A new approach for in vitro bioassay to measure tannin biological effects based on a gas production technique

The aim of this paper was to study a method based on gas production technique to measure the biological effects of tannins on rumen fermentation. Six feeds were used as fermentation substrates in a semi-automated gas method: feed A - aroeira (Astronium urundeuva); feed B - jurema preta (Mimosa hostilis), feed C - sorghum grains (Sorghum bicolor); feed D - Tifton-85 (Cynodon sp.); and two others prepared mixing 450 g sorghum leaves, 450 g concentrate (maize and soybean meal) and 100 g either of acacia (Acacia mearnsii) tannin extract (feed E) or quebracho (Schinopsis lorentzii) tannin extract (feed F) per kg (w:w). Three assays were carried out to standardize the bioassay for tannins. The first assay compared two binding agents (polyethylene glycol - PEG - and polyvinyl polypirrolidone - PVPP) to attenuate the tannin effects. The complex formed by PEG and tannins showed to be more stable than PVPP and tannins. Then, in the second assay, PEG was used as binding agent, and this assay was done to evaluate levels of PEG (0, 500, 750, 1000 and 1250 mg/g DM) to minimize the tannin effect. All the tested levels of PEG produced a response to evaluate tannin effects but the best response was for dose of 1000 mg/g DM. Using this dose of PEG, the final assay was carried out to test three compounds (tannic acid, quebracho extract and acacia extract) to establish a curve of biological equivalent effect of tannins. For this, five levels of each compound were added to I g of a standard feed (Lucerne hay). The equivalent effect showed not to be directly related to the chemical analysis for tannins. It was shown that different sources of tannins had different activities or reactivities. The curves of biological equivalence can provide information about tannin reactivity and its use seems to be important as an additional factor for chemical analysis. (C) 2007 Elsevier B.V. All rights reserved.

FATTY ACID COMPOSITION OF ACID, BIOLOGICAL AND ENZYMATIC FISH SILAGE

The use of the fish silage as an ingredient in feed for aquatic organisms is an alternative to solve sanitary and environmental problems caused by the lack of an adequate destination for the residues generated by the fishing industry. It would also lower the costs with feed, and consequently the fish production costs, since the expenses with the feed account for approximately 60% of the total cost. The objective of this study was to evaluate the fatty acid composition of the acid silage (AS), biological silage (BS) and enzymatic silage (ES) produced from discardings of the culture and from processing residues of the Nile tilapia (Oreochromis niloticus). The values found for lipids (dry matter basis) were: 12.45; 12.25 and 12.17 g 100 g(-1) for BS, AS, and ES, respectively. The fatty acids present in the lipid fraction of the silages are predominantly unsaturated. Oleic acid was present in larger amounts (30.49, 28.60 and 30.60 g 100 g(-1) of lipids for BS, AS and ES, respectively). Among saturated fatty acids, palmitic and stearic acids were present in larger amounts. Only traces of eicosapentaenoic (EPA) and docosahexaenoic (DHA) fatty acids were found. The silages produced from discardings of the culture and processing residues of the Nile tilapia are not a good source of EPA and DHA for fish feeds.

Contribuition of nitrogen from biological nitrogen fixation, nitrogen fertilizer and soil nitrogen on the growth of the common bean and cowpea

Biological nitrogen fixation (BNF) constitutes a valuable source of this nutrient for the common bean Phaseolus vulgaris L and cowpea Vigna unguiculata (L.) Walp., being its avaibility affected by mineral N in the soil solution. The objectives of this work were to evaluate the effects of nitrogen rate, as urea, on symbiotic fixation of N(2) in common bean and cowpea plants, using the isotopic technique, and quantifying the relative contributions of N sources symbiotic N(2) fixation, soil native nitrogen and urea N on the growth of the common bean and cowpea. Non nodulating soybean plants were used as standard. The research was carried out in greenhouse, using pots with 5 kg of soil from a Typic Haplustox (Dystrophic Red Yellow Latosol). The experimental design was completely randomized blocks, with 30 treatments and three replications, arranged in 5x3x2 factorial outline. The treatments consisted of five N rates: 2, 15, 30, 45 and 60 mg N kg(-1) soil; three sampling times: 23, 40 and 76 days after sowing (DAS) and two crops: common bean and cowpea. The BNF decreased with increase N rates, varying from 81.5% to 55.6% for cowpea, and from 71.9% to 55.1% for common bean. The symbiotic N(2) fixation in cowpea can substitute totally the nitrogen fertilization. The nitrogen absorption from soil is not affected by nitrogen fertilizer rate. The N recovery from fertilizer at 76 DAS was of 60.7% by common bean, and 57.1% by cowpea. The symbiotic association in common bean needs the application of a starting dose (40 kg N ha(-1)) for economically acceptable yields.

Influences of sex and age on biological rhythms of serum lipids and lipoproteins

Background: Few studies have evaluated seasonal variations of biochemical parameters routinely analyzed in clinical laboratories. Rhythmic patterns for lipids and lipoproteins have been demonstrated and have been the object of research, mainly because of their demonstrated association with coronary artery disease. This study evaluated the occurrence of biological rhythms on serum lipids and lipoproteins and the effects of sex and age on the rhythms in a Brazilian hospital outpatient population. Methods: Retrospective laboratory study was carried out to evaluate the results of total cholesterol (TC), HDL-cholesterol (HDL-C), LDL-cholesterol (LDL-C) and triglycerides (TG), from individuals registered at a university referral hospital over 8years. The studied population was composed of individuals of both sexes and all ages totaling 38,579 participants and 301,934 measurements. Statistical analyses were carried out using the SAS program and the temporal analysis used the Cosinor method. Results: TG rhythm was present only in females. All other parameters were equally rhythmic in both sexes. Regarding age, HDL-C presented rhythms in all age groups, but TC and LDL-C showed seasonality only for those > 13years, TG did not present rhythms in all age groups. Conclusion: Effects of sex and age on biological rhythms detected in TC, LDL-C and HDL-C should be considered a significant cause of pre-analytical variation in these laboratory tests. (C) 2009 Elsevier B.V. All rights reserved.

Correlation of Modified Natural Rubber Properties by Artificial Neural Networks

Natural rubber (NR) is a raw material largely used by the modern industry; however, it is common that chemical modifications must be made to NR in order to improve properties such as hydrophobicity or mechanical resistance. This work deals with the correlation of properties of NR modified with dimethylaminoethylmethacrylate or methylmethacrylate as grafting agents. Dynamic-mechanical behavior and stress/strain relations are very important properties because they furnish essential characteristics of the material such as glass transition temperature and rupture point. These properties are concerned with different physical principles; for this reason, normally they are not related to each other. This work showed that they can be correlated by artificial neural networks (ANN). So, from one type of assay, the properties that as a rule only could be obtained from the other can be extracted by ANN correlation. POLYM. ENG. SCI., 49:499-505, 2009. (c) 2009 Society of Plastics Engineers

Optimization of the AZO dyes decoloration process through neural networks: Determination of the H2O2 addition critical point

The concentration of hydrogen peroxide is an important parameter in the azo dyes decoloration process through the utilization of advanced oxidizing processes, particularly by oxidizing via UV/H2O2. It is pointed out that, from a specific concentration, the hydrogen peroxide works as a hydroxyl radical self-consumer and thus a decrease of the system`s oxidizing power happens. The determination of the process critical point (maximum amount of hydrogen peroxide to be added) was performed through a ""thorough mapping"" or discretization of the target region, founded on the maximization of an objective function objective (constant of reaction kinetics of pseudo-first order). The discretization of the operational region occurred through a feedforward backpropagation neural model. The neural model obtained presented remarkable coefficient of correlation between real and predicted values for the absorbance variable, above 0.98. In the present work, the neural model had, as phenomenological basis the Acid Brown 75 dye decoloration process. The hydrogen peroxide addition critical point, represented by a value of mass relation (F) between the hydrogen peroxide mass and the dye mass, was established in the interval 50 < F < 60. (C) 2007 Elsevier B.V. All rights reserved.

Biological detoxification of different hemicellulosic hydrolysates using Issatchenkia occidentalis CCTCC M 206097 yeast

This work had as its main objective to contribute to the development of a biological detoxification of hemicellulose hydrolysates obtained from different biomass plants using Issatchenkia occidentalis CCTCC M 206097 yeast. Tests with hemicellulosic hydrolysate of sugarcane bagasse in different concentrations were carried out to evaluate the influence of the hydrolysate concentration on the inhibitory compounds removal from the sugarcane bagasse hydrolysate, without reduction of sugar concentration. The highest reduction values of inhibitors concentration and less sugar losses were observed when the fivefold concentrated hydrolysate was treated by the evaluated yeast. In these experiments it was found that the high sugar concentrations favored lower sugar consumption by the yeast. The highest concentration reduction of syringaldehyde (66.67%), ferulic acid (73.33%), furfural (62%), and 5-HMF (85%) was observed when the concentrated hydrolysate was detoxified by using this yeast strain after 24 h of experimentation. The results obtained in this work showed the potential of the yeast Issatchenkia occidentalis CCTCC M 206097 as detoxification agent of hemicellulosic hydrolysate of different biomass plants.

Novel Isolates for Biological Detoxification of Lignocellulosic Hydrolysate

In this paper, two new strians, Issatchenkia occidentalis (Lj-3, CCTCC M 2006097) and Issatchenkia orienalis (S-7, CCTCC M 2006098), isolated from different environments on solid media, were used in the detoxification process of the hemicellulosic hydrolysate of sugarcane bagasse. High-pressure liquid chromatography elution curve of UV-absorption compounds represented by acetic acid, furfural, and guaiacol (toxic compounds found in the hemicellulosic hydrolysate) showed that several chromatographic peaks were evidently diminished for the case of detoxified hydrolysate with isolate strains compared to the high peaks resulted for no detoxified hydrolysate. It was clear that these inhibitors were degraded by the two new isolates during their cultivation process. Fermentation results for the biodetoxified hydrolysate showed an increase in xylitol productivity (Q (p)) by 1.97 and 1.95 times (2.03 and 2.01 g l(-1) h(-1)) and in xylitol yield (Y (p)) by 1.72 and 1.65 times (0.93 and 0.89 g xylitol per gram xylose) for hydrolysate treated with S-7 and Lj-3, respectively, in comparison with no detoxified hydrolysate (1.03 g l(-1) h(-1) and 0.54 g xylitol per gram xylose). This present work demonstrated the importance of Issatchenkia yeast in providing an effective biological detoxification approach to remove inhibitors and improve hydrolysate fermentability, leading to a high xylitol productivity and yield.

Analysis of Bacteria, Parasites, and Heavy Metals in Lettuce (Lactuca sativa) and Rocket Salad (Eruca sativa L.) Irrigated with Treated Effluent from a Biological Wastewater Treatment Plant

This study aimed to evaluate the viability of using treated residuary water from the Biological Wastewater Treatment Plant of Ribeiro Preto to grow vegetables, through the characterization and quantification of parasites, coliforms, and heavy metals. Three equal cultivation areas were prepared. The first was irrigated with treated/chlorinated (0.2 mg L(-1)) wastewater, the second one with treated wastewater without chlorination, and the third site with potable water, which was the control group. The presence of Hymenolepis nana, Enterobius vermicularis, nematode larvae, and Entamoeba coli was verified in lettuce (Lactuca sativa) samples. Although nematode larvae were observed in rocket salad (Eruca sativa L.), no significant differences were found between the number of parasites and type of irrigation water used. No significant differences were found between the number of fecal coliforms in vegetables and the different types of irrigation. However, the vegetables irrigated with treated effluent without chlorination showed higher levels of fecal coliforms. The risk of pathogens is reduced with bleach addition to the treated effluent at 0.2 mg/L. Concentration of heavy metals in vegetables does not mean significant risks to human health, according with the parameters recommended by the World Health Organization.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Multi-objective analysis of impacts of distributed generation placement on the operational characteristics of networks for distribution system planning

Recent advances in energy technology generation and new directions in electricity regulation have made distributed generation (DG) more widespread, with consequent significant impacts on the operational characteristics of distribution networks. For this reason, new methods for identifying such impacts are needed, together with research and development of new tools and resources to maintain and facilitate continued expansion towards DG. This paper presents a study aimed at determining appropriate DG sites for distribution systems. The main considerations which determine DG sites are also presented, together with an account of the advantages gained from correct DG placement. The paper intends to define some quantitative and qualitative parameters evaluated by Digsilent (R), GARP3 (R) and DSA-GD software. A multi-objective approach based on the Bellman-Zadeh algorithm and fuzzy logic is used to determine appropriate DG sites. The study also aims to find acceptable DG locations both for distribution system feeders, as well as for nodes inside a given feeder. (C) 2010 Elsevier Ltd. All rights reserved.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop

This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed-crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The inferred categorical variable values for the weed-crop competitiveness along with three other categorical variables extracted from estimated maps for the weed seed production and weed coverage are then used as input for a second Bayesian network classifier to infer categorical variables values for the risk of infestation. Weed biomass and yield loss data samples are used to learn the probability relationship among the nodes of the first and second Bayesian classifiers in a supervised fashion, respectively. For comparison purposes, two types of Bayesian network structures are considered, namely an expert-based Bayesian classifier and a naive Bayes classifier. The inference system focused on the knowledge interpretation by translating a Bayesian classifier into a set of classification rules. The results obtained for the risk inference in a corn-crop field are presented and discussed. (C) 2009 Elsevier Ltd. All rights reserved.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.