Minimization of lead and copper interferences on spectrophotometric determination of cadmium using electrolytic deposition and ion-exchange in multi-commutation flow system

A new strategy for minimization of Cu2+ and Pb2+ interferences on the spectrophotometric determination of Cd2+ by the Malachite green (MG)-iodide reaction using electrolytic deposition of interfering species and solid phase extraction of Cd2+ in flow system is proposed. The electrolytic cell comprises two coiled Pt electrodes concentrically assembled. When the sample solution is electrolyzed in a mixed solution containing 5% (v/v) HNO3, 0.1% (v/v) H2SO4 and 0.5 M NaCl, Cu2+ is deposited as Cu on the cathode, Pb2+ is deposited as PbO2 on the anode while Cd2+ is kept in solution. After electrolysis, the remaining solution passes through an AG1-X8 resin (chloride form) packed minicolumn in which Cd2+ is extracted as CdCl4/2-. Electrolyte compositions, flow rates, timing, applied current, and electrolysis time was investigated. With 60 s electrolysis time, 0.25 A applied current, Pb2+ and Cu2+ levels up to 50 and 250 mg 1-1, respectively, can be tolerated without interference. For 90 s resin loading time, a linear relationship between absorbance and analyte concentration in the 5.00-50.0 μg Cd 1-1 range (r2 = 0.9996) is obtained. A throughput of 20 samples per h is achieved, corresponding to about 0.7 mg MG and 500 mg KI and 5 ml sample consumed per determination. The detection limit is 0.23 μg Cd 1-1. The accuracy was checked for cadmium determination in standard reference materials, vegetables and tap water. Results were in agreement with certified values of standard reference materials and with those obtained by graphite furnace atomic absorption spectrometry at 95% confidence level. The R.S.D. for plant digests and water containing 13.0 μg Cd 1-1 was 3.85% (n = 12). The recoveries of analyte spikes added to the water and vegetable samples ranged from 94 to 104%. (C) 2000 Elsevier Science B.V.

Cytogenetic and random amplified polymorphic DNA analysis of Leptodactylus species from rural and urban environments (Anura, Amphibia)

Cytogenetic and random amplified polymorphic DNA analyses carried out in the species Leptodactylus podicipinus, L. ocellatus, L. labyrinthicus, and L. fuscus from rural and urban habitats of the northwest region of São Paulo State, Brazil, showed that the karyotypes (2n = 22), constitutive heterochromatin distribution and nucleolus organizer region (NOR) location did not differ between the populations from the two environments. The in situ hybridization with an rDNA probe confirmed the location of the NORs on chromosome 8 revealing an in tandem duplication of that region in one of the chromosomes of L. fuscus. DAPI showed that part of the C-band-positive heterochromatin is rich in AT, including that in the proximity the NORs in L. podicipinus and L. ocellatus. The molecular analyses showed that the two populations (urban and rural) of L. podicipinus and L. fuscus are similar from a genetic point of view. The urban and rural populations of species L. ocellatus and L. labyrinthicus showed differences in genetic structures, probably due to urbanization which interferes with the dispersion of those frogs. The marked differences observed between the two populations of L. ocellatus can be representing the cryptic condition of the species. Unweighted pair-group method of analysis and genetic distance analysis detected the genetic proximity between L. ocellatus and L. fuscus. The results indicate that there was no reduction in the genetic diversity in the populations from the urban environment; however, the survival of these frogs would not be guaranteed in the case of an increase in human impact especially for populations of L. labyrinthicus and L. ocellatus. ©FUNPEC-RP.

Multi-trait and random regression mature weight heritability and breeding value estimates in Nelore cattle

Mature weight breeding values were estimated using a multi-trait animal model (MM) and a random regression animal model (RRM). Data consisted of 82 064 weight records from 8 145 animals, recorded from birth to eight years of age. Weights at standard ages were considered in the MM. All models included contemporary groups as fixed effects, and age of dam (linear and quadratic effects) and animal age as covariates. In the RRM, mean trends were modelled through a cubic regression on orthogonal polynomials of animal age and genetic maternal and direct and maternal permanent environmental effects were also included as random. Legendre polynomials of orders 4, 3, 6 and 3 were used for animal and maternal genetic and permanent environmental effects, respectively, considering five classes of residual variances. Mature weight (five years) direct heritability estimates were 0.35 (MM) and 0.38 (RRM). Rank correlation between sires' breeding values estimated by MM and RRM was 0.82. However, selecting the top 2% (12) or 10% (62) of the young sires based on the MM predicted breeding values, respectively 71% and 80% of the same sires would be selected if RRM estimates were used instead. The RRM modelled the changes in the (co)variances with age adequately and larger breeding value accuracies can be expected using this model. © South African Society for Animal Science.

Upconversion ultraviolet random lasing in Nd 3+ doped fluoroindate glass powder

An upconversion random laser (RL) operating in the ultraviolet is reported for Nd 3+ doped fluoroindate glass powder pumped at 575 nm. The RL is obtained by the resonant excitation of the Nd 3+ state 2G 7/2 followed by energy transfer among two excited ions such that one ion in the pair decays to a lower energy state and the other is promoted to state 4D 7/2 from where it decays emitting light at 381 nm. The RL threshold of 30 kW/cm 2 was determined by monitoring the photoluminescence intensity as a function of the pump laser intensity. The RL pulses have time duration of 29 ns that is 50 times smaller than the decay time of the upconversion signal when the sample is pumped with intensities below the RL laser threshold. © 2011 Optical Society of America.

A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and Bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. © 2013 American Dairy Science Association.

Comparison of random regression models to estimate genetic parameters for milk production in Guzerat (Bos indicus) cows

Random regression models have been widely used to estimate genetic parameters that influence milk production in Bos taurus breeds, and more recently in B. indicus breeds. With the aim of finding appropriate random regression model to analyze milk yield, different parametric functions were compared, applied to 20,524 test-day milk yield records of 2816 first-lactation Guzerat (B. indicus) cows in Brazilian herds. The records were analyzed by random regression models whose random effects were additive genetic, permanent environmental and residual, and whose fixed effects were contemporary group, the covariable cow age at calving (linear and quadratic effects), and the herd lactation curve. The additive genetic and permanent environmental effects were modeled by the Wilmink function, a modified Wilmink function (with the second term divided by 100), a function that combined third-order Legendre polynomials with the last term of the Wilmink function, and the Ali and Schaeffer function. The residual variances were modeled by means of 1, 4, 6, or 10 heterogeneous classes, with the exception of the last term of the Wilmink function, for which there were 1, from 0.20 to 0.33. Genetic correlations between adjacent records were high values (0.83-0.99), but they declined when the interval between the test-day records increased, and were negative between the first and last records. The model employing the Ali and Schaeffer function with six residual variance classes was the most suitable for fitting the data. © FUNPEC-RP.

Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach

Background: Meat quality involves many traits, such as marbling, tenderness, juiciness, and backfat thickness, all of which require attention from livestock producers. Backfat thickness improvement by means of traditional selection techniques in Canchim beef cattle has been challenging due to its low heritability, and it is measured late in an animal's life. Therefore, the implementation of new methodologies for identification of single nucleotide polymorphisms (SNPs) linked to backfat thickness are an important strategy for genetic improvement of carcass and meat quality.Results: The set of SNPs identified by the random forest approach explained as much as 50% of the deregressed estimated breeding value (dEBV) variance associated with backfat thickness, and a small set of 5 SNPs were able to explain 34% of the dEBV for backfat thickness. Several quantitative trait loci (QTL) for fat-related traits were found in the surrounding areas of the SNPs, as well as many genes with roles in lipid metabolism.Conclusions: These results provided a better understanding of the backfat deposition and regulation pathways, and can be considered a starting point for future implementation of a genomic selection program for backfat thickness in Canchim beef cattle. © 2013 Mokry et al.; licensee BioMed Central Ltd.

Genetic evaluation using multi-trait and random regression models in Simmental beef cattle

The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajectory of the animals. The (co)variance components were generally similar in MTM and RRM. Direct heritabilities of MTM were slightly lower than RRM and varied from 0.04 to 0.42 and 0.16 to 0.45, respectively. Additive direct correlations were mostly positive and of high magnitude, being highest at closest ages. Considering the results and that pre-adjustment of the weights to standard ages is not required, RRM is recommended for genetic evaluation of Simmental beef cattle in Brazil. ©FUNPEC-RP.

Simulated annealing for building roof contours identification from lidar data

This paper proposes a method by simulated annealing for building roof contours identification from LiDAR-derived digital elevation model. Our method is based on the concept of first extracting aboveground objects and then identifying those objects that are building roof contours. First, to detect aboveground objects (buildings, trees, etc.), the digital elevation model is segmented through a recursive splitting technique followed by a region merging process. Vectorization and polygonization are used to obtain polyline representations of the detected aboveground objects. Second, building roof contours are identified from among the aboveground objects by optimizing a Markov-random-field-based energy function that embodies roof contour attributes and spatial constraints. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing algorithm. Experiments carried out with laser scanning digital elevation model showed that the methodology works properly, as it provides roof contour information with approximately 90% shape accuracy and no verified false positives.

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. © 2013 American Dairy Science Association.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

Random laser action from flexible biocellulose-based device

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Time-resolved random laser spectroscopy of inhomogeneously broadened systems

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)