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Polymer based composites are widely used for treatment, for example as biofilm resistant seals of root canal fillings. Such clinical use, however, fails more frequently than other dental composite restorations, due to stress-related misfits. The reason for this is that the biomaterials used are inserted as viscous masses that may bond to the substrate, yet shrinkage stresses arising during setting of the cross-linking polymer, work against durable adhesion. Here we combine phase contrast enhanced time-lapse radiography (radioscopy), digital image correlation (DIC) and submicrometer resolution phase-contrast enhanced microtomography (PCE-CT), to reveal the spatial and temporal dynamics of composite polymerization and strain evolution. Radioscopy of cavities located in the upper part of human root canals demonstrates how the composite post-gelation "densification" is dominated by viscous flow with quantifiable motion of both particles and entrapped voids. Thereafter, these composites enter a "stress-relaxation" stage and exhibit several structural adaptations, induced by residual shrinkage stresses. Consequently critical alterations to the final biomaterial geometry emerge: (i) entrapped bubbles expand; (ii) microscopic root filling pull-out occurs; (iii) the cavity walls deform inwards, and (iv) occasionally delamination ensues, propagating out from the root canal filling along buried restoration-substrate interfaces. Our findings shed new light on the interactions between confined spaces and biomedical composites that cross-link in situ , highlighting the crucial role of geometry in channeling residual stresses. They further provide new insights into the emergence of structural flaws, calling attention to the need to find new treatment options. This work quantifies recurring spatial and temporal material redistribution in composites used clinically to fill internal spaces in teeth. This knowledge is important for both promoting biomaterial resistance against potentially pathologic biofilms and for improving structural capacity to endure years of mechanical function. Our study demonstrates the significant role of geometry and the need for improved control over stress raisers to develop better treatment protocols and new space filling materials. The use of high-brilliance X-rays for time-lapse imaging at submicrometer resolution provides dynamic information about the damaging effects of stress relaxation due to polymerization shrinkage. [Display omitted] [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a unified framework to perform classification and low-level modeling jointly. • Robustness is improved by considering a possibly badly labeled training set. • The proposed model allows a very rich interpretation of the modeled data structure. • Performance is assessed on synthetic and real data in the specific context of hyperspectral image interpretation. • The proposed model is generic enough to incorporate any kind of low-level modeling. Abstract Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via feature extraction. These preprocessing steps have motivated numerous research works aiming at recovering latent variables in an unsupervised context. This paper proposes a unified framework to perform classification and low-level modeling jointly. The main objective is to use the estimated latent variables as features for classification and to incorporate simultaneously supervised information to help latent variable extraction. The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second stage clustering these features into statistically homogeneous groups and a last classification stage exploiting the (possibly badly) labeled data. Performance of the model is assessed in the specific context of hyperspectral image interpretation, unifying two standard analysis techniques, namely unmixing and classification. [ABSTRACT FROM AUTHOR]