Release of Intracellular Calcium Stores Facilitates Coxsackievirus Entry into Polarized Endothelial Cells

Group B coxsackieviruses (CVB) are associated with viral-induced heart disease and are among the leading causes of aseptic meningitis worldwide. Here we show that CVB entry into polarized brain microvasculature and aortic endothelial cells triggers a depletion of intracellular calcium stores initiated through viral attachment to the apical attachment factor decay-accelerating factor. Calcium release was dependent upon a signaling cascade that required the activity of the Src family of tyrosine kinases, phospholipase C, and the inositol 1,4,5-trisphosphate receptor isoform 3. CVB-mediated calcium release was required for the activation of calpain-2, a calcium-dependent cysteine protease, which controlled the vesicular trafficking of internalized CVB particles. These data point to a specific role for calcium signaling in CVB entry into polarized endothelial monolayers and highlight the unique signaling mechanisms used by these viruses to cross endothelial barriers

A comparative analysis of host responses to avian influenza infection in ducks and chickens highlights a role for the interferon-induced transmembrane proteins in viral resistance

Chickens are susceptible to infection with a limited number of Influenza A viruses and are a potential source of a human influenza pandemic. In particular, H5 and H7 haemagglutinin subtypes can evolve from low to highly pathogenic strains in gallinaceous poultry. Ducks on the other hand are a natural reservoir for these viruses and are able to withstand most avian influenza strains. Results: Transcriptomic sequencing of lung and ileum tissue samples from birds infected with high (H5N1) and low (H5N2) pathogenic influenza viruses has allowed us to compare the early host response to these infections in both these species. Chickens (but not ducks) lack the intracellular receptor for viral ssRNA, RIG-I and the gene for an important RIG-I binding protein, RNF135. These differences in gene content partly explain the differences in host responses to low pathogenic and highly pathogenic avian influenza virus in chicken and ducks. We reveal very different patterns of expression of members of the interferon-induced transmembrane protein (IFITM) gene family in ducks and chickens. In ducks, IFITM1, 2 and 3 are strongly up regulated in response to highly pathogenic avian influenza, where little response is seen in chickens. Clustering of gene expression profiles suggests IFITM1 and 2 have an anti-viral response and IFITM3 may restrict avian influenza virus through cell membrane fusion. We also show, through molecular phylogenetic analyses, that avian IFITM1 and IFITM3 genes have been subject to both episodic and pervasive positive selection at specific codons. In particular, avian IFITM1 showed evidence of positive selection in the duck lineage at sites known to restrict influenza virus infection. Conclusions: Taken together these results support a model where the IFITM123 protein family and RIG-I all play a crucial role in the tolerance of ducks to highly pathogenic and low pathogenic strains of avian influenza viruses when compared to the chicken

Consistent pan-sharpening based on multistage joint and dual bilateral filters

A pan-sharpening method using joint and dual bilateral filters (DBFs) has been proposed. This approach is based on a consistent combination of large- and small-scale features obtained from the decomposition of high spectral resolution multispectral (MS) and high spatial resolution panchromatic (PAN) images. In the decomposition process, MS and PAN images are used to extract the features using joint and DBFs, respectively. These filters accommodate the relationship between MS and PAN images and decompose them into a base layer (large-scale) and a detail layer (small-scale). Since the joint bilateral filter (JBF) preserves the edges of an auxiliary image, it is used for decomposition of MS images where different layers are estimated using the PAN image as an auxiliary image. Similarly, different layers of the PAN image are obtained from a DBF which preserves the edges of both (MS and PAN) input images. This process is further extended to multistage decomposition to obtain a bilateral image pyramid. The base and detail layers of both MS and PAN images obtained at various stages are combined using a weighted sum. Finally, the estimated weighted sum of detail layer (small-scale) of the PAN image is fused separately to the weighted base layers (large-scale) of the MS images. Performance of the proposed method is evaluated by conducting the experiments on degraded as well as undegraded datasets, captured using different satellites such as Quickbird, Ikonos-2, and Worldview-2. The noise rejection capabilities of the proposed method are also tested by conducting experiments on the noisy data. The results are compared with the widely popular methods and the recently proposed fusion approaches based on a bilateral filter. Along with qualitative evaluation, the quantitative performance of the proposed fusion technique has also been verified by estimating different measures for degraded and undegraded experiments. The experimental results and quantitative measures demonstrate that the proposed method performs better in degraded and undegraded conditions along with noisy situations when compared to other state-of-art methods.by Sharad Joshi, Kishor P. Upla and Nitin Khann

Multi-object Detection in Night Time

This paper discusses the work on detecting multi-objects such as person and car in thermal image captured during night time using deep learning architecture. Thermal images are superior to the visible images when it comes to the amount of useful information required to detect the objects during night time. Thermal imager uses radiation emitted by the objects to create an image and improve the visibility of objects in a dark environment. Contrast to that, visible image does not provide useful information in darkness. Hence, it is better to use thermal images to detect objects present in darkness. The state-of-the-art, Yolo-v3, deep learning convolutional neural network model is the latest version of the Yolo model in which the feature extraction layer contains a much deeper network. The results of detecting person and car in the thermal images obtained by the proposed model are compared with the results of Yolo- v3. Experimental results show that there is a significant improvement in detecting person and car in the thermal images in terms of mean average precision (mAP) using the proposed method

Assessing the Potential for Private Sector Engagement in Integrated Landscape Approaches: Insights from Value-Chain Analyses in Southern Zambia

Agricultural and forested landscapes in Africa are changing rapidly in response to socio-economic and environmental pressures. Integrated landscape approaches provide an opportunity for a more holistic and coordinated resource management strategy through the engagement of multiple stakeholders. Despite their influence as landscape actors, participation of private businesses in such initiatives has thus far been limited. This study focuses on the Kalomo District in southern Zambia, which provides an example of a rural landscape characterized by high levels of poverty, low agricultural productivity, and widespread deforestation and forest degradation. The study applied a value-chain analysis approach to better understand how the production of four locally important commodities (maize, tobacco, cattle, and charcoal) impacts land use, local livelihoods, and environmental objectives in this landscape, focusing on the role and influence of private sector actors. Data were collected through focus group discussions and key informant semi-structured interviews. Qualitative content analysis was employed to analyze the data and contextualize the findings. Results indicate three key potential entry points for increased private sector engagement: (1) improving water security for smallholders; (2) empowering small and medium-sized enterprises (SMEs) as private sector actors; and (3) collective planning for sustainable landscape activities with deliberate measures to involve private sector actors. We discuss options for optimizing benefits from the identified entry points

Physico-chemical properties and fatty acid composition of Lagneraria siceraria seed oil

Oil was extracted from the dehulled seeds of Lagneraria siceraria (bottle gourd) and analysed for physico-chemical properties, as well a fatty acid composition. Standard procedures were employed in all analysis. The seed oil was liquid at room temperature with percentage yield (23.65%). The oil was characterized in terms of specific gravity (0.918 g/cm3), refractive index (1.34), viscosity (26.46 X 103 poise), melting point (11-14.5 °C), moisture content (0.18%), saponification value (203.36 mg KOH/g), unsaponifiable matter (7.13%), iodine value (46.1 g/100g), peroxide value (7.5 meq/kg), free fatty acid value (18.42%), acid value (60.02 mg KOH/g) and ester value (143.34 mg KOH/g). It was also classified as non- drying (iodine value ˂115 g/100 g). The peroxide value indicates that the oil is less prone to rancidity with iodine value less than 30meq/kg. The high saponification value qualifies it for use in the manufacture of soaps and shampoos. Four classes of fatty acid were identified in the oil: palmitic acid (C16:1) (13.5 ± 0.21), stearic acid (C18:1) (6.5 ± 0.96), oleic acid (C18:1) (11.6 ± 0.62) and linoleic acid (C18:2) (68.4 ± 0.13). Linoleic acid was the most abundant fatty acid in the oil. The fatty acid content of the oil reveals that L. Siceraria seed oil could be a rich source of oil for domestic and industrial purposes if exploited

Assessing the Potential for Private Sector Engagement in Integrated Landscape Approaches : Insights from Value-Chain Analyses in Southern Zambia

Agricultural and forested landscapes in Africa are changing rapidly in response to socio-economic and environmental pressures. Integrated landscape approaches provide an opportunity for a more holistic and coordinated resource management strategy through the engagement of multiple stakeholders. Despite their influence as landscape actors, participation of private businesses in such initiatives has thus far been limited. This study focuses on the Kalomo District in southern Zambia, which provides an example of a rural landscape characterized by high levels of poverty, low agricultural productivity, and widespread deforestation and forest degradation. The study applied a value-chain analysis approach to better understand how the production of four locally important commodities (maize, tobacco, cattle, and charcoal) impacts land use, local livelihoods, and environmental objectives in this landscape, focusing on the role and influence of private sector actors. Data were collected through focus group discussions and key informant semi-structured interviews. Qualitative content analysis was employed to analyze the data and contextualize the findings. Results indicate three key potential entry points for increased private sector engagement: (1) improving water security for smallholders; (2) empowering small and medium-sized enterprises (SMEs) as private sector actors; and (3) collective planning for sustainable landscape activities with deliberate measures to involve private sector actors. We discuss options for optimizing benefits from the identified entry points.Forestry, Faculty ofNon UBCForest and Conservation Sciences, Department ofReviewedFacultyResearcherGraduat