal humerus fractures matches the areas of high risk of fracture reported in medical literature. Such automation of the fracture analysis method is advantageous and could be extended to reduce the manual effort even further.},
  language  = {en}
}
@article{SchneiderSchwabedalBialonski2022,
  author    = {Schneider, Jules and Schwabedal, Justus T. C. and Bialonski, Stephan},
  title     = {Schlafspindeln - Funktion, Detektion und Nutzung als Biomarker f{\"u}r die psychiatrische Diagnostik},
  series = {Der Nervenarzt},
  journal   = {Der Nervenarzt},
  publisher = {Springer},
  address   = {Berlin, Heidelberg},
  issn      = {1433-0407},
  doi       = {10.1007/s00115-022-01340-z},
  pages     = {1 -- 8},
  year      = {2022},
  abstract  = {Hintergrund: Die Schlafspindel ist ein Graphoelement des Elektroenzephalogramms (EEG), das im Leicht- und Tiefschlaf beobachtet werden kann. Ver{\"a}nderungen der Spindelaktivit{\"a}t wurden f{\"u}r verschiedene psychiatrische Erkrankungen beschrieben. Schlafspindeln zeigen aufgrund ihrer relativ konstanten Eigenschaften Potenzial als Biomarker in der psychiatrischen Diagnostik. Methode: Dieser Beitrag liefert einen {\"U}berblick {\"u}ber den Stand der Wissenschaft zu Eigenschaften und Funktionen der Schlafspindeln sowie {\"u}ber beschriebene Ver{\"a}nderungen der Spindelaktivit{\"a}t bei psychiatrischen Erkrankungen. Verschiedene methodische Ans{\"a}tze und Ausblicke zur Spindeldetektion werden hinsichtlich deren Anwendungspotenzial in der psychiatrischen Diagnostik erl{\"a}utert. Ergebnisse und Schlussfolgerung: W{\"a}hrend Ver{\"a}nderungen der Spindelaktivit{\"a}t bei psychiatrischen Erkrankungen beschrieben wurden, ist deren exaktes Potenzial f{\"u}r die psychiatrische Diagnostik noch nicht ausreichend erforscht. Diesbez{\"u}glicher Erkenntnisgewinn wird in der Forschung gegenw{\"a}rtig durch ressourcenintensive und fehleranf{\"a}llige Methoden zur manuellen oder automatisierten Spindeldetektion ausgebremst. Neuere Detektionsans{\"a}tze, die auf Deep-Learning-Verfahren basieren, k{\"o}nnten die Schwierigkeiten bisheriger Detektionsmethoden {\"u}berwinden und damit neue M{\"o}glichkeiten f{\"u}r die praktisch},
  language  = {de}
}
@article{LindnerBurgerRutledgeetal.2022,
  author    = {Lindner, Simon and Burger, Ren{\´e} and Rutledge, Douglas N. and Do, Xuan Tung and Rumpf, Jessica and Diehl, Bernd W. K. and Schulze, Margit and Monakhova, Yulia},
  title     = {Is the calibration transfer of multivariate calibration models between high- and low-field NMR instruments possible? A case study of lignin molecular weight},
  series = {Analytical chemistry},
  volume    = {94},
  journal   = {Analytical chemistry},
  number    = {9},
  publisher = {ACS Publications},
  address   = {Washington, DC},
  isbn      = {1520-6882},
  doi       = {10.1021/acs.analchem.1c05125},
  pages     = {3997 -- 4004},
  year      = {2022},
  abstract  = {Although several successful applications of benchtop nuclear magnetic resonance (NMR) spectroscopy in quantitative mixture analysis exist, the possibility of calibration transfer remains mostly unexplored, especially between high- and low-field NMR. This study investigates for the first time the calibration transfer of partial least squares regressions [weight average molecular weight (Mw) of lignin] between high-field (600 MHz) NMR and benchtop NMR devices (43 and 60 MHz). For the transfer, piecewise direct standardization, calibration transfer based on canonical correlation analysis, and transfer via the extreme learning machine auto-encoder method are employed. Despite the immense resolution difference between high-field and low-field NMR instruments, the results demonstrate that the calibration transfer from high- to low-field is feasible in the case of a physical property, namely, the molecular weight, achieving validation errors close to the original calibration (down to only 1.2 times higher root mean square errors). These results introduce new perspectives for applications of benchtop NMR, in which existing calibrations from expensive high-field instruments can be transferred to cheaper benchtop instruments to economize.},
  language  = {en}
}
@article{VahidpourAlghazaliAkcaetal.2022,
  author    = {Vahidpour, Farnoosh and Alghazali, Yousef H. M. and Akca, Sevilay and Hommes, Gregor and Sch{\"o}ning, Michael Josef},
  title     = {An Enzyme-Based Interdigitated Electrode-Type Biosensor for Detecting Low Concentrations of H₂O₂ Vapor/Aerosol},
  series = {Chemosensors},
  volume    = {10},
  journal   = {Chemosensors},
  number    = {6},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2227-9040},
  doi       = {10.3390/chemosensors10060202},
  pages     = {Arikel 202},
  year      = {2022},
  abstract  = {This work introduces a novel method for the detection of H₂O₂ vapor/aerosol of low concentrations, which is mainly applied in the sterilization of equipment in medical industry. Interdigitated electrode (IDE) structures have been fabricated by means of microfabrication techniques. A differential setup of IDEs was prepared, containing an active sensor element (active IDE) and a passive sensor element (passive IDE), where the former was immobilized with an enzymatic membrane of horseradish peroxidase that is selective towards H₂O₂. Changes in the IDEs' capacitance values (active sensor element versus passive sensor element) under H₂O₂ vapor/aerosol atmosphere proved the detection in the concentration range up to 630 ppm with a fast response time (<60 s). The influence of relative humidity was also tested with regard to the sensor signal, showing no cross-sensitivity. The repeatability assessment of the IDE biosensors confirmed their stable capacitive signal in eight subsequent cycles of exposure to H₂O₂ vapor/aerosol. Room-temperature detection of H₂O₂ vapor/aerosol with such miniaturized biosensors will allow a future three-dimensional, flexible mapping of aseptic chambers and help to evaluate sterilization assurance in medical industry.},
  language  = {en}
}