Computational identification and functional validation of regulatory motifs in cartilage-expressed genes.

Chondrocyte gene regulation is important for the generation and maintenance of cartilage tissues. Several regulatory factors have been identified that play a role in chondrogenesis, including the positive transacting factors of the SOX family such as SOX9, SOX5, and SOX6, as well as negative transacting factors such as C/EBP and delta EF1. However, a complete understanding of the intricate regulatory network that governs the tissue-specific expression of cartilage genes is not yet available. We have taken a computational approach to identify cis-regulatory, transcription factor (TF) binding motifs in a set of cartilage characteristic genes to better define the transcriptional regulatory networks that regulate chondrogenesis. Our computational methods have identified several TFs, whose binding profiles are available in the TRANSFAC database, as important to chondrogenesis. In addition, a cartilage-specific SOX-binding profile was constructed and used to identify both known, and novel, functional paired SOX-binding motifs in chondrocyte genes. Using DNA pattern-recognition algorithms, we have also identified cis-regulatory elements for unknown TFs. We have validated our computational predictions through mutational analyses in cell transfection experiments. One novel regulatory motif, N1, found at high frequency in the COL2A1 promoter, was found to bind to chondrocyte nuclear proteins. Mutational analyses suggest that this motif binds a repressive factor that regulates basal levels of the COL2A1 promoter.

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.

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.

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

Unconstrained and Contactless Hand Geometry Biometrics

This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely Support Vector Machines (SVM) and k-Nearest Neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices

Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech

We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech.

Mejora de la calidad de los electrocardiogramas recogidos por terminales móviles

Inicio del desarrollo de un algoritmo eficiente orientado a dispositivos con baja capacidad de proceso, que ayude a personas sin necesariamente una preparación adecuada a llevar a cabo un proceso de toma de una señal biológica, como puede ser un electrocardiograma. La aplicación deberá, por tanto, asesorar en la toma de la señal al usuario, evaluar la calidad de la grabación obtenida, y en tiempo seudo real, comprobar si la calidad de la señal obtenida es suficientemente buena para su posterior diagnóstico, de tal modo que en caso de que sea necesaria una repetición de la prueba médica, esta pueda realizarse de inmediato. Además, el algoritmo debe extraer las características más relevantes de la señal electrocardiográfica, procesarlas, y obtener una serie de patrones significativos que permitan la orientación a la diagnosis de algunas de las patologías más comunes que se puedan extraer de la información de las señales cardíacas. Para la extracción, evaluación y toma de decisiones de este proceso previo a la generación del diagnóstico, se seguirá la arquitectura clásica de un sistema de detección de patrones, definiendo las clases que sean necesarias según el número de patologías que se deseen identificar. Esta información de diagnosis, obtenida mediante la identificación del sistema de reconocimiento de patrones, podría ser de ayuda u orientación para la posterior revisión de la prueba por parte de un profesional médico cualificado y de manera remota, evitando así el desplazamiento del mismo a zonas donde, por los medios existentes a día de hoy, es muy remota la posibilidad de presencia de personal sanitario. ABTRACT Start of development of an efficient algorithm designed to devices with low processing power, which could help people without adequate preparation to undertake a process of taking a biological signal, such as an electrocardiogram. Therefore, the application must assist the user in taking the signal and evaluating the quality of the recording. All of this must to be in live time. It must to check the quality of the signal obtained, and if is it necessary a repetition of the test, this could be done immediately. Furthermore, the algorithm must extract the most relevant features of the ECG signal, process it, and get meaningful patterns that allow to a diagnosis orientation of some of the more common diseases that can be drawn from the cardiac signal information. For the extraction, evaluation and decision making in this previous process to the generation of diagnosis, we will follow the classic architecture of a pattern recognition system, defining the necessary classes according to the number of pathologies that we wish to identify. This diagnostic information obtained by identifying the pattern recognition system could be for help or guidance for further review of the signal by a qualified medical professional, and it could be done remotely, thus avoiding the movements to areas where nowadays it is extremely unlikely to place any health staff, due to the poor economic condition.