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Artikel: Bayesian Physics-Based Modeling of Tau Propagation in Alzheimer's Disease.

Schäfer, Amelie / Peirlinck, Mathias / Linka, Kevin / Kuhl, Ellen

2021 Band 12, Seite(n) 702975

Abstract: Amyloid-β and hyperphosphorylated tau protein are known drivers of neuropathology in Alzheimer's disease. Tau in particular spreads in the brains of patients following a spatiotemporal pattern that is highly sterotypical and correlated with subsequent ... ...

Abstract	Amyloid-β and hyperphosphorylated tau protein are known drivers of neuropathology in Alzheimer's disease. Tau in particular spreads in the brains of patients following a spatiotemporal pattern that is highly sterotypical and correlated with subsequent neurodegeneration. Novel medical imaging techniques can now visualize the distribution of tau in the brain
Sprache	Englisch
Erscheinungsdatum	2021-07-16
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2564217-0
ISSN	1664-042X
ISSN	1664-042X
DOI	10.3389/fphys.2021.702975
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Network Diffusion Modeling Explains Longitudinal Tau PET Data.

Schäfer, Amelie / Mormino, Elizabeth C / Kuhl, Ellen

Frontiers in neuroscience

2020 Band 14, Seite(n) 566876

Abstract: Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive ... ...

Abstract	Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understanding of tau propagation relies almost exclusively on postmortem histopathology, and the precise propagation dynamics of misfolded tau in the living brain remain poorly understood. Here we combine longitudinal positron emission tomography and dynamic network modeling to test the hypothesis that misfolded tau propagates preferably along neuronal connections. We follow 46 subjects for three or four annual positron emission tomography scans and compare their pathological tau profiles against brain network models of intracellular and extracellular spreading. For each subject, we identify a personalized set of model parameters that characterizes the individual progression of pathological tau. Across all subjects, the mean protein production rate was 0.21 ± 0.15 and the intracellular diffusion coefficient was 0.34 ± 0.43. Our network diffusion model can serve as a tool to detect non-clinical symptoms at an earlier stage and make informed predictions about the timeline of neurodegeneration on an individual personalized basis.
Sprache	Englisch
Erscheinungsdatum	2020-12-23
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2020.566876
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Spatially-extended nucleation-aggregation-fragmentation models for the dynamics of prion-like neurodegenerative protein-spreading in the brain and its connectome.

Fornari, Sveva / Schäfer, Amelie / Kuhl, Ellen / Goriely, Alain

Journal of theoretical biology

2019 Band 486, Seite(n) 110102

Abstract: The prion-like hypothesis of neurodegenerative diseases states that the accumulation of misfolded proteins in the form of aggregates is responsible for tissue death and its associated neurodegenerative pathology and cognitive decline. Some disease- ... ...

Abstract	The prion-like hypothesis of neurodegenerative diseases states that the accumulation of misfolded proteins in the form of aggregates is responsible for tissue death and its associated neurodegenerative pathology and cognitive decline. Some disease-specific misfolded proteins can interact with healthy proteins to form long chains that are transported through the brain along axonal pathways. Since aggregates of different sizes have different transport properties and toxicity, it is important to follow independently their evolution in space and time. Here, we model the spreading and propagation of aggregates of misfolded proteins in the brain using the general Smoluchowski theory of nucleation, aggregation, and fragmentation. The transport processes considered here are either anisotropic diffusion along axonal bundles or discrete Laplacian transport along a network. In particular, we model the spreading and aggregation of both amyloid-β and τ molecules in the brain connectome. We show that these two models lead to different size distributions and different propagation along the network. A detailed analysis of these two models also reveals the existence of four different stages with different dynamics and invasive properties.
Mesh-Begriff(e)	Amyloid beta-Peptides ; Brain/metabolism ; Connectome ; Humans ; Neurodegenerative Diseases ; Prions/metabolism ; tau Proteins
Chemische Substanzen	Amyloid beta-Peptides ; Prions ; tau Proteins
Sprache	Englisch
Erscheinungsdatum	2019-12-03
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2972-5
ISSN	1095-8541 ; 0022-5193
ISSN (online)	1095-8541
ISSN	0022-5193
DOI	10.1016/j.jtbi.2019.110102
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study.

Linka, Kevin / Peirlinck, Mathias / Schäfer, Amelie / Tikenogullari, Oguz Ziya / Goriely, Alain / Kuhl, Ellen

Archives of computational methods in engineering : state of the art reviews

2021 Band 28, Heft 6, Seite(n) 4225–4236

Abstract: The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had ... ...

Abstract	The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.
Sprache	Englisch
Erscheinungsdatum	2021-08-23
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article
ZDB-ID	2276736-8
ISSN	1886-1784 ; 1134-3060
ISSN (online)	1886-1784
ISSN	1134-3060
DOI	10.1007/s11831-021-09638-y
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Buch ; Online: Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems

Linka, Kevin / Schafer, Amelie / Meng, Xuhui / Zou, Zongren / Karniadakis, George Em / Kuhl, Ellen

2022

Abstract: Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision ...

Abstract	Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data, physics, and uncertainties by combining neural networks, physics-informed modeling, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system, the outbreak dynamics of COVID-19. Our Physics-Informed Neural Networks can seamlessly integrate data and physics, robustly solve forward and inverse problems, and perform well for both interpolation and extrapolation, even for a small amount of noisy and incomplete data. At only minor additional cost, they can self-adaptively learn the weighting between data and physics. Combined with Bayesian Neural Networks, they can serve as priors in a Bayesian Inference, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these approaches for the simple model problem of a seasonal endemic infectious disease, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and, more broadly, to a wide variety of nonlinear dynamical systems.
Schlagwörter	Computer Science - Machine Learning ; Mathematics - Dynamical Systems ; Nonlinear Sciences - Chaotic Dynamics ; 62Mxx ; 70Kxx ; G.3 ; J.3
Thema/Rubrik (Code)	006
Erscheinungsdatum	2022-05-12
Erscheinungsland	us
Dokumenttyp	Buch ; Online
Datenquelle	BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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Artikel: Irrt die Deutsche Rentenversicherung?

Schäfer, Amelie / Falter, Kira

Der Betrieb : mit Recht Innovation sichern Vol. 68, No. 36 , p. 2091

Indizien für das Vorliegen von freier Mitarbeit bleiben weiterhin nur Indizien

2015 Band 68, Heft 36, Seite(n) 2091

Verfasserangabe	Amelie Schäfer; Kira Falter
Sprache	Deutsch
Verlag	Handelsblatt Fachmedien
Erscheinungsort	Düsseldorf
Dokumenttyp	Artikel
ZDB-ID	879-5
ISSN	0005-9935
Datenquelle	ECONomics Information System

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Artikel ; Online: Prion-like spreading of Alzheimer's disease within the brain's connectome.

Fornari, Sveva / Schäfer, Amelie / Jucker, Mathias / Goriely, Alain / Kuhl, Ellen

Journal of the Royal Society, Interface

2019 Band 16, Heft 159, Seite(n) 20190356

Abstract: The prion hypothesis states that misfolded proteins can act as infectious agents that template the misfolding and aggregation of healthy proteins to transmit a disease. Increasing evidence suggests that pathological proteins in neurodegenerative diseases ...

Abstract	The prion hypothesis states that misfolded proteins can act as infectious agents that template the misfolding and aggregation of healthy proteins to transmit a disease. Increasing evidence suggests that pathological proteins in neurodegenerative diseases adopt prion-like mechanisms and spread across the brain along anatomically connected networks. Local kinetic models of protein misfolding and global network models of protein spreading provide valuable insight into several aspects of prion-like diseases. Yet, to date, these models have not been combined to simulate how pathological proteins multiply and spread across the human brain. Here, we create an efficient and robust tool to simulate the spreading of misfolded protein using three classes of kinetic models, the Fisher-Kolmogorov model, the Heterodimer model and the Smoluchowski model. We discretize their governing equations using a human brain network model, which we represent as a weighted Laplacian graph generated from 418 brains from the Human Connectome Project. Its nodes represent the anatomic regions of interest and its edges are weighted by the mean fibre number divided by the mean fibre length between any two regions. We demonstrate that our brain network model can predict the histopathological patterns of Alzheimer's disease and capture the key characteristic features of finite-element brain models at a fraction of their computational cost: simulating the spatio-temporal evolution of aggregate size distributions across the human brain throughout a period of 40 years takes less than 7 s on a standard laptop computer. Our model has the potential to predict biomarker curves, aggregate size distributions, infection times, and the effects of therapeutic strategies including reduced production and increased clearance of misfolded protein.
Mesh-Begriff(e)	Alzheimer Disease/diagnostic imaging ; Alzheimer Disease/metabolism ; Brain/diagnostic imaging ; Connectome ; Female ; Humans ; Male ; Models, Neurological ; Nerve Net/diagnostic imaging ; Nerve Net/metabolism ; Prions/metabolism
Chemische Substanzen	Prions
Sprache	Englisch
Erscheinungsdatum	2019-10-16
Erscheinungsland	England
Dokumenttyp	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2156283-0
ISSN	1742-5662 ; 1742-5689
ISSN (online)	1742-5662
ISSN	1742-5689
DOI	10.1098/rsif.2019.0356
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Rechtliche Rahmenbedingungen von Crowdworking

Meyer-Michaelis, Isabel / Falter, Kira / Schäfer, Amelie

Der Betrieb Vol. 69, No. 43 , p. 2543-2546

Chancen und Risiken dieser Möglichkeit von Fremdpersonaleinsatz

2016 Band 69, Heft 43, Seite(n) 2543–2546

Verfasserangabe	RAin Dr. Isabel Meyer-Michaelis/RAin Kira Falter/RAin Amelie Schäfer
Sprache	Deutsch
Verlag	Handelsblatt Fachmedien
Erscheinungsort	Düsseldorf
Dokumenttyp	Artikel
ZDB-ID	879-5
ISSN	0005-9935
Datenquelle	ECONomics Information System

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Artikel ; Online: Effects of B.1.1.7 and B.1.351 on COVID-19 dynamics. A campus reopening study

Linka, Kevin / Peirlinck, Mathias / Schafer, Amelie / Tikenogullari, Oguz Ziya / Goriely, Alain / Kuhl, Ellen

medRxiv

Abstract	The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.
Schlagwörter	covid19
Sprache	Englisch
Erscheinungsdatum	2021-04-27
Verlag	Cold Spring Harbor Laboratory Press
Dokumenttyp	Artikel ; Online
DOI	10.1101/2021.04.22.21255954
Datenquelle	COVID19

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Artikel ; Online: Towards Patient-Specific Computational Modelling of Articular Cartilage on the Basis of Advanced Multiparametric MRI Techniques.

Linka, Kevin / Schäfer, Amelie / Hillgärtner, Markus / Itskov, Mikhail / Knobe, Matthias / Kuhl, Christiane / Hitpass, Lea / Truhn, Daniel / Thuering, Johannes / Nebelung, Sven

Scientific reports

2019 Band 9, Heft 1, Seite(n) 7172

Abstract: Cartilage degeneration is associated with tissue softening and represents the hallmark change of osteoarthritis. Advanced quantitative Magnetic Resonance Imaging (qMRI) techniques allow the assessment of subtle tissue changes not only of structure and ... ...

Abstract	Cartilage degeneration is associated with tissue softening and represents the hallmark change of osteoarthritis. Advanced quantitative Magnetic Resonance Imaging (qMRI) techniques allow the assessment of subtle tissue changes not only of structure and morphology but also of composition. Yet, the relation between qMRI parameters on the one hand and microstructure, composition and the resulting functional tissue properties on the other hand remain to be defined. To this end, a Finite-Element framework was developed based on an anisotropic constitutive model of cartilage informed by sample-specific multiparametric qMRI maps, obtained for eight osteochondral samples on a clinical 3.0 T MRI scanner. For reference, the same samples were subjected to confined compression tests to evaluate stiffness and compressibility. Moreover, the Mankin score as an indicator of histological tissue degeneration was determined. The constitutive model was optimized against the resulting stress responses and informed solely by the sample-specific qMRI parameter maps. Thereby, the biomechanical properties of individual samples could be captured with good-to-excellent accuracy (mean R
Mesh-Begriff(e)	Adult ; Aged ; Algorithms ; Cartilage Diseases/pathology ; Cartilage, Articular/diagnostic imaging ; Cartilage, Articular/pathology ; Cartilage, Articular/physiology ; Compressive Strength ; Female ; Humans ; Image Processing, Computer-Assisted ; Male ; Middle Aged ; Models, Biological ; Multiparametric Magnetic Resonance Imaging ; Prospective Studies
Sprache	Englisch
Erscheinungsdatum	2019-05-09
Erscheinungsland	England
Dokumenttyp	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-019-43389-y
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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