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.

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.

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.

Acoustic records of the underwater soundscape at PALAOA with links to audio stream files, 2005-2011

Scientific background: Marine mammals use sound for communication, navigation and prey detection. Acoustic sensors therefore allow the detection of marine mammals, even during polar winter months, when restricted visibility prohibits visual sightings. The animals are surrounded by a permanent natural soundscape, which, in polar waters, is mainly dominated by the movement of ice. In addition to the detection of marine mammals, acoustic long-term recordings provide information on intensity and temporal variability of characteristic natural and anthropogenic background sounds, as well as their influence on the vocalization of marine mammals Scientific objectives: The PerenniAL Acoustic Observatory in the Antarctic Ocean (PALAOA, Hawaiian "whale") near Neumayer Station is intended to record the underwater soundscape in the vicinity of the shelf ice edge over the duration of several years. These long-term recordings will allow studying the acoustic repertoire of whales and seals continuously in an environment almost undisturbed by humans. The data will be analyzed to (1) register species specific vocalizations, (2) infer the approximate number of animals inside the measuring range, (3) calculate their movements relative to the observatory, and (4) examine possible effects of the sporadic shipping traffic on the acoustic and locomotive behaviour of marine mammals. The data, which are largely free of anthropogenic noise, provide also a base to set up passive acoustic mitigation systems used on research vessels. Noise-free bioacoustic data thereby represent the foundation for the development of automatic pattern recognition procedures in the presence of interfering sounds, e.g. propeller noise.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.

Real Time FPGA Implementation of an Modulation Classifier for Electronic Warfare Applications

This paper presents an automatic modulation classifier for electronic warfare applications. It is a pattern recognition modulation classifier based on statistical features of the phase and instantaneous frequency. This classifier runs in a real time operation mode with sampling rates in excess of 1 Gsample/s. The hardware platform for this application is a Field Programmable Gate Array (FPGA). This AMC is subsidiary of a digital channelised receiver also implemented in the same platform.

Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images.

Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

Microcalcifications Detection using PFCM and ANN

This work presents a method to detect Microcalcifications in Regions of Interest from digitized mammograms. The method is based mainly on the combination of Image Processing, Pattern Recognition and Artificial Intelligence. The Top-Hat transform is a technique based on mathematical morphology operations that, in this work is used to perform contrast enhancement of microcalcifications in the region of interest. In order to find more or less homogeneous regions in the image, we apply a novel image sub-segmentation technique based on Possibilistic Fuzzy c-Means clustering algorithm. From the original region of interest we extract two window-based features, Mean and Deviation Standard, which will be used in a classifier based on a Artificial Neural Network in order to identify microcalcifications. Our results show that the proposed method is a good alternative in the stage of microcalcifications detection, because this stage is an important part of the early Breast Cancer detection

Secure access control by means of human stress detection

This paper proposes a stress detection system based on fuzzy logic and the physiological signals heart rate and galvanic skin response. The main contribution of this method relies on the creation of a stress template, collecting the behaviour of previous signals under situations with a different level of stress in each individual. The creation of this template provides an accuracy of 99.5% in stress detection, improving the results obtained by current pattern recognition techniques like GMM, k-NN, SVM or Fisher Linear Discriminant. In addition, this system can be embedded in security systems to detect critical situations in accesses as cross-border control. Furthermore, its applications can be extended to other fields as vehicle driver state-of-mind management, medicine or sport training.