The Fisher-Kolmogorov equation is a diffusion-reaction PDE that is used to model the accumulation of prionic proteins, which are responsible for many different neurological disorders. Likely, the most important and studied misfolded protein in literature is the Amyloid-$\beta$, responsible for the onset of Alzheimer disease. Starting from medical images we construct a reduced-order model based on a graph brain connectome. The reaction coefficient of the proteins is modelled as a stochastic random field, taking into account all the many different underlying physical processes, which can hardly be measured. Its probability distribution is inferred by means of the Monte Carlo Markov Chain method applied to clinical data. The resulting model is patient-specific and can be employed for predicting the disease's future development. Forward uncertainty quantification techniques (Monte Carlo and sparse grid stochastic collocation) are applied with the aim of quantifying the impact of the variability of the reaction coefficient on the progression of protein accumulation within the next 20 years.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187358 | PMC |
Publication Analysis
Top Keywords
Similar Publications
Interlaboratory comparison of particle filtration efficiency testing equipment.
J Occup Environ Hyg
January 2025
Metrology Research Centre, National Research Council Canada, Ottawa, Ontario, Canada.
Particle filtration efficiency (PFE) is a critical property of face masks, with the most common test methods using sodium chloride as a challenge aerosol. In the absence of bottom-up uncertainty budgets for PFE, interlaboratory comparisons provide an alternative route to robustly quantify the precision and bias of the method. This work presents the results of several interlaboratory comparisons of particle filtration efficiency performed across a network of laboratories.
View Article and Find Full Text PDFPractical identifiability is a critical concern in data-driven modeling of mathematical systems. In this paper, we propose a novel framework for practical identifiability analysis to evaluate parameter identifiability in mathematical models of biological systems. Starting with a rigorous mathematical definition of practical identifiability, we demonstrate its equivalence to the invertibility of the Fisher Information Matrix.
View Article and Find Full Text PDFSemi-supervised medical image segmentation via weak-to-strong perturbation consistency and edge-aware contrastive representation.
Med Image Anal
January 2025
School of Computer Science and Technology, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China; National Key Laboratory of Smart Farm Technologies and Systems, Harbin, 150001, China. Electronic address:
Despite that supervised learning has demonstrated impressive accuracy in medical image segmentation, its reliance on large labeled datasets poses a challenge due to the effort and expertise required for data acquisition. Semi-supervised learning has emerged as a potential solution. However, it tends to yield satisfactory segmentation performance in the central region of the foreground, but struggles in the edge region.
View Article and Find Full Text PDFUncertainty-Aware Multimodal Trajectory Prediction via a Single Inference from a Single Model.
Sensors (Basel)
January 2025
Seamless Trans-X Lab (STL), School of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.
In the domain of autonomous driving, trajectory prediction plays a pivotal role in ensuring the safety and reliability of autonomous systems, especially when navigating complex environments. Unfortunately, trajectory prediction suffers from uncertainty problems due to the randomness inherent in the driving environment, but uncertainty quantification in trajectory prediction is not widely addressed, and most studies rely on deep ensembles methods. This study presents a novel uncertainty-aware multimodal trajectory prediction (UAMTP) model that quantifies aleatoric and epistemic uncertainties through a single forward inference.
View Article and Find Full Text PDFData-model interactive Rul prediction of stochastic degradation devices with multiple uncertainty quantification and multi-sensor information fusion.
ISA Trans
December 2024
College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China.
This paper proposes an improved remaining useful life (RUL) prediction method for stochastic degradation devices monitored by multi-source sensors under data-model interactive framework. Firstly, the interrelationships among sensors are established using k-nearest neighbor (KNN), and the composite health index (CHI) is constructed by aggregating the multi-source sensor information through the graph convolutional network (GCN). Secondly, a stochastic degradation model with triple uncertainty at any initial degradation level is established to improve the matching degree between the stochastic degradation model and the actual degradation process.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!