The role of farmed fish in the diets of the resource-poor in Egypt.

The Egyptian aquaculture industry provides more than 100,000 full-time or part-time jobs and produces the country’s least-expensive farmed animal protein. Thus, aquaculture plays an important role in both sustaining livelihoods and improving the diet quality and nutritional health of Egyptians, including a significant proportion of the 25.5% who are resource-poor. Recognizing this dual role, WorldFish has promoted sustainable growth in Egyptian aquaculture for more than 20 years. Through its work, WorldFish has identified a lack of quality data about fish consumption preferences and practices. Eager to fill this knowledge gap, WorldFish partnered with the Environment and Development Group (EDG) to study consumption of fish, red meat and poultry among the resource-poor in Egypt. This study aimed to characterize current consumer preferences for and consumption patterns of animal-source foods, comparing red meat, poultry and fish. The resulting data is meant to contribute to a better understanding of what drives demand for fish among the resource-poor in Egypt, allowing value chain actors to more successfully market their products to this segment of the population.

Nanostructured carbon films from supersonic cluster beam deposition: Structure and morphology

Nanostructured carbon thin films have been grown by deposition of cluster beams produced by a supersonic expansion. Due to separation effects typical of supersonic beams, films with different nanostructures can be grown by the simple intercepting of different regions of the cluster beam with a substrate. Films show a low-density porous structure, which has been characterized by Raman and Brillouin spectroscopy. Film morphology suggests that growth processes are similar to those occurring in a ballistic deposition regime.

Making agri-food systems work for the rural poor in eastern and southern Africa

The purpose of this output was to use the results of the baseline and participatory diagnostics analysis to develop alternative innovations for agricultural production, natural resource management and food security. The farming systems in the project areas were analysed to identify the innovations that communities had been using for agricultural production, natural resource management and food security. The innovative strategies were examined for their contribution to sustainable agriculture, food security and natural resource management. Comparative analysis of the agricultural productivity, food security and natural resource management in the different areas where the innovations have been put in place was undertaken. The best practices would be identified, which should be scaled-up, modified or sustained. The willingness and perceptions of the farmers to adopt the innovations would then be assessed.

Cluster analysis of heterogeneous rank data

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.

Rapid evolution of an X-linked microRNA cluster in primates

MicroRNAs (miRNAs) are a growing class of small RNAs ( about 22 nt) that play crucial regulatory roles in the genome by targeting mRNAs for cleavage or translational repression. Most of the identified miRNAs are highly conserved among species, indicating

Poor fish farmers' accessibility to credits: A review

Fisheries sector contributes about 5.3% to GDP and about 6% of the export earnings of Bangladesh. There are about 4.1 million ha of inland water bodies in Bangladesh. However, over last two decades the catch from inland capture fishery has decreased due to filling of wet lands and other anthropogenic reasons. Accordingly, the production of inland fish has decreased not only for the decrease of water bodies but also due to irrational catch of fish fries, brood fishes and use of current nets for fishing. Significant responses from the fisheries entrepreneurs have not been received for the small loan scheme of the Bangladesh Bank. The bank could not disburse more than Tk. 500 million under the scheme. The total revolving credit under the scheme was Tk. 1,000 million with the assistance from the World Bank. The business houses having fixed assets of value not more than Tk. 10 million will be eligible to borrow from this fund. About Tk. 0.2-5.0 million can be borrowed as term loan and working capital from Bangladesh Bank through commercial banks. The loan was given to the commercial banks at 5% interest (bank rate) and the commercial banks shall also bridge finance to the entrepreneurs at a lower rate of interest. Working capital time limit is for a maximum of 1 year with half yearly rest, mid-term loan maximum of 3 years in 5 installments and with 6 months grace period and long-term loan maximum of 5 years in 9 installments with 6 months grace period.

Polymorphisms in the C-type lectin genes cluster in chromosome 19 and predisposition to severe acute respiratory syndrome coronavirus (SARS-CoV) infection

Background: Polymorphisms of CLEC4M have been associated with predisposition for infection by the severe acute respiratory syndrome coronavirus (SARS-CoV). DC-SIGNR, a C-type lectin encoded by CLEC4M, is a receptor for the virus. A variable number tandem

Global commodity chains and the development of poor fisherfolk in Uganda

The Ugandan fishery, heavily influenced by the emergence of global markets, is extremely dynamic. In recent years a major export trade, principally in Nile perch fillets from Lake Victoria, has expanded markedly. The growth of this factory based processing industry has had a marked impact on the pre-existing artisanal fishery, which has become increasingly dependent on supplying the export market instead of its traditional local small-scale markets. The industrial fishery developed as a response to the liberalisation of the management of the Ugandan economy and the consequent opening up of the export markets in North America and Europe. The emergence of the export industry has resulted in the creation of a dual structure in the fisheries sector, with the Nile perch catching and processing chain operating to European standards, whilst the artisanal sub-sector still utilises traditional methods. This dual structure is a potential source of disadvantage to the artisanal fishery which has command over fewer financial assets than the export fishery.

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

The Gaussian mixture MCMC particle algorithm for dynamic cluster tracking

We present a novel filtering algorithm for tracking multiple clusters of coordinated objects. Based on a Markov chain Monte Carlo (MCMC) mechanism, the new algorithm propagates a discrete approximation of the underlying filtering density. A dynamic Gaussian mixture model is utilized for representing the time-varying clustering structure. This involves point process formulations of typical behavioral moves such as birth and death of clusters as well as merging and splitting. For handling complex, possibly large scale scenarios, the sampling efficiency of the basic MCMC scheme is enhanced via the use of a Metropolis within Gibbs particle refinement step. As the proposed methodology essentially involves random set representations, a new type of estimator, termed the probability hypothesis density surface (PHDS), is derived for computing point estimates. It is further proved that this estimator is optimal in the sense of the mean relative entropy. Finally, the algorithm's performance is assessed and demonstrated in both synthetic and realistic tracking scenarios. © 2012 Elsevier Ltd. All rights reserved.