Mitochondria shed their outer membrane in response to infection-induced stress.

The outer mitochondrial membrane (OMM) is essential for cellular homeostasis. Yet little is known of the mechanisms that remodel it during natural stresses. We found that large "SPOTs" (structures positive for OMM) emerge during Toxoplasma gondii infection in mammalian cells. SPOTs mediated the depletion of the OMM proteins mitofusin 1 and 2, which restrict parasite growth. The formation of SPOTs depended on the parasite effector TgMAF1 and the host mitochondrial import receptor TOM70, which is required for optimal parasite proliferation. TOM70 enabled TgMAF1 to interact with the host OMM translocase SAM50. The ablation of SAM50 or the overexpression of an OMM-targeted protein promoted OMM remodeling independently of infection. Thus, Toxoplasma hijacks the formation of SPOTs, a cellular response to OMM stress, to promote its growth.

The Manhattan Frame Model-Manhattan World Inference in the Space of Surface Normals.

Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches utilize these regularities via the restrictive, and rather local, Manhattan World (MW) assumption which posits that every plane is perpendicular to one of the axes of a single coordinate system. The aforementioned regularities are especially evident in the surface normal distribution of a scene where they manifest as orthogonally-coupled clusters. This motivates the introduction of the Manhattan-Frame (MF) model which captures the notion of an MW in the surface normals space, the unit sphere, and two probabilistic MF models over this space. First, for a single MF we propose novel real-time MAP inference algorithms, evaluate their performance and their use in drift-free rotation estimation. Second, to capture the complexity of real-world scenes at a global scale, we extend the MF model to a probabilistic mixture of Manhattan Frames (MMF). For MMF inference we propose a simple MAP inference algorithm and an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that let us infer the unknown number of mixture components. We demonstrate the versatility of the MMF model and inference algorithm across several scales of man-made environments.