F. Ferreira

Optical fiber biosensor coupled to chromatographic separation for screening of dopamine, norepinephrine and epinephrine in human urine and plasma

An optical fiber biosensor has been developed for the determination of catecholamines (dopamine, norepinephrine and epinephrine) based on the recognition capacity of the enzyme laccase. In this study, a glass tube constituted by a fused silica fiber coated with a film of polystyrene/divinylbenzene resin (PS/DVB) was used for catecholamines separation. Firstly, the analyzer was tested for calibration and its analytical performance for catecholamines detection was compared with a classical analytical method, namely high performance liquid chromatography-electrochemical detector (HPLC-ED). The developed analytical device shows a high potential for catecholamines quantification with a detection limit of 2.1, 2.6 and 3.4 pg mL(-1) for dopamine, norepinephrine and epinephrine, respectively. The analytical sensitivity, inferred from the slope of the calibration curves established for a range of concentrations between 5 and 125 pg mL(-1), was found to be 0.344, 0.252 and 0.140 dB/pg mL(-1) for dopamine, norepinephrine and epinephrine, respectively. Furthermore, catecholamines speciation with the PS/DVB fiber was completely achieved in 3 min. The analytical performance of the reported sensor was also evaluated and found adequate for catecholamines determination in human urine and plasma samples.

Biological treatment of the effluent from a bleached kraft pulp mill using basidiomycete and zygomycete fungi

Three white-rot fungi (Pleurotus sajor caju, Trametes versicolor and Phanerochaete chrysosporium) and one soft-rot fungi (Rhizopus oryzae) species confirmed their potential for future applications in the biological treatment of effluents derived from the secondary treatment of a bleached kraft pulp mill processing Eucalyptus globulus. Among the four species P. sajor caju and R. oryzae were the most effective in the biodegradation of organic compounds present in the effluent, being responsible for the reduction of relative absorbance (25-46% at 250 nm and 72-74% at 465 nm) and of chemical oxygen demand levels (74 to 81%) after 10 days of incubation. Laccase (Lac), lignin (Lip) and manganese peroxidases (MnP) expression varied among fungal species, where Lac and LiP activities were correlated with the degradation of organic compounds in the effluent treated with P. sajor caju. The first two axes of a principal component analysis explained 88.9% of the total variation among sub-samples treated with the four fungus species, after different incubation periods. All the variables measured contributed positively to the first component except for the MnP enzyme activity which was the only variable contributing negatively to the first component. Absorbances at 465 nm, LiP and Lac enzyme activities were the variables with more weight on the second component. P. sajor caju revealed to be the only species able to perform the biological treatment without promoting an increment in the toxicity of the effluent to the Vibrio fischeri, as it was assessed by the Microtox assay. The opposite was recorded for the treatments with the other three species of fungus. EC(50-5 min) values ranging between 28 and 57% (effluent concentrations) were recorded even after 10 to 13 days of treatment with P. chrysosporium, R. oryzae or with T. versicolor.

High performance liquid chromatography coupled to an optical fiber detector coated with laccase for screening catecholamines in plasma and urine

An analytical method based on separation by high performance liquid chromatography (HPLC) and detection by optical fiber (OF) coated with an enzyme (laccase), has been developed for separation and quantification of catecholamines, namely epinephrine, dopamine and norepinephrine. The application of OF as a detector in this analytical system relies on the variation of the reflected optical power detected when the catecholamines eluted from the HPLC column act as the substrate of the laccase immobilized on a tip of a single-mode OF. The developed method shows a high linearity in a range between 5 and 125 pg/mL and detection limits of 3.5, 2.9 and 3.3 pg/mL for epinephrine, dopamine and norepinephrine, respectively. The analytical performance of the proposed method was compared with a classical analytical method, namely high performance liquid chromatography-electrochemical detector (HPLC-ED) regarding catecholamines detection, showing great analytical advantages such as low cost of equipment. Additionally, the proposed method was applied to catecholamines determination in actual samples of plasma and human urine.

The effectiveness of a biological treatment with Rhizopus oryzae and of a photo-Fenton oxidation in the mitigation of toxicity of a bleached kraft pulp mill effluent.

Huge efforts have been made both in adopting more environmental-friendly bleaching processes, and in developing advanced oxidation processes and more effective biological treatments for the reduction of deleterious impacts of paper mill effluents. Even so, the success of such treatments is frequently reported in terms of chemical parameters without a proper evaluation of the effluents toxicity mitigation. This is the first study reporting an exhaustive evaluation of the toxicity of a secondary bleached kraft pulp mill effluent, after either tertiary treatment with the soft-rot fungi Rhizopus oryzae or with a photo-Fenton oxidation, using a battery of freshwater species. As it has been reported the photo-Fenton/UV treatment has proved to be the most effective in reducing the colour and the COD (chemical oxygen demand) of the effluent. Nevertheless, extremely low EC(50) values were reported for almost all species, after this tertiary treatment. The treatment with R. oryzae was less effective in terms of colour removal and COD reduction, but proved to be the most promising in reducing toxicity.

Carbon nanotube field-effect transistor detector associated to gas chromatography for speciation of benzene, toluene, ethylbenzene, (o-, m- and p-)xylene.

An analytical methodology based on a field-effect transistor detector using carbon nanotubes (NTFET) coupled to a gas chromatograph has been developed for the speciation of the following aromatic compounds: benzene, toluene, ethylbenzene, m-xylene, p-xylene and o-xylene (BTEX). This methodology combines the proven separation capability of gas chromatography (GC) with the potential for detection of a NTFET. The developed analyzer shows a high and stable analytical response upon repeated analysis of BTEX during 4 weeks, with detection limit less than 4mug/L. The GC-NTFET system also shows a great suitability for actual monitoring of indoor atmospheres and no significant difference was observed between the results obtained by the developed analyzer and a more classical analytical methodology, namely gas chromatography-flame ionization detection (GC-FID).

Integration of Multiple Sensors using Binary Features in a Bernoulli Mixture Model

This article reports on the use of a Bernoulli mixture model to integrate features extracted independently from two or more distinct sensors. Local image features (SIFT) and multiple types of features from a 2D laser range scan are all converted into Binary form and integrated into a single binary feature incidence matrix (FIM). The correlation between the different features is captured by modeling the resultant FIM in terms of a Bernoulli mixture model. The integration of binary features from different sensors allows for good place recognition. The use of binary features also promises a much simpler integration of features from dissimilar sensors

ROBUST PLACE RECOGNITION WITHIN MULTI-SENSOR VIEW SEQUENCES USING BERNOULLI MIXTURE MODELS

Abstract This article reports on the use of Hidden Markov Models to improve the results of Localization within a sequence of Sensor Views. Local image features (SIFT) and multiple types of features from a 2D laser range scan are all converted into binary form and integrated into a single, binary, Feature Incidence Matrix (FIM). To reduce the large dimensionality of the binary data, it is modeled in terms of a Bernoulli Mixture providing good results that were reported in an earlier presentation. We have improved the good performance of the approach by incorporating the Bernoulli mixture model inside a Bayesian Network Model, an HMM, that accumulates evidence as the robot travels along the environment.

A low-level framework for a probabilistic treatment of the topological description of a robot mission

This article describes a mathematical basis required to integrate features obtained for perception for topological navigation. It is intended for application to navigation in an environment that is not mapped, but in which a mission is described in the form of a semantic description of the perception stimulus that the robot is expected to encounter. The need to integrate features from different sensors led to the use of an uncertainty estimate employed in information theory; binary entropy. By using entropy, the features are ranked in order of decreasing uncertainty. This article describes the state of the work in an as yet preliminary stage, but appears promising for application to navigation using topological information. It also offers interesting perspectives on commonly used sensory data such as local intensity image features.