Micropathological Chip Modeling the Neurovascular Unit Response to Inflammatory Bone Condition.

Organ-on-a-chip in vitro platforms accurately mimic complex microenvironments offering the ability to recapitulate and dissect mechanisms of physiological and pathological settings, revealing their major importance to develop new therapeutic targets. Bone diseases, such as osteoarthritis, are extremely complex, comprising of the action of inflammatory mediators leading to unbalanced bone homeostasis and de-regulation of sensory innervation and angiogenesis. Although there are models to mimic bone vascularization or innervation, in vitro platforms merging the complexity of bone, vasculature, innervation, and inflammation are missing. Therefore, in this study a microfluidic-based neuro-vascularized bone chip (NVB chip) is proposed to 1) model the mechanistic interactions between innervation and angiogenesis in the inflammatory bone niche, and 2) explore, as a screening tool, novel strategies targeting inflammatory diseases, using a nano-based drug delivery system. It is possible to set the design of the platform and achieve the optimized conditions to address the neurovascular network under inflammation. Moreover, this system is validated by delivering anti-inflammatory drug-loaded nanoparticles to counteract the neuronal growth associated with pain perception. This reliable in vitro tool will allow understanding the bone neurovascular system, enlightening novel mechanisms behind the inflammatory bone diseases, bone destruction, and pain opening new avenues for new therapies discovery.

A Framework for Fast Image Deconvolution With Incomplete Observations.

In image deconvolution problems, the diagonalization of the underlying operators by means of the fast Fourier transform (FFT) usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather slow methods or, otherwise, use inexact convolution models, resulting in the occurrence of artifacts in the enhanced images. In this paper, we propose a new deconvolution framework for images with incomplete observations that allows us to work with diagonalized convolution operators, and therefore is very fast. We iteratively alternate the estimation of the unknown pixels and of the deconvolved image, using, e.g., an FFT-based deconvolution method. This framework is an efficient, high-quality alternative to existing methods of dealing with the image boundaries, such as edge tapering. It can be used with any fast deconvolution method. We give an example in which a state-of-the-art method that assumes periodic boundary conditions is extended, using this framework, to unknown boundary conditions. Furthermore, we propose a specific implementation of this framework, based on the alternating direction method of multipliers (ADMM). We provide a proof of convergence for the resulting algorithm, which can be seen as a "partial" ADMM, in which not all variables are dualized. We report experimental comparisons with other primal-dual methods, where the proposed one performed at the level of the state of the art. Four different kinds of applications were tested in the experiments: deconvolution, deconvolution with inpainting, superresolution, and demosaicing, all with unknown boundaries.

Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data.

Remote sensing hyperspectral images (HSIs) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low-dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods mainly decreases because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSIs are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low-dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough, such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution through local dictionary learning using endmember induction algorithms. We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data.