Morphometric evaluation of gingival overgrowth and regression caused by cyclosporin in rats

Cyclosporin A is a selective immunosuppressant, used in organ transplants to prevent graft rejection. Cyclosporin A can cause various side effects including gingival overgrowth. The aim of this work was to evaluate gingival overgrowth of rats treated daily with 10 mg/kg body weight of Cyclosporin A for 60 days, as well as the regression after the interruption of treatment. All rats treated with Cyclosporin A developed gingival overgrowth, with increased thickness of the epithelium, height and width of the connective tissue. The density of fibroblasts and collagen fibers also increased. Five to 90 days after the interruption of treatment with Cyclosporin A, there was a progressive reduction of the gingival volume and of collagen fibers and fibroblast densities. The reduction was more pronounced in the initial periods and after 90 days did not return to the normal values.

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.

Theory of constraints contributions to outbound logistics

Purpose: The purpose of this paper is to systematically describe the key practical contributions of the theory of constraints (TOC) to outbound (distribution) logistics. Design/methodology/approach: Based on theoretical research, this paper presents the main practical aspects of the approach suggested by TOC to outbound logistics and discusses the assumptions upon which it is based. Findings: This paper corroborates the thesis defended by TOC, according to which the current ways of managing outbound logistics, based mainly on sales forecasts lead to difficulties in handling trade-offs between logistics (stock and transportation) costs and stock-out levels. Research limitations/implications: The reported research is of a theoretical nature. Practical implications: TOC offers a proposal that is complementary in many aspects and very distinguishable in others about the way some key processes and elements of supply chain management (SCM) are managed, especially outbound logistics. Originality/value: Considering the dearth of papers dealing with the conceptual articulation and organization of this subject, the paper contributes to systematize the knowledge currently available about the contributions of the TOC to outbound logistics, highlighting the practical implications of applying TOC to outbound logistics. © Emerald Group Publishing Limited.

Preprocessing data for short-term load forecasting with a general regression neural network and a moving average filter

This paper proposes a filter based on a general regression neural network and a moving average filter, for preprocessing half-hourly load data for short-term multinodal load forecasting, discussed in another paper. Tests made with half-hourly load data from nine New Zealand electrical substations demonstrate that this filter is able to handle noise, missing data and abnormal data. © 2011 IEEE.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

Using the Analytic Network Process to evaluate the relation between Reverse Logistics and corporate performance in Brazilian companies

Reverse Logistics activities are practiced by most Brazilian companies. However, a relevant problem is to identify how different Reverse Logistics programs can affect corporate performance indicators. Analytic Network Process is one of the analytical tools, which can be used to handle a multi-criteria decision-making problem and it is the only one that can capture the interdependencies between the criteria under consideration. This method was adopted here to study the influence of Reverse Logistics practices in corporate performance. Preliminary results indicated that the method can be used, reaching a result compatible to the reality of the Brazilian companies.

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.

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.

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.

Computerization of maritime ports, e-commerce platforms and logistics communities

In the past few years there has been a clear trend of attaching increasing importance to "inforstructures", or the capacity of ports to process the information that accompanies foreign trade flows, so that the processing becomes a facilitating factor for trade, rather than an obstacle.This led to development of the concept of a port community system, which is a computerized system that interconnects all the members of a logistics community, making the exchange of documentation as effective as possible, reducing the volume of data to be re-entered in different systems and ultimately improving the whole process of monitoring an operation until its completion. Computerization of communications between all the actors at the ports facilitates integration of the community, while it also assists interaction between ports, thus forming logistics corridors.

Toward an integrated transport policy: institutions, infrastructure and logistics - the case in Chile

This edition of the FAL Bulletin presents a summary of the major outcomes of the workshop, “Toward an integrated transport policy: institutions, infrastructure and logistics”, which was organized by the ECLAC Infrastructure Services Unit, in late 2009. The objective of the event was to analyse the various government bodies involved in the transport sector, Chile’s experience in formulating transport policy and the challenge that formulating and executing integrated policies entails.

Intelligent transport systems in Latin American sea port logistics

This issue of the FAL bulletin analyses the role of intelligent transport systems (ITS) in sea port logistics in Latin America.

Security in the logistics chain and its impact on Mesoamerican competitiveness

This issue of the FAL Bulletin analyses the implications of logistics security for the competitiveness of the member countries of the Mesoamerica Project. This study analyses a number of international indicators related to logistics security and proposes a set of actions to improve the organization of the governments and their coordination with the private sector, to enhance the efficiency of the measures implemented and thus the competitiveness of their economies.