Insulin and leptin relations in obesity: a multimedia approach

Obesity has been recognized as a worldwide public health problem. It significantly increases the chances of developing several diseases, including Type II diabetes. The roles of insulin and leptin in obesity involve reactions that can be better understood when they are presented step by step. The aim of this work was to design software with data from some of the most recent publications on obesity, especially those concerning the roles of insulin and leptin in this metabolic disturbance. The most notable characteristic of this software is the use of animations representing the cellular response together with the presentation of recently discovered mechanisms on the participation of insulin and leptin in processes leading to obesity. The software was field tested in the Biochemistry of Nutrition web-based course. After using the software and discussing its contents in chatrooms, students were asked to answer an evaluation survey about the whole activity and the usefulness of the software within the learning process. The teaching assistants (TA) evaluated the software as a tool to help in the teaching process. The students' and TAs' satisfaction was very evident and encouraged us to move forward with the software development and to improve the use of this kind of educational tool in biochemistry classes.

Discharge-calcium concentration relationships in streams of the Amazon and Cerrado of Brazil: soil or land use controlled

Stream discharge-concentration relationships are indicators of terrestrial ecosystem function. Throughout the Amazon and Cerrado regions of Brazil rapid changes in land use and land cover may be altering these hydrochemical relationships. The current analysis focuses on factors controlling the discharge-calcium (Ca) concentration relationship since previous research in these regions has demonstrated both positive and negative slopes in linear log(10)discharge-log(10)Ca concentration regressions. The objective of the current study was to evaluate factors controlling stream discharge-Ca concentration relationships including year, season, stream order, vegetation cover, land use, and soil classification. It was hypothesized that land use and soil class are the most critical attributes controlling discharge-Ca concentration relationships. A multilevel, linear regression approach was utilized with data from 28 streams throughout Brazil. These streams come from three distinct regions and varied broadly in watershed size (< 1 to > 10(6) ha) and discharge (10(-5.7)-10(3.2) m(3) s(-1)). Linear regressions of log(10)Ca versus log(10)discharge in 13 streams have a preponderance of negative slopes with only two streams having significant positive slopes. An ANOVA decomposition suggests the effect of discharge on Ca concentration is large but variable. Vegetation cover, which incorporates aspects of land use, explains the largest proportion of the variance in the effect of discharge on Ca followed by season and year. In contrast, stream order, land use, and soil class explain most of the variation in stream Ca concentration. In the current data set, soil class, which is related to lithology, has an important effect on Ca concentration but land use, likely through its effect on runoff concentration and hydrology, has a greater effect on discharge-concentration relationships.

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.

Zone trapping/merging zones in flow analysis: A novel approach for rapid assays involving relatively slow chemical reactions

A novel strategy for accomplishing zone trapping in flow analysis is proposed. The sample and the reagent solutions are simultaneously inserted into convergent carrier streams and the established zones merge together before reaching the detector, where the most concentrated portion of the entire sample zone is trapped. The main characteristics, potentialities and limitations of the strategy were critically evaluated in relation to an analogous flow system with zone stopping. When applied to the spectrophotometric determination of nitrite in river waters, the main figures of merit were maintained, exception made for the sampling frequency which was calculated as 189h(-1), about 32% higher relatively to the analogous system with zone stopping. The sample inserted volume can be increased up to 1.0 mL without affecting sampling frequency and no problems with pump heating or malfunctions were noted after 8-h operation of the system. In contrast to zone stopping, only a small portion of the sample zone is halted with zone trapping, leading to these beneficial effects. (C) 2011 Elsevier B.V. All rights reserved.

Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.

The production of school numbers (1871-1931): contributions to a critical approach to statistical sources in History of Education

This article intends to contribute to the reflection on the Educational Statistics as being source for the researches on History of Education. The main concern was to reveal the way Educational Statistics related to the period from 1871 to 1931 were produced, in central government. Official reports - from the General Statistics Directory - and Statistics yearbooks released by that department were analyzed and, on this analysis, recommendations and definitions to perform the works were sought. By rending problematic to the documental issues on Educational Statistics and their usual interpretations, the intention was to reduce the ignorance about the origin of the school numbers, which are occasionally used in current researches without the convenient critical exam.

Electrodeposition of copper on titanium wires: Taguchi experimental design approach

Electrodeposition of thin copper layer was carried out on titanium wires in acidic sulphate bath. The influence of titanium surface preparation, cathodic current density, copper sulphate and sulphuric acid concentrations, electrical charge density and stirring of the solution on the adhesion of the electrodeposits was studied using the Taguchi statistical method. A L(16) orthogonal array with the six factors of control at two levels each and three interactions was employed. The analysis of variance of the mean adhesion response and signal-to-noise ratio showed the great influence of cathodic current density on adhesion. on the contrary, the other factors as well as the three investigated interactions revealed low or no significant effect. From this study optimized electrolysis conditions were defined. The copper electrocoating improved the electrical conductivity of the titanium wire. This shows that copper electrocoated titanium wires could be employed for both electrical purpose and mechanical reinforcement in superconducting magnets. (C) 2008 Elsevier B.V. All rights reserved.

SAB(IO): A BIOLOGICALLY PLAUSIBLE CONNECTIONIST APPROACH TO AUTOMATIC TEXT SUMMARIZATION

An implementation of a computational tool to generate new summaries from new source texts is presented, by means of the connectionist approach (artificial neural networks). Among other contributions that this work intends to bring to natural language processing research, the use of a more biologically plausible connectionist architecture and training for automatic summarization is emphasized. The choice relies on the expectation that it may bring an increase in computational efficiency when compared to the sa-called biologically implausible algorithms.

Response surface methodology as an approach to determine optimal activities of lipase entrapped in sol-gel matrix using different vegetable oils

The conditions for maximization of the enzymatic activity of lipase entrapped in sol-gel matrix were determined for different vegetable oils using an experimental design. The effects of pH, temperature, and biocatalyst loading on lipase activity were verified using a central composite experimental design leading to a set of 13 assays and the surface response analysis. For canola oil and entrapped lipase, statistical analyses showed significant effects for pH and temperature and also the interactions between pH and temperature and temperature and biocatalyst loading. For the olive oil and entrapped lipase, it was verified that the pH was the only variable statistically significant. This study demonstrated that response surface analysis is a methodology appropriate for the maximization of the percentage of hydrolysis, as a function of pH, temperature, and lipase loading.

An integrated approach to produce biodiesel and monoglycerides by enzymatic interestification of babassu oil (Orbinya sp)

Two screenings of commercial lipases were performed to find a lipase with superior performance for the integrated production of biodiesel and monoglycerides. The first screening was carried out under alcoholysis conditions using ethanol as acyl acceptor to convert triglycerides to their corresponding ethyl esters (biodiesel). The second screening was performed under glycerolysis conditions to yield monoglycerides (MG). All lipases were immobilized on silica-PVA composite by covalent immobilization. The assays were performed using babassu oil and alcohols (ethanol or glycerol) in solvent free systems. For both substrates, lipase from Burkholderia cepacia (lipase PS) was found to be the most suitable enzyme to attain satisfactory yields. To further improve the process, the Response Surface Methodology (RSM) was used to determine the optima operating conditions for each biotransformation. For biodiesel production, the highest transesterification yield (>98%) was achieved within 48 h reaction at 39 degrees C using an oil-to-ethanol molar ratio of 1:7. For MG production, optima conditions corresponded to oil-to-glycerol molar ratio of 1: 15 at 55 degrees C, yielding 25 wt.% MG in 6 h reaction. These results show the potential of B. cepacia lipase to catalyze both reactions and the feasibility to consider an integrated approach for biodiesel and MG production. (C) 2009 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.

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.

Loss minimization by the predictor-corrector modified barrier approach

This paper presents a new approach, predictor-corrector modified barrier approach (PCMBA), to minimize the active losses in power system planning studies. In the PCMBA, the inequality constraints are transformed into equalities by introducing positive auxiliary variables. which are perturbed by the barrier parameter, and treated by the modified barrier method. The first-order necessary conditions of the Lagrangian function are solved by predictor-corrector Newton`s method. The perturbation of the auxiliary variables results in an expansion of the feasible set of the original problem, reaching the limits of the inequality constraints. The feasibility of the proposed approach is demonstrated using various IEEE test systems and a realistic power system of 2256-bus corresponding to the Brazilian South-Southeastern interconnected system. The results show that the utilization of the predictor-corrector method with the pure modified barrier approach accelerates the convergence of the problem in terms of the number of iterations and computational time. (C) 2008 Elsevier B.V. All rights reserved.

An approach of optimal sensitivity applied in the tertiary loop of the automatic generation control

This paper proposes an approach of optimal sensitivity applied in the tertiary loop of the automatic generation control. The approach is based on the theorem of non-linear perturbation. From an optimal operation point obtained by an optimal power flow a new optimal operation point is directly determined after a perturbation, i.e., without the necessity of an iterative process. This new optimal operation point satisfies the constraints of the problem for small perturbation in the loads. The participation factors and the voltage set point of the automatic voltage regulators (AVR) of the generators are determined by the technique of optimal sensitivity, considering the effects of the active power losses minimization and the network constraints. The participation factors and voltage set point of the generators are supplied directly to a computational program of dynamic simulation of the automatic generation control, named by power sensitivity mode. Test results are presented to show the good performance of this approach. (C) 2008 Elsevier B.V. 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.