@article{KaiserAggensteinerHoltmannetal.2021,
  author    = {Kaiser, Anna and Aggensteiner, Pascal-M. and Holtmann, Martin and Fallgatter, Andreas and Romanos, Marcel and Abenova, Karina and Alm, Barbara and Becker, Katja and D{\"o}pfner, Manfred and Ethofer, Thomas and Freitag, Christine M. and Geissler, Julia and Hebebrand, Johannes and Huss, Michael and Jans, Thomas and Jendreizik, Lea Teresa and Ketter, Johanna and Legenbauer, Tanja and Philipsen, Alexandra and Poustka, Luise and Renner, Tobias and Retz, Wolfgang and R{\"o}sler, Michael and Thome, Johannes and Uebel-von Sandersleben, Henrik and von Wirth, Elena and Zinnow, Toivo and Hohmann, Sarah and Millenet, Sabina and Holz, Nathalie E. and Banaschewski, Tobias and Brandeis, Daniel},
  title     = {EEG data quality: determinants and impact in a multicenter study of children, adolescents, and adults with attention-deficit/hyperactivity disorder (ADHD)},
  series = {Brain Sciences},
  volume    = {11},
  journal   = {Brain Sciences},
  number    = {2},
  issn      = {2076-3425},
  doi       = {10.3390/brainsci11020214},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228788},
  year      = {2021},
  abstract  = {Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (n\(_{total}\) = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.},
  language  = {en}
}
@phdthesis{Letschert2019,
  author    = {Letschert, Sebastian},
  title     = {Quantitative Analysis of Membrane Components using Super-Resolution Microscopy},
  doi       = {10.25972/OPUS-16213},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162139},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {The plasma membrane is one of the most thoroughly studied and at the same time most complex, diverse, and least understood cellular structures. Its function is determined by the molecular composition as well as the spatial arrangement of its components. Even after decades of extensive membrane research and the proposal of dozens of models and theories, the structural organization of plasma membranes remains largely unknown. Modern imaging tools such as super-resolution fluorescence microscopy are one of the most efficient techniques in life sciences and are widely used to study the spatial arrangement and quantitative behavior of biomolecules in fixed and living cells. In this work, direct stochastic optical reconstruction microscopy (dSTORM) was used to investigate the structural distribution of mem-brane components with virtually molecular resolution. Key issues are different preparation and staining strategies for membrane imaging as well as localization-based quantitative analyses of membrane molecules. An essential precondition for the spatial and quantitative analysis of membrane components is the prevention of photoswitching artifacts in reconstructed localization microscopy images. Therefore, the impact of irradiation intensity, label density and photoswitching behavior on the distribution of plasma membrane and mitochondrial membrane proteins in dSTORM images was investigated. It is demonstrated that the combination of densely labeled plasma membranes and inappropriate photoswitching rates induces artificial membrane clusters. Moreover, inhomogeneous localization distributions induced by projections of three-dimensional membrane structures such as microvilli and vesicles are prone to generate artifacts in images of biological membranes. Alternative imaging techniques and ways to prevent artifacts in single-molecule localization microscopy are presented and extensively discussed. Another central topic addresses the spatial organization of glycosylated components covering the cell membrane. It is shown that a bioorthogonal chemical reporter system consisting of modified monosaccharide precursors and organic fluorophores can be used for specific labeling of membrane-associated glycoproteins and -lipids. The distribution of glycans was visualized by dSTORM showing a homogeneous molecule distribution on different mammalian cell lines without the presence of clusters. An absolute number of around five million glycans per cell was estimated and the results show that the combination of metabolic labeling, click chemistry, and single-molecule localization microscopy can be efficiently used to study cell surface glycoconjugates. In a third project, dSTORM was performed to investigate low-expressing receptors on cancer cells which can act as targets in personalized immunotherapy. Primary multiple myeloma cells derived from the bone marrow of several patients were analyzed for CD19 expression as potential target for chimeric antigen receptor (CAR)-modified T cells. Depending on the patient, 60-1,600 CD19 molecules per cell were quantified and functional in vitro tests demonstrate that the threshold for CD19 CAR T recognition is below 100 CD19 molecules per target cell. Results are compared with flow cytometry data, and the important roles of efficient labeling and appropriate control experiments are discussed.},
  subject      = {Fluoreszenzmikroskopie},
  language  = {en}
}