Many patients with PTMC had clinical and histopathological poor prognostic factors. The expression of molecular markers, such as EGFR and COX-2, may have a role in the prognosis of PTMC. When considering all of the prognostic factors, a more tailored management approach appears to be necessary for patients with PTMC.

Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.

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Applying motion‐capture data to multi‐person interaction between virtual characters is challenging because one needs to preserve the interaction semantics while also satisfying the general requirements of motion retargeting, such as preventing penetration and preserving naturalness. An efficient means of representing interaction semantics is by defining the spatial relationships between the body parts of characters. However, existing methods consider only the character skeleton and thus are not suitable for capturing skin‐level spatial relationships. This paper proposes a novel method for retargeting interaction motions with respect to character skins. Specifically, we introduce the aura mesh, which is a volumetric mesh that surrounds a character's skin. The spatial relationships between two characters are computed from the overlap of the skin mesh of one character and the aura mesh of the other, and then the interaction motion retargeting is achieved by preserving the spatial relationships as much as possible while satisfying other constraints. We show the effectiveness of our method through a number of experiments.

Serum BMP-4 levels are inversely associated with surrogate markers of arterial stiffness and carotid atherosclerosis in patients with Type 2 diabetes.

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