Development of an amperometric biosensor for phenol detection.

With the aim of searching for an in situ method for monitoring phenol, Agaricus bisporus tissue with tyrosine activity was used as a biocomponent and an oxygen electrode used as a transducer to develop a biosensor. The experimental methodology investigated the relation between dissolved oxygen and phenol concentration using a standard solution. Biosensor calibration was evaluated by studying reaction time and tissue amount necessary to promote a reliable response and to minimize errors. The influence of air saturation of the sample and washing of the electrode was also investigated. Results showed that 5 g of mushroom tissue with a 1 min reaction time promoted the best biosensor response within a phenol concentration range of 5-10 ppm. Washing of the electrode did not change the performance of the analysis; however, initial air saturation caused less variation amongst the samples.

Agaricus bisporus as a source of tyrosinase for phenol detection for future biosensor development.

Phenols are toxic compounds that are present in several industrial wastewaters, so their quantification has great environmental importance. In order to permit an analytical methodology for in situ monitoring, this work aims to study the application of Agaricus bisporus tissue as a source of tyrosinase and the optimum reaction conditions for the development of a phenol biosensor. Such an enzyme is a polyphenol oxidase that transforms many different phenolic compounds into quinones. Experiments with fungi tissue were performed to evaluate different sizes of tissue (0.5, 1.0 and 1.5 cm), different temperatures (23.5 degrees C to 60 degrees C), and different pH values (6, 7 and 8) to quantify analytically phenol content. Amongst the tested conditions, those that had presented larger efficiency in phenol oxidation were attained with the fungal tissue size of 1 cm, at pH 8.0, in the temperature range from 35 degrees C to 45 degrees C.

Assessment of the impact of salinity and irradiance on the combined carbon dioxide sequestration and carotenoids production by Dunaliella salina: A mathematical model.

Current anthropogenic activities have been causing a significant increase in the atmospheric concentration of CO2 over the past 60 years. To mitigate the consequent global warming problem, efficient technological solutions, based on economical and technical grounds, are required. In this work, microalgae are studied as important biological systems of CO2 fixation into organic compounds through photosynthesis. These microorganisms are potential sources of a wide variety of interesting chemical compounds, which can be used for commercial purposes, reducing the cost of CO2 capture and sequestration. Specifically, Dunaliella salina culture was studied aiming at the impact evaluation of operational conditions over cellular growth and carotenoid production associated with the CO2 sequestration on focus. The main experimental parameters investigated were salinity and irradiance conditions. The experimental results supported the development of a descriptive mathematical model of the process. Based on the proposed model, a sensitivity analysis was carried out to investigate the operational conditions that maximize CO2 consumption and carotenoid production, in order to guide further development of technological routes for CO2 capture through microalgae. A preliminary cost estimation of CO2 sequestration combined to carotenoids production for a 200 MW power plant is presented, based on the growth rates achieved in this study.

Stalked protozoa identification by image analysis and multivariable statistical techniques.

Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semiautomatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in wastewater treatment plants by determining the geometrical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Geometrical descriptors were found to be responsible for the best identification ability and the identification of the crucial Opercularia and Vorticella microstoma microorganisms provided some degree of confidence to establish their presence in wastewater treatment plants.

Yarrowia lipolytica lipase production enhanced by increased air pressure.

AIMS: To study the cellular growth and morphology of Yarrowia lipolytica W29 and its lipase and protease production under increased air pressures. METHODS AND RESULTS: Batch cultures of the yeast were conducted in a pressurized bioreactor at 4 and 8 bar of air pressure and the cellular behaviour was compared with cultures at atmospheric pressure. No inhibition of cellular growth was observed by the increase of pressure. Moreover, the improvement of the oxygen transfer rate (OTR) from the gas to the culture medium by pressurization enhanced the extracellular lipase activity from 96.6 U l(-1) at 1 bar to 533.5 U l(-1) at 8 bar. The extracellular protease activity was reduced by the air pressure increase, thereby eliciting further lipase productivity. Cell morphology was slightly affected by pressure, particularly at 8 bar, where cells kept the predominant oval form but decreased in size. CONCLUSIONS: OTR improvement by total air pressure rise up to 8 bar in a bioreactor can be applied to the enhancement of lipase production by Y. lipolytica. SIGNIFICANCE AND IMPACT OF THE STUDY: Hyperbaric bioreactors can be successfully applied for yeast cells cultivation, particularly in high-density cultures used for enzymes production, preventing oxygen limitation and consequently increasing overall productivity.

Model-based optimization of a sequencing batch reactor for biological nitrogen removal.

An optimal operating mode for a sequencing batch reactor was determined via a model-based optimization. Synthetic wastewater containing mainly organic matter (as glucose) and nitrogen (as ammonium chloride) was treated without any addition of an external carbon source to accomplish denitrification step. A simplified model was used to describe process dynamics, comprised of six ordinary differential equations and an empirical correlation for oxygen consumption rate. Batch cycle time was the chosen objective function to be minimized for a fixed volume of waste to be treated. Furthermore, as SBR operation is divided in two major phases - aerobic and anoxic, to achieve total pollutants removal within minimum time, these phases can be repeatedly alternated. To ensure availability of organic matter necessary for denitrification, these two phases were combined with feed steps. Different feed strategies were tested using one, two or three feed steps. A successive quadratic programming algorithm was used, and maximum values for final COD, nitrate and ammonium concentrations, as well as maximum feed pump flow rate were some the process constraints. One step feed strategy was indicated by the optimization leading to a batch cycle time of 5h.

Recognition of Protozoa and Metazoa using image analysis tools, discriminant analysis, neural networks and decision trees.

Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. This work presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes, with high values for the overall species recognition with the exception of sessile protozoa. In terms of the wastewater conditions assessment the obtained results were found to be suitable for the prediction of these conditions. Finally, the discriminant analysis and neural networks results were found to be quite similar whereas the decision trees technique was less appropriate.

Development of an image analysis procedure for identifying protozoa and metazoa typical of activated sludge system.

A procedure for the semi-automatic identification of the main protozoa and metazoa species present in the activated sludge of wastewater treatment plants was developed. This procedure was based on both image processing and multivariable statistical methodologies, leading to the use of the image analysis morphological descriptors by discriminant analysis and neural network techniques. The image analysis program written in Matlab has proved to be adequate in terms of protozoa and metazoa recognition, as well as for the operating conditions assessment.

Cell surface characterization of Yarrowia lipolytica IMUFRJ 50682.

In the present work, the surface characteristics of a wild-type strain of Yarrowia lipolytica (IMUFRJ50682) were investigated. Six different methods to characterize cell surfaces--adhesion to polystyrene; hydrophobic interaction chromatography (HIC); microbial adhesion to solvents (MATS) test; zeta potential; microbial adhesion to hydrocarbons (MATH) test; and contact angle measurement (CAM)--were employed to explain the cell surface behaviour of Y. lipolytica (IMUFRJ50682). This Y. lipolytica strain presents significant differences at the cell surface compared with another Y. lipolytica strain (W29) previously reported in the literature. The main difference is related to the higher cell adhesion to non-polar solvents. The proteins present on the cell wall of Y. lipolytica IMUFRJ50682 seem to play an important role in these particular surface characteristics because of the consistent reduction of this yeast hydrophobic character after the action of pronase on its cell wall.

Optimization and modeling of laccase production by Trametes versicolor in a bioreactor using statistical experimental design.

Experimental design and response surface methodologies were applied to optimize laccase production by Trametes versicolor in a bioreactor. The effects of three factors, initial glucose concentration (0 and 9 g/L), agitation (100 and 180 rpm), and pH (3.0 and 5.0), were evaluated to identify the significant effects and its interactions in the laccase production. The pH of the medium was found to be the most important factor, followed by initial glucose concentration and the interaction of both factors. Agitation did not seem to play an important role in laccase production, nor did the interaction agitation x medium pH and agitation x initial glucose concentration. Response surface analysis showed that an initial glucose concentration of 11 g/L and pH controlled at 5.2 were the optimal conditions for laccase production by T. versicolor. Under these conditions, the predicted value for laccase activity was >10,000 U/L, which is in good agreement with the laccase activity obtained experimentally (11,403 U/L). In addition, a mathematical model for the bioprocess was developed. It is shown that it provides a good description of the experimental profile observed, and that it is capable of predicting biomass growth based on secondary process variables.

Tyrosinase extract from Agaricus bisporus mushroom and its in natura tissue for specific phenol removal.

Phenols are toxic pollutants found in industrial wastes imposing several risks to human health. Tyrosinase (EC 1.14.18.1) is an oxygenase oxyreductase found in several life forms, like the mushroom Agaricus bisporus. This enzyme is readily available from this fungal tissue leading to high activity extracts without extensive purification, thus suggesting its potential as a biocatalyst for applications involving biomodification of phenols or bioremediation of phenol-polluted waters. The purpose of this work was to employ a crude extract from the Agaricus bisporus mushroom and its biomass for the removal of phenol from polluted water. Experiments were carried out without pH control. The initial phenol concentration in all solutions was 100 mg l(-1). Four enzymatic concentrations (50, 100, 200 and 400 U ml(-1)) were tested. Reactions, with 200 U ml(-1) and 400 U ml(-1) enzymatic activity, led to 90% of phenol removal. Chitosan was used as a coagulant, but no significant improvement was observed. The in natura fungi was also able to remove 90% of phenol, demostrating its viability as a biocatalyst in bioremediation process.

Decolorization of dyes from textile wastewater by Trametes versicolor.

The white rot fungus Trametes versicolor was applied to the decolourisation of three synthetic textile dyes in the presence and absence of glucose. Different initial dye concentrations were tested and approximately 97% decolourisation was achieved. It was found that fungal metabolism induced by the glucose as well as the pH play an important role in the decolourisation process. This treatment was also applied to a real wastewater from a textile industry-dyeing sector leading to 92% decolourisation.

Effect of hyperbaric stress on yeast morphology: study by automated image analysis.

The effects of hyperbaric stress on the morphology of Saccharomyces cerevisiae were studied in batch cultures under pressures between 0.1 MPa and 0.6 MPa and different gas compositions (air, oxygen, nitrogen or carbon dioxide), covering aerobic and anaerobic conditions. A method using automatic image analysis for classification of S. cerevisiae cells based on their morphology was developed and applied to experimental data. Information on cell size distribution and bud formation throughout the cell cycle is reported. The results show that the effect of pressure on cell activity strongly depends on the nature of the gas used for pressurization. While nitrogen and air to a maximum of 0.6 MPa of pressure were innocuous to yeast, oxygen and carbon dioxide pressure caused cell inactivation, which was confirmed by the reduction of bud cells with time. Moreover, a decrease in the average cell size was found for cells exposed for 7.5 h to 0.6 MPa CO2.

Morphological analysis of Yarrowia lipolytica under stress conditions through image processing.

Stress conditions (thermal and oxidative) were applied to Yarrowia lipolytica culture. A rearrangement in cell metabolism as well as dimorphism was observed under these conditions. An image analysis procedure was employed for morphology characterization, and a net increase of around 25% on hyphae formation was detected. A significant increment in total hyphal length was detected, compared with the control system. The results obtained lead to the consideration of a possible relationship between dimorphism and a cell response mechanism to stress conditions.