Dissimilaridade genética e análise de trilha de cultivares de soja avaliada por meio de descritores quantitativos

In addition to the evaluations among genotypes, the use of multivariate techniques enables to restrict errors, mainly concerning genetic diversity, and therefore to predict combinations with greater heterotic effect, and the greater possibility of recovery of superior genotypes. The objective of this study was to evaluate the genetic divergence between 18 soybean cultivars based on six morphological characteristics. Path analysis was performed to verify the contribution of direct and indirect characters on grain yield. The Mahalanobis distance has founded techniques of both Tocher Method and dendrogram by Single Linkage. Five different groups were formed: with nine genotypes considered similar among them; while the cultivars CEP 59, Netuno and Urano formed groups isolated by the two grouping methods. The path analysis showed that the indirect characters had little influence on grain yield, with significant direct relationship with mass of 100 grain, and cultivars Tertulha and CEP 53 standing out with grain yields above 3.7 t.ha-1.

Identification and feature selection of non-technical losses for industrial consumers using the software WEKA

This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids. © 2012 IEEE.

BBA: A binary bat algorithm for feature selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.

Improving image classification through descriptor combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.

Efeitos do extrato de parede de levedura em dieta seca sobre a Microbiologia, ácidos graxos de cadeia curta e redução do odor das fezes de gatos adultos

The aim of this study was to evaluate the effects of yeast cell wall extract (YCW) in dry diet on the fecal microbiota, concentration of short-chain fatty acids (SCFA) and on the odor reduction of cats feces. We used 20 animals of both sexes, randomly assigned to four treatments and five repetitions totaling 20 experimental units: 1) dry commercial diet (control); 2) control + 0.2%, 3) control + 0.4%, and 4) control + 0.6% of YCW in dry matter. Enterobacteriaceae and lactic acid bacteria, fecal concentration of acetic, propionic and butyric acids, ammonia nitrogen and sensory panel were performed. There were no significant differences (p> 0.05) for bacterial counts and the concentration of SCFA and ammonia, but in sensory panel a reduction in the odor of feces could be noted with the use of 0.2% of YCW. We concluded that the addition of up to 0.6% YCW had no effect on the microbiology and the concentration of fatty acids, but there is potential for its use as an additive because of the improvement in the odor of feces. However, further studies are needed to understand the mechanisms of action and the effects of prebiotics for domestic cats.

Speeding up optimum-path forest training by path-cost propagation

In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training. © 2012 ICPR Org Committee.

Substituição dos grãos secos de milho pela silagem de grãos úmidos de milho para vacas da raça holandesa em lactação

In order to evaluate the effects of replacement of dried corn (GSM) for silage moisture corn (HMCS) on production and milk composition were used five Holstein cows, primiparous with a mean of 112 days post delivery, confined in Tie Stall, for 70 days. We used five diets according to NRC to 17.5% CP (DM) and 2.4 Mcal / kg DM, the 40:60 ratio of roughage and concentrate containing soybean meal, sugar cane silage and hay and substitution levels of the GSM HMCS the following treatments: a) 0%, 2) 25%, 3) 50%, 4) 75% and 5) 100%. Milk production and dry matter intake (DMI) were recorded daily. The animals were milked daily 6:00 and 18:00 h, and milk samples collected from consecutive milkings of each experimental period of 14 days (four days of collection). The experimental design was a 5x5 Latin square and the data analyzed by the statistical program SAS. Body weight (508 kg), milk (23.6 kg), corrected milk (22.7 kg), DMI (17.13 kg) showed no significant difference, but the intakes of neutral detergent fiber (6.67 kg), and detergent acid (3.39 kg), feed efficiency for the production of milk (milk 1.41 kg / day) urea nitrogen (17.67 mg / dL) differ, thus indicating that HMCS is more efficient than GSM in the diet of dairy cows not alter the production and milk composition.

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Inoculação de sementes com bactéria diazotrófica e aplicação de nitrogênio em cobertura e foliar em milho

Considering the importance of nitrogen management and its biological fixation with diazotrophic bacteria, this study was carried out aiming to evaluate the agronomic performance of maize, in response to seed inoculation with Azospirillum brasilense and nitrogen application in side-dressing and leaf. The experiment was conducted in Selvíria, Mato Grosso of Sul State, Brazil, during the growing season 2010/2011, on a clayey Rhodic Haplustox (20° 20' S and 51° 24' W, with altitude of 340 m). Sixteen treatments were established with four replications, in randomized blocks with the combination of the factors A. brasilense (with and without inoculante), nitrogen rate (0 and 90 kg ha-1, in V5 growth stage) and urea leaf application (0, 4, 8 and 12%: application in V5 and V8 growth stage). The maize hybrid used was the DKB 390 YG®, sowed in the row spacing of 0.9 m. Parameters measured were productive and morphological components of culture and crop yield. Increase in maize yield by seed inoculation with A. brasilense was observed. The application of 90 kg ha -1 of nitrogen in side-dressing provided higher chlorophyll leaf index, stalk diameter and prolificacy, however, the yield not was increased. The application of urea leaf did not agronomic efficiency and, therefore, should not be used as the unique form of supply and alternative to nitrogen addition to crop.

Indirect measurement of photosynthetic pigments in the leaves of Jatropha curcas

The aim of this work was to generate mathematical models capable of identifying photosynthetic pigments and soluble proteins from the leaves of Jatropha curcas using the relationship between classical readings performed by spectrophotometry and the chlorophyll meter, ClorofiLOG ® 1030. The work was conducted at Embrapa Cotton, in the city of Campina Grande, state of Paraíba, Brazil. For indirect analysis, portable equipment was used to read leaf discs at different stages of development. The chlorophyll in these discs was then determined using a classical method, while the Bradford method was used to determine soluble proteins. The data were subjected to analysis of variance and regression analyses, in which the readings obtained using the portable chlorophyll meter were the dependent variables and the photosynthetic pigments and soluble protein determined by the classical method the independents variables. The results indicated that with the exception of chlorophyll b and soluble protein, the mathematical models obtained with the portable chlorophyll ClorofiLOG ® 1030 can be used to estimate the concentration of photosynthetic pigments with high precision, thus saving time and the chemical reagents required for conventional procedures.

Parasitism level by helminths and weight gain of calves kept in organic and conventional grazing

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Dairy goat health management and milk production on organic and conventional system in Brazil

The aimed of this article is to measure risk factors on health and milk production on organic and conventional dairy goats in Brazil. Two experimental groups (organic and conventional) were evaluated simultaneously. The study design was completely randomized. The organic herd consisted of 25 goats and 15 kids. In the conventional production system, a dairy herd comprising 40 goats and 20 kids participated in the study. Data on milk production and health management were available from January 2007 to December 2009. The abortion rate in the conventional system was 5% (2/40) whereas in organic system no abortion was diagnosed (0/25). The mortality rate at weaning in the conventional system was 5% (2/40) and in the organic system was 8% (2/25). Milk production was lower (2.20 kg/day) in organic than conventional system (2.66 kg/day). Goats and kids in organic farm had a higher FEC (386±104 and 900±204, respectively) (p<0.05) than those in conventional farm (245±132 and 634±212, respectively). In addition, Saanen kids had higher FEC (p<0.001) than goats. Treatment with antiparasitic drugs was higher in conventional system (50%) than organic system (1.3%).

Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals

Secondary phases such as Laves and carbides are formed during the final solidification stages of nickel based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ″ and δ phases. This work presents a new application and evaluation of artificial intelligent techniques to classify (the background echo and backscattered) ultrasound signals in order to characterize the microstructure of a Ni-based alloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasound signals were acquired using transducers with frequencies of 4 and 5 MHz. Thus with the use of features extraction techniques, i.e.; detrended fluctuation analysis and the Hurst method, the accuracy and speed in the classification of the secondary phases from ultrasound signals could be studied. The classifiers under study were the recent optimum-path forest (OPF) and the more traditional support vector machines and Bayesian. The experimental results revealed that the OPF classifier was the fastest and most reliable. In addition, the OPF classifier revealed to be a valid and adequate tool for microstructure characterization through ultrasound signals classification due to its speed, sensitivity, accuracy and reliability. © 2013 Elsevier B.V. All rights reserved.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Development and Biotechnological Application of a Novel Endoxylanase Family GH10 Identified from Sugarcane Soil Metagenome

Metagenomics has been widely employed for discovery of new enzymes and pathways to conversion of lignocellulosic biomass to fuels and chemicals. In this context, the present study reports the isolation, recombinant expression, biochemical and structural characterization of a novel endoxylanase family GH10 (SCXyl) identified from sugarcane soil metagenome. The recombinant SCXyl was highly active against xylan from beechwood and showed optimal enzyme activity at pH 6,0 and 45°C. The crystal structure was solved at 2.75 Å resolution, revealing the classical (β/α)8-barrel fold with a conserved active-site pocket and an inherent flexibility of the Trp281-Arg291 loop that can adopt distinct conformational states depending on substrate binding. The capillary electrophoresis analysis of degradation products evidenced that the enzyme displays unusual capacity to degrade small xylooligosaccharides, such as xylotriose, which is consistent to the hydrophobic contacts at the +1 subsite and low-binding energies of subsites that are distant from the site of hydrolysis. The main reaction products from xylan polymers and phosphoric acid-pretreated sugarcane bagasse (PASB) were xylooligosaccharides, but, after a longer incubation time, xylobiose and xylose were also formed. Moreover, the use of SCXyl as pre-treatment step of PASB, prior to the addition of commercial cellulolytic cocktail, significantly enhanced the saccharification process. All these characteristics demonstrate the advantageous application of this enzyme in several biotechnological processes in food and feed industry and also in the enzymatic pretreatment of biomass for feedstock and ethanol production. © 2013 Alvarez et al.