Resolving genetic functions within microbial populations: In situ analyses using rRNA and mRNA stable isotope probing coupled with single-cell raman-fluorescence in situ hybridization

Prokaryotes represent one-half of the living biomass on Earth, with the vast majority remaining elusive to culture and study within the laboratory. As a result, we lack a basic understanding of the functions that many species perform in the natural world. To address this issue, we developed complementary population and single-cell stable isotope (C-13)-linked analyses to determine microbial identity and function in situ. We demonstrated that the use of rRNA/mRNA stable isotope probing (SIP) recovered the key phylogenetic and functional RNAs. This was followed by single-cell physiological analyses of these populations to determine and quantify in situ functions within an aerobic naphthalene-degrading groundwater microbial community. Using these culture-independent approaches, we identified three prokaryote species capable of naphthalene biodegradation within the groundwater system: two taxa were isolated in the laboratory (Pseudomonas fluorescens and Pseudomonas putida), whereas the third eluded culture (an Acidovorax sp.). Using parallel population and single-cell stable isotope technologies, we were able to identify an unculturable Acidovorax sp. which played the key role in naphthalene biodegradation in situ, rather than the culturable naphthalene-biodegrading Pseudomonas sp. isolated from the same groundwater. The Pseudomonas isolates actively degraded naphthalene only at naphthalene concentrations higher than 30 mu M. This study demonstrated that unculturable microorganisms could play important roles in biodegradation in the ecosystem. It also showed that the combined RNA SIP-Raman-fluorescence in situ hybridization approach may be a significant tool in resolving ecology, functionality, and niche specialization within the unculturable fraction of organisms residing in the natural environment.

Infinite Dimensional Banach spaces of functions with nonlinear properties

The aim of this paper is to show that there exist infinite dimensional Banach spaces of functions that, except for 0, satisfy properties that apparently should be destroyed by the linear combination of two of them. Three of these spaces are: a Banach space of differentiable functions on Rn failing the Denjoy-Clarkson property; a Banach space of non Riemann integrable bounded functions, but with antiderivative at each point of an interval; a Banach space of infinitely differentiable functions that vanish at infinity and are not the Fourier transform of any Lebesgue integrable function.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.