Abstract:We present an evaluation of daily estimates from three near real-time quasi-global Satellite Precipitation Products-Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Climate Prediction Center (CPC) Morphing Technique (CMORPH)-over the African continent, using the Global Precipitation Climatology Project one Degree Day (GPCP-1dd) as a reference dataset for years 2001 to 2013. Different types of errors are characterized for each season as a function of spatial classifications (latitudinal bands, climatic zones and topography) and in relationship with the main rain-producing mechanisms in the continent: the Intertropical Convergence Zone (ITCZ) and the East African Monsoon. A bias correction of the satellite estimates is applied using a probability density function (pdf) matching approach, with a bias analysis as a function of rain intensity, season and latitude. The effects of bias correction on different error terms are analyzed, showing an almost elimination of the mean and variance terms in most of the cases. While raw estimates of TMPA show higher efficiency, all products have similar efficiencies after bias correction. PERSIANN consistently shows the smallest median errors when it correctly detects precipitation events. The areas with smallest relative errors and other performance measures follow the position of the ITCZ oscillating seasonally over the equator, illustrating the close relationship between satellite estimates and rainfall regime.
The oxidation process of sulfide minerals in natural environments is achieved by microbial communities from the Archaea and Bacteria domains. A metabolic reconstruction of two dominant species, Leptospirillum ferriphilum and Ferroplasma acidiphilum, which are always found together as a mixed culture in this natural environments, was made. The metabolic model, composed of 152 internal reactions and 29 transport reactions, describes the main interactions between these species, assuming that both use ferrous iron as energy source, and F. acidiphilum takes advantage of the organic compounds secreted by L. ferriphilum for chemomixotrophic growth. A first metabolic model for a mixed culture used in bacterial leaching is proposed in this article, which pretends to represent the characteristics of the mixed culture in a simplified manner. It was evaluated with experimental data through flux balance analysis (FBA) using as objective function the maximization of biomass. The growth yields on ferrous iron obtained for each microorganism are consistent with experimental data, and the flux distribution obtained allows understanding of the metabolic capabilities of both microorganisms growing together in a bioleaching process. The model was used to simulate the growth of F. acidiphilum on different substrates, to determine in silico which compounds maximize cell growth, and which are essential. Knockout simulations were carried out for L. ferriphilum and F. acidiphilum metabolic models, predicting key enzymes of central metabolism. The results of this analysis are consistent with experimental data from literature, showing a robust behavior of the metabolic model.
A metabolic model for Leptospirillum ferrooxidans was developed based on the genomic information of an analogous iron oxidizing bacteria and on the pathways of ferrous iron oxidation, nitrogen and CO(2) assimilation based on experimental evidence for L. ferrooxidans found in the literature. From this metabolic reconstruction, a stoichiometric model was built, which includes 86 reactions describing the main catabolic and anabolic aspects of its metabolism. The model obtained has 2 degrees of freedom, so two external fluxes were estimated to achieve a determined and observable system. By using the external oxygen consumption rate and the generation flux biomass as input data, a metabolic flux map with a distribution of internal fluxes was obtained. The results obtained were verified with experimental data from the literature, achieving a very good prediction of the metabolic behavior of this bacterium at steady state.
Biomining is defined as biotechnology for metal recovery from minerals, and is promoted by the concerted effort of a consortium of acidophile prokaryotes, comprised of members of the Bacteria and Archaea domains. Ferroplasma acidiphilum and Leptospirillum ferriphilum are the dominant species in extremely acid environments and have great use in bioleaching applications; however, the role of each species in this consortia is still a subject of research. The hypothesis of this work is that F. acidiphilum uses the organic matter secreted by L. ferriphilum for growth, maintaining low levels of organic compounds in the culture medium, preventing their toxic effects on L. ferriphilum. To test this hypothesis, a characterization of Ferroplasma acidiphilum strain BRL-115 was made with the objective of determining its optimal growth conditions. Subsequently, under the optimal conditions, L. ferriphilum and F. acidiphilum were tested growing in each other's supernatant, in order to define if there was exchange of metabolites between the species. With these results, a mixed culture in batch cyclic operation was performed to obtain main specific growth rates, which were used to evaluate a mixed metabolic model previously developed by our group. It was observed that F. acidiphilum, strain BRL-115 is a chemomixotrophic organism, and its growth is maximized with yeast extract at a concentration of 0.04% wt/vol. From the experiments of L. ferriphilum growing on F. acidiphilum supernatant and vice versa, it was observed that in both cases cell growth is favorably affected by the presence of the filtered medium of the other microorganism, proving a synergistic interaction between these species. Specific growth rates were obtained in cyclic batch operation of the mixed culture and were used as input data for a Flux Balance Analysis of the mixed metabolic model, obtaining a reasonable behavior of the metabolic fluxes and the system as a whole, therefore consolidating the model previously developed. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1390-1396, 2016.
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