A New and Fast Technique to Generate Offspring after Germ Cells Transplantation in Adult Fish: The Nile Tilapia (Oreochromis niloticus) Model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Use of sorghum seed tissue as a biocatalyst in a stirred reactor for oxalic acid determination

The enzyme oxalate oxidase, E.C. 1.2.3.4 from Sorghum vulgare seeds (variety BR303) was used to develop a new sensor for oxalate determination without any purification. The sorghum seeds were conditioned in a 0.10 mol I-1 KCl solution. Then, these seeds were put in a stirring bar type enzymic reactor and coupled with an electrode for CO2. This device was introduced into a cell containing 10.0 ml of a 0.10 mol I-1 KCl solution saturated with oxygen. This sensor showed a linear response between 1.0 and 4.0 × 10-3 mol I-1 with a slope of 30 mV per decade of oxalate concentration at 25.0°C. The sensor was stable for one month or 200 determinations. The response time was about 60 s. The Michaelis-Menten constant determined for this enzyme was 1.5 × 10-3 mol I-1.

Determination of Oxalate in Grass by FIA with a Spectrophotometric Detection System Using a Two Enzyme Reactor

A new methodology for soluble oxalic acid determination in grass samples was developed using a two enzyme reactor in an FIA system. The reactor consisted of 3 U of oxalate oxidase and 100 U of peroxidase immobilized on Sorghum vulgare seeds activated with glutaraldehyde. The carbon dioxide was monitored spectrophotometrically, after reacting with an acid-base indicator (Bromocresol Purple) after it permeated through a PTFE membrane. A linear response range was observed between 0.25 and 1.00mmol l-1 of oxalic acid; the data was fit by the equation A=-0.8(±1.5)+ 57.2(±2.5)[oxalate], with a correlation coefficient of 0.9971 and a relative standard deviation of 2% for n=5. The variance for a 0.25 mmol l-1 oxalic acid standard solution was lower than 4% for 11 measurements. The FIA system allows analysis of 20 samples per hour without prior treatment. The proposed method showed a good correlation with that of the Sigma Kit.

A fast electric load forecasting using neural networks

The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.

A fast electric load forecasting using adaptive neural networks

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.

Immobilization of lipases and assay in continuous fixed bed reactor

Lipases are versatile enzymes regarding the range of reactions they catalyse and substrates on which they act. They are as well important as catalyst in organic synthesis. Their immobilization on appropriate supports confer them greater stability besides the possibility of operating in continuous reactors. In order to explore these abilities, the reactions involving hydrolysis of p-nitrophenyl acetate (PNPA) and transesterification of PNPA with n-butanol were chosen. Lipases from two different sources were assayed, namely: microbial (Candida rugosa, CRL, Sigma Type VII) and pancreatic (PPL, Sigma, Type 11). Two immobilization methods were also used, namely: 1) adsorption, using as support the following silica derivatives (150-300μm e 450μ): phenyl, epoxy, amino and without derivation, and 2) covalent binding, using glutaraldehyde as binding agent and silica amino as support. This later method led to better results. Hydrolytic activity was 6.1 U/gsupport for CRL and 0.97U/gsupport for PPL, and of transesterification, 2,8U/gsupport for CRL and 1,9U/gsupport for PPL. Stability of the immobilized enzyme as a function of temperature was evaluated for CRL at 40°C and 50°C and for PPL at 32°C and 40°C. The assays were initially carried out batchwise, both for soluble and immobilized enzymes, aiming to the obtention of parameters for the continues reactor. Lipases immobilized by covalent binding were used in the assays of operacional stability in continuos reactors. For PPL in aqueous medium, at 32°C, and CRL in organic medium at 40°C, both operating continuously, no significant loss of activity was detected along the analysis period of 17 days. In the case of CRL in aqueous medium at 40°C there was a loss of activity around 40% after 18 days. For PPL in organic medium at 40°C the loss was 33% after 20 days. Compairing both sources with each other, very different results were obtained. Higher activitiy was found for CRL, both for hydrolysis and for transesterification reactions, with higher stability in organic medium. PPL showed lower activity as well as higher stability in aqueous medium. The immobilization method by covalent binding showed to be the most appropriate. Immobilized lipases are therefore relatively stable both in aqueous and organic medium.

Photoelectrocatalytic production of active chlorine on nanocrystalline titanium dioxide thin-film electrodes

The production of chlorine and hypochlorite is of great economical and technological interest due to their large-scale use in many kinds of commercial applications. Yet, the current processes are not without problems such as inevitable side reactions and the high cost of production. This work reports the photoelectrocatalytic oxidation of chloride ions to free chlorine as it has been investigated by using titanium dioxide (TiO2) and several metal-doped titanium dioxide (M-TiO2) material electrodes. An average concentration of 800 mg L-1 of free chlorine was obtained in an open-air reactor using a TiO2 thin-film electrode biased at +1.0 V (SCE) and illuminated by UV light. The M-doped electrodes have performed poorly compared with the pure TiO2 counterpart. Test solutions containing 0.05 mol L-1 NaCl pH 2.0-4.0 were found to be the best conditions for fast production of free chlorine. A complete investigation of all parameters that influence the global process of chlorine production by the photoelectrocatalytic method such as applied potential, concentration of NaCl, pH solution, and time is presented in detail. In addition, photocurrent vs potential curves and the reaction order are also discussed.

Electrospray ionization mass spectrometry screening of piperidine alkaloids from Senna spectabilis (Fabaceae) extracts: Fast identification of new constituents and co-metabolites

The fragmentation pattern of a homologous series of piperidine alkaloids isolated from S. spectabilis was investigated using electrospray ionization tandem mass spectrometry (ESI-MS/MS). The ESI-MS and ESI-MS/MS analyses of EtOH extracts and fractions from flowers and fruits of S. spectabilis allowed to elucidate the structures of four new compounds. The identification of these co-metabolites, based on the fragmentation patterns of previously isolated compounds, and further confirmed by accurate mass spectrometry defines this technique as a powerful tool to determine the metabolomic profile of species which has pharmacological importance. ©2005 Sociedade Brasileira de Química.

Fast preparative separation of flavones from capitula of Eriocaulon ligulatum (Vell.) L.B.Smith (Eriocaulaceae) by High-speed countercurrent chromatography (HSCCC)

High-speed countercurrent chromatography (HSCCC) is a leading method for the fast separation of natural products from plants. It was used for the preparative isolation of two flavone monoglucosides present in the capitula of Eriocaulon ligulatum (Veil.) L.B.Smith (Eriocaulaceae). This species, known locally as botão-dourado, is exported to Europe, Japan and North America as an ornamental species, constituting an important source of income for the local population of Minas Gerais State, Brazil. The solvent system, optimized in tests prior to the HSCCC run, consisted of the two phases of the mixture ethyl acetate: n-propanol: water (140:8:80, v/v/v), which led to the successful separation of 6-methoxyluteolin-7-O-β-D-allopyranoside and 6-methoxyapigenin-7-O-β-D-allopyranoside in only 3 hours. The two flavonoids were identified by NMR (1-D and 2-D) and ESI-MS, comparing their spectra with published data.

Microwave-induced fast decalcification of rat bone for electron microscopic analysis: An ultrastructural and cytochemical study

Bone decalcification is a time-consuming process. It takes weeks and preservation of the tissue structure depends on the quality and velocity of the demineralization process. In the present study, a decalcification methodology was adapted using microwaving to accelerate the decalcification of rat bone for electron microscopic analysis. The ultrastructure of the bone decalcified by microwave energy was observed. Wistar rats were perfused with paraformaldehyde and maxillary segments were removed and fixed in glutaraldehyde. Half of specimens were decalcified by conventional treatment with immersion in Warshawsky solution at 4oC during 45 days, and the other half of specimens were placed into the beaker with 20 mL of the Warshawsky solution in ice bath and thereafter submitted to irradiation in a domestic microwave oven (700 maximum power) during 20 s/350 W/±37°C. In the first day, the specimens were irradiated 9 times and stored at 40°C overnight. In the second day, the specimens were irradiated 20 times changing the solution and the ice after each bath. After decalcification, some specimens were postfixed in osmium tetroxide and others in osmium tetroxide and potassium pyroantimonate. The specimens were observed under transmission electron microscopy. The results showed an increase in the decalcification rate in the specimens activated by microwaving and a reduction of total experiment time from 45 days in the conventional method to 48 hours in the microwave-aided method.

Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

Fast petroleum well drilling monitoring through optimum-path forest

Automatic inspection of petroleum well drilling has became paramount in the last years, mainly because of the crucial importance of saving time and operations during the drilling process in order to avoid some problems, such as the collapse of the well borehole walls. In this paper, we extended another work by proposing a fast petroleum well drilling monitoring through a modified version of the Optimum-Path Forest classifier. Given that the cutting's volume at the vibrating shale shaker can provide several information about drilling, we used computer vision techniques to extract texture informations from cutting images acquired by a digital camera. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and effciency. We used the Optimum-Path Forest (OPF), EOPF (Efficient OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP) Support Vector Machines (SVM), and a Bayesian Classifier (BC) to assess the robustness of our proposed schema for petroleum well drilling monitoring through cutting image analysis.

Morphological changes in the fast vs slow fiber profiles of the urethras of diabetic pregnant rats

Background: This study was undertaken to test the hypothesis that diabetes and pregnancy detrimentally affect the normal function of urethral striated muscles in rats, providing a model for additional studies related to urinary incontinence. The aim of this study was to evaluate morphological alterations in the urethral striated muscles of diabetic pregnant rats. Materials and Methods: Twenty female Wistar rats were distributed into four experimental groups of five rats as follows: virgin, pregnant, diabetic virgin, and diabetic pregnant. Diabetes was induced using streptozotocin administration (40 mg/kg i.v.). The rats were lethally anesthetized, and the urethra and vagina were extracted as a unit. Cryostat sections (6 μm thick) were cut and stained with hematoxylin-eosin, and immunohistochemical procedures were performed and subjected to morphological and semi quantitative analysis. Results: The urethral striated muscle from the diabetic pregnant rats presented with the following variations: thinning and atrophy, disorganization and disruption associated with the colocalization of fast and slow fibers and a steady decrease in the proportion of fast vs slow fibers. Conclusion: Diabetes and pregnancy impair the urethral striated muscle and alter its fiber type distribution. © Copyright G. Marini et al., 2011.