2024-01-05 16:59 |
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2023-09-04 11:41 |
[DZNE-2023-00855]
Preprint
Pollak, C. ; Kügler, D. ; Reuter, M.
Estimating Head Motion from MR-Images
Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed. In order to estimate subtle head motion, that remains undetected by experts, we introduce a deep learning method to predict in-scanner head motion directly from T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) images using motion estimates from an in-scanner depth camera as ground truth. [...]
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2023-09-01 11:42 |
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2023-07-19 16:03 |
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2023-07-03 10:47 |
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2023-03-21 16:47 |
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2023-02-24 14:54 |
[DZNE-2023-00264]
Preprint
Hoffmann, S. ; Orlando, M. ; Andrzejak, E. ; et al
Light induced synaptic vesicle autophagy
The regulated turnover of synaptic vesicle (SV) proteins is thought to involve the ubiquitin dependent tagging and degradation through endo-lysosomal and autophagy pathways. Yet, it remains unclear which of these pathways are used, when they become activated and whether SVs are cleared en-mass together with SV proteins or whether both are degraded selectively. [...]
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2023-01-11 12:19 |
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2022-07-15 15:36 |
[DZNE-2022-01323]
Preprint
Fritze, T. ; Reinke, C. ; van den Berg, G. J. ; et al
World War II cohorts and diabetes mellitus, coronary heart disease and cerebrovascular disease later in life: An observational cohort study based on German claims data
This study applied a data-driven approach to explore whether being born during or around World War II affects the risk of morbidity later in life.Methods Incident diagnoses were explored for diabetes (ICD-10 code E10-E14; 75,487 persons/12,905 incident cases), cerebrovascular disease (CeVD; I6; 79,829/11,664), and coronary heart disease (CHD; I20-I25; 89,657/11,116) for birth cohorts 1935-1950, using German health-claims data from 2004-2015. The data include quarterly information of the inpatient and outpatient treatment. [...]
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2022-07-14 14:34 |
[DZNE-2022-01321]
Preprint
Doblhammer-Reiter, G. ; Reinke, C. ; Kreft, D.
Social disparities in the first wave of COVID-19 infections in Germany: A county-scale explainable machine learning approach
Little is known about factors correlated with this geographic spread of the first wave of COVID-19 infections in Germany. Given the lack of individual-level socioeconomic information on COVID-19 cases, we resorted to an ecological study design, exploring regional correlates of COVID-19 diagnoses.Data and Method We used data from the Robert-Koch-Institute on COVID-19 diagnoses by sex, age (age groups: 0-4, 5-14, 15-34, 35-59, 60-79, 80+), county (NUTS3 region) differentiating five periods (initial phase: through 15 March; 1st lockdown period: 16 March to 31 March; 2nd lockdown period: from 1 April to 15 April; easing period: 16 April to 30 April; post-lockdown period: 1 May through 23 July). [...]
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