Improved cerebellar tissue classification on magnetic resonance images of brain.

PURPOSE: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation. MATERIALS AND METHODS: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices. RESULTS: The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology. CONCLUSION: The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object.

Symmetric inverse consistent nonlinear registration driven by mutual information.

A nonlinear viscoelastic image registration algorithm based on the demons paradigm and incorporating inverse consistent constraint (ICC) is implemented. An inverse consistent and symmetric cost function using mutual information (MI) as a similarity measure is employed. The cost function also includes regularization of transformation and inverse consistent error (ICE). The uncertainties in balancing various terms in the cost function are avoided by alternatively minimizing the similarity measure, the regularization of the transformation, and the ICE terms. The diffeomorphism of registration for preventing folding and/or tearing in the deformation is achieved by the composition scheme. The quality of image registration is first demonstrated by constructing brain atlas from 20 adult brains (age range 30-60). It is shown that with this registration technique: (1) the Jacobian determinant is positive for all voxels and (2) the average ICE is around 0.004 voxels with a maximum value below 0.1 voxels. Further, the deformation-based segmentation on Internet Brain Segmentation Repository, a publicly available dataset, has yielded high Dice similarity index (DSI) of 94.7% for the cerebellum and 74.7% for the hippocampus, attesting to the quality of our registration method.

Systemic levels of the anti-inflammatory decoy receptor soluble RAGE (receptor for advanced glycation end products) are decreased in dogs with inflammatory bowel disease.

Inflammatory bowel disease (IBD) is a common condition in dogs, and a dysregulated innate immunity is believed to play a major role in its pathogenesis. S100A12 is an endogenous damage-associated molecular pattern molecule, which is involved in phagocyte activation and is increased in serum/fecal samples from dogs with IBD. S100A12 binds to the receptor of advanced glycation end products (RAGE), a pattern-recognition receptor, and results of studies in human patients with IBD and other conditions suggest a role of RAGE in chronic inflammation. Soluble RAGE (sRAGE), a decoy receptor for inflammatory proteins (e.g., S100A12) that appears to function as an anti-inflammatory molecule, was shown to be decreased in human IBD patients. This study aimed to evaluate serum sRAGE and serum/fecal S100A12 concentrations in dogs with IBD. Serum and fecal samples were collected from 20 dogs with IBD before and after initiation of medical treatment and from 15 healthy control dogs. Serum sRAGE and serum and fecal S100A12 concentrations were measured by ELISA, and were compared between dogs with IBD and healthy controls, and between dogs with a positive outcome (i.e., clinical remission, n=13) and those that were euthanized (n=6). The relationship of serum sRAGE concentrations with clinical disease activity (using the CIBDAI scoring system), serum and fecal S100A12 concentrations, and histologic disease severity (using a 4-point semi-quantitative grading system) was tested. Serum sRAGE concentrations were significantly lower in dogs with IBD than in healthy controls (p=0.0003), but were not correlated with the severity of histologic lesions (p=0.4241), the CIBDAI score before (p=0.0967) or after treatment (p=0.1067), the serum S100A12 concentration before (p=0.9214) and after treatment (p=0.4411), or with the individual outcome (p=0.4066). Clinical remission and the change in serum sRAGE concentration after treatment were not significantly associated (p=0.5727); however, serum sRAGE concentrations increased only in IBD dogs with complete clinical remission. Also, dogs that were euthanized had significantly higher fecal S100A12 concentrations than dogs that were alive at the end of the study (p=0.0124). This study showed that serum sRAGE concentrations are decreased in dogs diagnosed with IBD compared to healthy dogs, suggesting that sRAGE/RAGE may be involved in the pathogenesis of canine IBD. Lack of correlation between sRAGE and S100A12 concentrations is consistent with sRAGE functioning as a non-specific decoy receptor. Further studies need to evaluate the gastrointestinal mucosal expression of RAGE in healthy and diseased dogs, and also the formation of S100A12-RAGE complexes.

Novel antiviral properties of azithromycin in cystic fibrosis airway epithelial cells.

Virus-associated pulmonary exacerbations, often associated with rhinoviruses (RVs), contribute to cystic fibrosis (CF) morbidity. Currently, there are only a few therapeutic options to treat virus-induced CF pulmonary exacerbations. The macrolide antibiotic azithromycin has antiviral properties in human bronchial epithelial cells. We investigated the potential of azithromycin to induce antiviral mechanisms in CF bronchial epithelial cells. Primary bronchial epithelial cells from CF and control children were infected with RV after azithromycin pre-treatment. Viral RNA, interferon (IFN), IFN-stimulated gene and pattern recognition receptor expression were measured by real-time quantitative PCR. Live virus shedding was assessed by assaying the 50% tissue culture infective dose. Pro-inflammatory cytokine and IFN-β production were evaluated by ELISA. Cell death was investigated by flow cytometry. RV replication was increased in CF compared with control cells. Azithromycin reduced RV replication seven-fold in CF cells without inducing cell death. Furthermore, azithromycin increased RV-induced pattern recognition receptor, IFN and IFN-stimulated gene mRNA levels. While stimulating antiviral responses, azithromycin did not prevent virus-induced pro-inflammatory responses. Azithromycin pre-treatment reduces RV replication in CF bronchial epithelial cells, possibly through the amplification of the antiviral response mediated by the IFN pathway. Clinical studies are needed to elucidate the potential of azithromycin in the management and prevention of RV-induced CF pulmonary exacerbations.

Prolonged activity of the pestiviral RNase Erns as an interferon antagonist after uptake by clathrin-mediated endocytosis

The RNase activity of the envelope glycoprotein E(rns) of the pestivirus bovine viral diarrhea virus (BVDV) is required to block type I interferon (IFN) synthesis induced by single-stranded RNA (ssRNA) and double-stranded RNA (dsRNA) in bovine cells. Due to the presence of an unusual membrane anchor at its C terminus, a significant portion of E(rns) is also secreted. In addition, a binding site for cell surface glycosaminoglycans is located within the C-terminal region of E(rns). Here, we show that the activity of soluble E(rns) as an IFN antagonist is not restricted to bovine cells. Extracellularly applied E(rns) protein bound to cell surface glycosaminoglycans and was internalized into the cells within 1 h of incubation by an energy-dependent mechanism that could be blocked by inhibitors of clathrin-dependent endocytosis. E(rns) mutants that lacked the C-terminal membrane anchor retained RNase activity but lost most of their intracellular activity as an IFN antagonist. Surprisingly, once taken up into the cells, E(rns) remained active and blocked dsRNA-induced IFN synthesis for several days. Thus, we propose that E(rns) acts as an enzymatically active decoy receptor that degrades extracellularly added viral RNA mainly in endolysosomal compartments that might otherwise activate intracellular pattern recognition receptors (PRRs) in order to maintain a state of innate immunotolerance. IMPORTANCE The pestiviral RNase E(rns) was previously shown to inhibit viral ssRNA- and dsRNA-induced interferon (IFN) synthesis. However, the localization of E(rns) at or inside the cells, its species specificity, and its mechanism of interaction with cell membranes in order to block the host's innate immune response are still largely unknown. Here, we provide strong evidence that the pestiviral RNase E(rns) is taken up within minutes by clathrin-mediated endocytosis and that this uptake is mostly dependent on the glycosaminoglycan binding site located within the C-terminal end of the protein. Remarkably, the inhibitory activity of E(rns) remains for several days, indicating the very potent and prolonged effect of a viral IFN antagonist. This novel mechanism of an enzymatically active decoy receptor that degrades a major viral pathogen-associated molecular pattern (PAMP) might be required to efficiently maintain innate and, thus, also adaptive immunotolerance, and it might well be relevant beyond the bovine species.

Total Variation Blind Deconvolution: The Devil Is in the Details

In this paper we study the problem of blind deconvolution. Our analysis is based on the algorithm of Chan and Wong [2] which popularized the use of sparse gradient priors via total variation. We use this algorithm because many methods in the literature are essentially adaptations of this framework. Such algorithm is an iterative alternating energy minimization where at each step either the sharp image or the blur function are reconstructed. Recent work of Levin et al. [14] showed that any algorithm that tries to minimize that same energy would fail, as the desired solution has a higher energy than the no-blur solution, where the sharp image is the blurry input and the blur is a Dirac delta. However, experimentally one can observe that Chan and Wong's algorithm converges to the desired solution even when initialized with the no-blur one. We provide both analysis and experiments to resolve this paradoxical conundrum. We find that both claims are right. The key to understanding how this is possible lies in the details of Chan and Wong's implementation and in how seemingly harmless choices result in dramatic effects. Our analysis reveals that the delayed scaling (normalization) in the iterative step of the blur kernel is fundamental to the convergence of the algorithm. This then results in a procedure that eludes the no-blur solution, despite it being a global minimum of the original energy. We introduce an adaptation of this algorithm and show that, in spite of its extreme simplicity, it is very robust and achieves a performance comparable to the state of the art.

Optimized aperture shapes for depth estimation

The finite depth of field of a real camera can be used to estimate the depth structure of a scene. The distance of an object from the plane in focus determines the defocus blur size. The shape of the blur depends on the shape of the aperture. The blur shape can be designed by masking the main lens aperture. In fact, aperture shapes different from the standard circular aperture give improved accuracy of depth estimation from defocus blur. We introduce an intuitive criterion to design aperture patterns for depth from defocus. The criterion is independent of a specific depth estimation algorithm. We formulate our design criterion by imposing constraints directly in the data domain and optimize the amount of depth information carried by blurred images. Our criterion is a quadratic function of the aperture transmission values. As such, it can be numerically evaluated to estimate optimized aperture patterns quickly. The proposed mask optimization procedure is applicable to different depth estimation scenarios. We use it for depth estimation from two images with different focus settings, for depth estimation from two images with different aperture shapes as well as for depth estimation from a single coded aperture image. In this work we show masks obtained with this new evaluation criterion and test their depth discrimination capability using a state-of-the-art depth estimation algorithm.

Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset.

Interferon response of the cystic fibrosis bronchial epithelium to major and minor group rhinovirus infection

Rhinoviruses (RVs) are associated with exacerbations of cystic fibrosis (CF), asthma and COPD. There is growing evidence suggesting the involvement of the interferon (IFN) pathway in RV-associated morbidity in asthma and COPD. The mechanisms of RV-triggered exacerbations in CF are poorly understood. In a pilot study, we assessed the antiviral response of CF and healthy bronchial epithelial cells (BECs) to RV infection, we measured the levels of IFNs, pattern recognition receptors (PRRs) and IFN-stimulated genes (ISGs) upon infection with major and minor group RVs and poly(IC) stimulation. Major group RV infection of CF BECs resulted in a trend towards a diminished IFN response at the level of IFNs, PRRs and ISGs in comparison to healthy BECs. Contrary to major group RV, the IFN pathway induction upon minor group RV infection was significantly increased at the level of IFNs and PRRs in CF BECs compared to healthy BECs.

Temporally Coherent Disparity Maps Using CRFs with Fast 4D Filtering

State of the art methods for disparity estimation achieve good results for single stereo frames, but temporal coherence in stereo videos is often neglected. In this paper we present a method to compute temporally coherent disparity maps. We define an energy over whole stereo sequences and optimize their Conditional Random Field (CRF) distributions using mean-field approximation. We introduce novel terms for smoothness and consistency between the left and right views, and perform CRF optimization by fast, iterative spatio-temporal filtering with linear complexity in the total number of pixels. Our results rank among the state of the art while having significantly less flickering artifacts in stereo sequences.

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance.