Microbial communities in a porphyry copper tailings impoundment and their impact on the geochemical dynamics of the mine waste

The distribution and diversity of acidophilic bacteria of a tailings impoundment at the La Andina copper mine, Chile, was examined. The tailings have low sulfide (1.7% pyrite equivalent) and carbonate (1.4% calcite equivalent) contents and are stratified into three distinct zones: a surface (0-70-80 cm) `oxidation zone' characterized by low-pH (2.5-4), a `neutralization zone' (70-80 to 300-400 cm) and an unaltered `primary zone' below 400 cm. A combined cultivation-dependent and biomolecular approach (terminal restriction enzyme fragment length polymorphism and 16S rRNA clone library analysis) was used to characterize the indigenous prokaryotic communities in the mine tailings. Total cell counts showed that the microbial biomass was greatest in the top 125 cm of the tailings. The largest numbers of bacteria (10(9) g(-1) dry weight of tailings) were found at the oxidation front (the junction between the oxidation and neutralization zones), where sulfide minerals and oxygen were both present. The dominant iron-/sulfur-oxidizing bacteria identified at the oxidation front included bacteria of the genus Leptospirillum (detected by molecular methods), and Gram-positive iron-oxidizing acidophiles related to Sulfobacillus (identified both by molecular and cultivation methods). Acidithiobacillus ferrooxidans was also detected, albeit in relatively small numbers. Heterotrophic acidophiles related to Acidobacterium capsulatum were found by molecular methods, while another Acidobacterium-like bacterium and an Acidiphilium sp. were isolated from oxidation zone samples. A conceptual model was developed, based on microbiological and geochemical data derived from the tailings, to account for the biogeochemical evolution of the Piuquenes tailings impoundment.

From caging to rouse dynamics in polymer melts with intramolecular barriers: a critical test of the mode coupling theory

By means of computer simulations and solution of the equations of the mode coupling theory (MCT),we investigate the role of the intramolecular barriers on several dynamic aspects of nonentangled polymers. The investigated dynamic range extends from the caging regime characteristic of glass-formers to the relaxation of the chain Rouse modes. We review our recent work on this question,provide new results, and critically discuss the limitations of the theory. Solutions of the MCT for the structural relaxation reproduce qualitative trends of simulations for weak and moderate barriers. However, a progressive discrepancy is revealed as the limit of stiff chains is approached. This dis-agreement does not seem related with dynamic heterogeneities, which indeed are not enhanced by increasing barrier strength. It is not connected either with the breakdown of the convolution approximation for three-point static correlations, which retains its validity for stiff chains. These findings suggest the need of an improvement of the MCT equations for polymer melts. Concerning the relaxation of the chain degrees of freedom, MCT provides a microscopic basis for time scales from chain reorientation down to the caging regime. It rationalizes, from first principles, the observed deviations from the Rouse model on increasing the barrier strength. These include anomalous scaling of relaxation times, long-time plateaux, and nonmonotonous wavelength dependence of the mode correlators.

Exploring neural cell dynamics with digital holographic microscopy.

In this review, we summarize how the new concept of digital optics applied to the field of holographic microscopy has allowed the development of a reliable and flexible digital holographic quantitative phase microscopy (DH-QPM) technique at the nanoscale particularly suitable for cell imaging. Particular emphasis is placed on the original biological information provided by the quantitative phase signal. We present the most relevant DH-QPM applications in the field of cell biology, including automated cell counts, recognition, classification, three-dimensional tracking, discrimination between physiological and pathophysiological states, and the study of cell membrane fluctuations at the nanoscale. In the last part, original results show how DH-QPM can address two important issues in the field of neurobiology, namely, multiple-site optical recording of neuronal activity and noninvasive visualization of dendritic spine dynamics resulting from a full digital holographic microscopy tomographic approach.

Stochastic inversion of tracer test and electrical geophysical data to estimate hydraulic conductivities.

Quantifying the spatial configuration of hydraulic conductivity (K) in heterogeneous geological environments is essential for accurate predictions of contaminant transport, but is difficult because of the inherent limitations in resolution and coverage associated with traditional hydrological measurements. To address this issue, we consider crosshole and surface-based electrical resistivity geophysical measurements, collected in time during a saline tracer experiment. We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to jointly invert the dynamic resistivity data, together with borehole tracer concentration data, to generate multiple posterior realizations of K that are consistent with all available information. We do this within a coupled inversion framework, whereby the geophysical and hydrological forward models are linked through an uncertain relationship between electrical resistivity and concentration. To minimize computational expense, a facies-based subsurface parameterization is developed. The Bayesian-McMC methodology allows us to explore the potential benefits of including the geophysical data into the inverse problem by examining their effect on our ability to identify fast flowpaths in the subsurface, and their impact on hydrological prediction uncertainty. Using a complex, geostatistically generated, two-dimensional numerical example representative of a fluvial environment, we demonstrate that flow model calibration is improved and prediction error is decreased when the electrical resistivity data are included. The worth of the geophysical data is found to be greatest for long spatial correlation lengths of subsurface heterogeneity with respect to wellbore separation, where flow and transport are largely controlled by highly connected flowpaths.