Direct imaging of pH1N1 2009 influenza virus replication in alveolar pneumocytes in fatal cases by transmission electron microscopy

Human influenza virus pandemics constitute a major global public health issue. Although studies on autopsy specimens from the recent pandemic by the 2009 influenza A (H1N1) virus have revealed a broad spectrum of pathologic findings, direct electron microscopic studies of the lung tissue from influenza fatalities are few. In this study, we examined five well-preserved pulmonary necropsy specimens from fatal cases of laboratory-confirmed pH1N1 from India. The novel observations in comparison with earlier reports included direct imaging of influenza virus budding within dilated cisternae of pneumocytes, cell-free virus emerging from the cell membrane of a pneumocyte in the alveolar lumen, presence of polymorphonuclear cells with red blood cells as inflammatory exudates close to hyaline membranes and extensive cytoplasmic degeneration of epithelial cells of the alveolar lining. These observations are in consistent with the earlier findings and emphasize the possible role of this virus directly infecting cells of the lower respiratory tract as a key event in the rapid pathogenesis of pH1N1 disease process.     

Imaging the interaction between dengue 2 virus and human blood platelets using atomic force and electron microscopy

Thrombocytopenia is frequently associated with dengue virus infection. Host factors such as anti-platelet immunopathogenic processes have been implicated in the origin of dengue-associated thrombocytopenia but the role of dengue virus in directly interacting with platelets and altering their hemostatic property remains incompletely understood. In the present study, we examined the effect of dengue 2 virus on the morphology and physiological activation profile of normal human platelets using atomic force microscopy, electron microscopy and flowcytometry. Platelets obtained from healthy donors were exposed to a cell culture-adapted 10(4) LD(50) dengue 2 virus isolate in vitro and the subsequent effect on morphology and activation biology studied. Our results show that dengue 2 virus exposure at doses comparable to natural viremic states in human infections can activate platelets with an increase in P-selectin expression and fibrinogen-binding property. Atomic force, scanning and transmission electron microscopy also showed typical activation-related morphological changes such as altered platelet membrane architecture, degranulation, presence of filopodia and dilatation of the open canalicular system in the dengue 2 virus-exposed platelets but not in the controls. Importantly, Japanese encephalitis virus exposure at the same dose did not activate platelets or show any morphological changes. Our findings suggest that dengue 2 virus may directly interact with and activate platelets - an event that might be important in the origin of dengue-associated thrombocytopenia. Detailed molecular characterization of this effect might provide key knowledge toward better prophylaxis of the hemostatic complications of dengue disease.     

A semi-supervised probabilistic model for clustering large databases of complex images

Image content clustering is an effective way to organize large databases thereby making the content based image retrieval process much easier. However, clustering of images with varied background and foreground is quite challenging. In this paper, we propose a novel image content clustering paradigm suitable for clustering large and diverse image databases. In our approach images are represented in a continuous domain based on a probabilistic Gaussian Mixture Model (GMM) with the images modeled as mixture of Gaussian distributions in the selected feature space. The distance metric between the Gaussian distributions is defined in the sense of Kullback–Leibler (KL) divergence. The clustering is done using a semi-supervised learning framework where labeled data in the form of cluster templates is used to classify the unlabelled data. The clusters are formed around initially chosen seeds and are updated in the due course based on user inputs. In our clustering approach the user interaction is done in a structured way as to get maximum inputs from the user in a limited time. We propose two methods to carry out the structured user interaction using which the cluster templates are updated to improve the quality of the clusters formed. The proposed method is experimentally evaluated on benchmark datasets that are specifically chosen to include a wide variation of images around a common theme that is typically encountered in applications like photo-summarization and poses a major semantic gap challenge to conventional clustering approaches. The experimental results presented demonstrate the effectiveness of the proposed approach.     

Deep Graph-Long Short-Term Memory: A Deep Learning Based Approach for Text Classification

Wireless Personal Communications - Multi-label text classification is a challenging task in many real applications. Mostly, in all the traditional techniques, word2vec is used to show the...