Desenvolvimento de aplicações em medicina e agronomia utilizando lógica fuzzy e neuro fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Assessment of in vitro sperm characteristics and their importance in the prediction of conception rate in a bovine timed-AI program

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.

An improved interolog mapping-based computational prediction of protein protein interactions with increased network coverage

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fast determination of beef quality parameters with time-domain nuclear magnetic resonance spectroscopy and chemometrics

The noteworthy of this study is to predict seven quality parameters for beef samples using time-domain nuclear magnetic resonance (TD-NMR) relaxometry data and multivariate models. Samples from 61 Bonsmara heifers were separated into five groups based on genetic (breeding composition) and feed system (grain and grass feed). Seven sample parameters were analyzed by reference methods; among them, three sensorial parameters, flavor, juiciness and tenderness and four physicochemical parameters, cooking loss, fat and moisture content and instrumental tenderness using Warner Bratzler shear force (WBSF). The raw beef samples of the same animals were analyzed by TD-NMR relaxometry using Carr-Purcell-Meiboom-Gill (CPMG) and Continuous Wave-Free Precession (CWFP) sequences. Regression models computed by partial least squares (PLS) chemometric technique using CPMG and CWFP data and the results of the classical analysis were constructed. The results allowed for the prediction of aforementioned seven properties. The predictive ability of the method was evaluated using the root mean square error (RMSE) for the calibration (RMSEC) and validation (RMSEP) data sets. The reference and predicted values showed no significant differences at a 95% confidence level.

Development of a simple method for determination of NO2 in air using digital scanner images

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks using a matrix element method

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estimation of lower flammability limits of C-H compounds in air at atmospheric pressure, evaluation of temperature dependence and diluent effect

Estimation of the lower flammability limits of C-H compounds at 25 degrees C and 1 atm; at moderate temperatures and in presence of diluent was the objective of this study. A set of 120 degrees C H compounds was divided into a correlation set and a prediction set of 60 compounds each. The absolute average relative error for the total set was 7.89%; for the correlation set, it was 6.09%; and for the prediction set it was 9.68%. However, it was shown that by considering different sources of experimental data the values were reduced to 6.5% for the prediction set and to 6.29% for the total set. The method showed consistency with Le Chatelier's law for binary mixtures of C H compounds. When tested for a temperature range from 5 degrees C to 100 degrees C , the absolute average relative errors were 2.41% for methane; 4.78% for propane; 0.29% for iso-butane and 3.86% for propylene. When nitrogen was added, the absolute average relative errors were 2.48% for methane; 5.13% for propane; 0.11% for iso-butane and 0.15% for propylene. When carbon dioxide was added, the absolute relative errors were 1.80% for methane; 5.38% for propane; 0.86% for iso-butane and 1.06% for propylene. (C) 2014 Elsevier B.V. All rights reserved.

Classification of intact açai (Euterpe oleracea Mart.) and jucara (Euterpe edulis Mart) fruits based on dry matter content by means of near infrared spectroscopy

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Graded meshes in bio-thermal problems with transmission-line modeling method

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.

Prediction of culture performance of juvenile Litopenaeus vannamei by in vitro (pH-stat) degree of feed protein hydrolysis with species-specific enzymes

Rapid in vitro methods for measuring digestibility may be useful in analysing aqua feeds if the extent and limits of their application are clearly defined. The pH-stat protein digestibility routine with shrimp hepatopancreas enzymes was previously related to apparent protein digestibility with juvenile Litopenaeus vannamei fed diets containing different protein ingredients. The potential of the method to predict culture performance of shrimp fed six commercial feeds (T3, T4, T5, T6, T7 and T8) with 350 g kg(-1) declared crude-protein content was assessed. The consistency of results obtained using hepatopancreas enzyme extracts from either pond or clear water-raised shrimp was further verified in terms of reproducibility and possible diet history effects upon in vitro outputs. Shrimps were previously acclimated and then maintained over 56 days (initial mean weight 3.28 g) on each diet in 500-L tanks at 114 ind m(-2), clear water closed system with continuous renewal and mechanical filtering (50 mu m), with four replicates per treatment. Feeds were offered four times daily (six days a week) delivered in trays at feeding rates ranging from 4.0% to 7.0% of stocked shrimp biomass. Feed was accessible to shrimp 4 h daily for 1-h feeding period after which uneaten feed was recovered. Growth and survival were determined every 14 days from a sample of 16 individuals per tank. Water quality was monitored daily (pH, temperature and salinity) and managed by water back flushing filter cleaning every 7-10 days. Feeds were analysed for crude protein, gross energy, amino acids and pepsin digestibility. In vitro pH-stat degree of protein hydrolysis (DH%) was determined for each feed using hepatopancreas enzyme extracts from experimental (clear water) or pond-raised shrimp. Feeds resulted in significant differences in shrimp performance (P < 0.05) as seen by the differences in growth rates (0.56-0.98 g week(-1)), final weight and feed conversion ratio (FCR). Shrimp performance and in vitro DH% with pond-raised shrimp enzymes showed significant correlation (P < 0.05) for yield (R-2 = 0.72), growth rates (R-2 = 0.72-0.80) and FCR (R-2 = -0.67). Other feed attributes (protein : energy ratio, amino acids, true protein, non-protein nitrogen contents and in vitro pepsin digestibility) showed none or limited correlation with shrimp culture performance. Additional correlations were found between growth rates and methionine (R-2 = 0.73), FCR and histidine (R-2 = -0.60), and DH% and methionine or methionine+cystine feed contents (R-2 = 0.67-0.92). pH-stat assays with shrimp enzymes generated reproducible DH% results with either pond (CV <= 6.5%) or clear water (CV <= 8.5%) hepatopancreas enzyme sources. Moreover, correlations between shrimp growth rates and feed DH% were significant regardless of the enzyme origin (pond or clear water-raised shrimp) and showed consistent R-2 values. Results suggest the feasibility of using standardized hepatopancreas enzyme extracts for in vitro protein digestibility.

Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement

Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)Penman-Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO-Penman-Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature-based estimates by Camargo and Jensen-Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.

Prediction with measurement errors in finite populations

We address the problem of selecting the best linear unbiased predictor (BLUP) of the latent value (e.g., serum glucose fasting level) of sample subjects with heteroskedastic measurement errors. Using a simple example, we compare the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability, the first of which corresponds to simple random sampling and the second, to heteroskedastic measurement errors. Under this last approach, we show that when measurement errors are subject-specific, the BLUP shrinkage constants are based on a pooled measurement error variance as opposed to the individual ones generally considered for the usual mixed model BLUP. In contrast, when the heteroskedastic measurement errors are measurement condition-specific, the FPMM BLUP involves different shrinkage constants. We also show that in this setup, when measurement errors are subject-specific, the usual mixed model predictor is biased but has a smaller mean squared error than the FPMM BLUP which points to some difficulties in the interpretation of such predictors. (C) 2011 Elsevier By. All rights reserved.

Relationship between global structural parameters and Enzyme Commission hierarchy: Implications for function prediction

In protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies suggesting that function evolves fundamentally by recruitment, i.e., a same protein motif or fold can be used to perform different enzymatic functions and a few specific amino acids (AAs) are actually responsible for enzyme activity. These essential amino acids should belong to active sites and an effective method for predicting function should be able to recognize them. (C) 2012 Elsevier Ltd. All rights reserved.