Quantitative Detection of Syntrophic Fatty Acid-degrading Bacterial Communities in Methanogenic Environments

In methanogenic habitats, volatile fatty acids (VFA), such as propionate and butyrate, are major intermediates in organic matter degradation. VFA are further metabolized to H2, acetate and CO2 by syntrophic fatty acid-degrading bacteria (SFAB) in association with methanogenic archaea. Despite their indispensable role in VFA degradation, little is known about SFAB abundance and their environmental distribution. To facilitate ecological studies, we developed four novel genus-specific quantitative PCR (qPCR) assays, with primer sets targeting known SFAB: Syntrophobacter, Smithella, Pelotomaculum and Syntrophomonas. Primer set specificity was confirmed using in silico and experimental (target controls, clone libraries and melt-curve analysis) approaches. These qPCR assays were applied to quantify SFAB in a variety of mesophilic methanogenic habitats, including a laboratory propionate enrichment culture, pilotand full-scale anaerobic reactors, cow rumen, horse faeces, an experimental rice paddy soil, a bog stream and swamp sediments. The highest SFAB 16S rRNA gene copy numbers were found in the propionate enrichment culture and anaerobic reactors, followed by the bog stream and swamp sediment samples. In addition, it was observed that SFAB and methanogen abundance varied with reactor configuration and substrate identity. To our knowledge, this research represents the first comprehensive study to quantify SFAB in methanogenic habitats using qPCR-based methods. These molecular tools will help investigators better understand syntrophic microbial communities in engineered and natural environments

Tied factor analysis for face recognition across large pose differences

Face recognition algorithms perform very unreliably when the pose of the probe face is different from the gallery face: typical feature vectors vary more with pose than with identity. We propose a generative model that creates a one-to-many mapping from an idealized “identity” space to the observed data space. In identity space, the representation for each individual does not vary with pose. We model the measured feature vector as being generated by a pose-contingent linear transformation of the identity variable in the presence of Gaussian noise. We term this model “tied” factor analysis. The choice of linear transformation (factors) depends on the pose, but the loadings are constant (tied) for a given individual. We use the EM algorithm to estimate the linear transformations and the noise parameters from training data. We propose a probabilistic distance metric that allows a full posterior over possible matches to be established. We introduce a novel feature extraction process and investigate recognition performance by using the FERET, XM2VTS, and PIE databases. Recognition performance compares favorably with contemporary approaches

Higher Education Financial Assistance Tools for Middle- and Upper-Income Taxpayers

This article describes higher education financial assistance tools designed mainly for students of middle- and upper-income families who may not be eligible for financial aid from other sources. It includes the 2007 legislative updates for these tools, all of which have been devised and offered by either state or federal governments. The authors discuss the advantages and disadvantages of each tool and offer planning suggestions for families, students, and others who may participate in the higher education financial planning process

The North West cyber security industry:export potential assessment

Cyber Security businesses in the North West of England form an industry which is growing. This study has taken an in depth analysis of the industry to determine its size, make up and readiness to export and is the first of its kind classifying business within the framework identified by the UKTI cyber export strategy. This report presents the findings of the research undertaken independently by Lancaster University commissioned by the UKTI

Stock Walk with Consumable Deliverables: Association of Price and Dividend in the Nigerian Capital Market

This study examined the relationship between stock price and dividend per share. In the analytical framework, dividend per share (the independent variable) serves as proxy for corporate financial performance, while stock price of consumer good firms listed on the Nigerian Stock Exchange (NSE) features as dependent variable. The baseline assumption is that investors rely on key financial indicators (KFIs) in making rational investment decisions. All 26 consumer good firms on the Daily Official List on 31st December, 2014 were eligible for involvement, by using a profit – dividend payment filter, 18 (69%) of the firms selected. The panel data so harnessed covered a period of six years (2009-2014), and 108 observations were made. Data treatment methods include descriptive statistics, correlation and regression analyses, as well as t-test. The results established that stock price is significantly associated with dividend per share, at the 0.05 level. Against this concentric analytical outcome, it was recommended that investment analysts/managers should mine unfolding information to mind the dynamics of dividends, especially in the consumer goods market segment. In tracking and attracting greater competitive stock pricing fortunes, they should reengineer internal market mechanisms to strategically guard against scenic transaction pitfalls particularly in fragile trading settings.Key Words: Dividend, Stock market, Price