Effect of within-litter birth weight variation on piglet survival and pre-weaning weight gain in a commercial herd

There are several factors that affect piglet survival and this has a bearing on sow productivity. Ten variables that influence pre-weaning vitality were analysed using records from the Pig Industry Board, Zimbabwe. These included individual piglet birth weight, piglet origin (nursed in original litter or fostered), sex, relative birth weight expressed as standard deviation units, sow parity, total number of piglets born, year and month of farrowing, within-litter variability and the presence of stillborn or mummified littermates. The main factors that influenced piglet mortality were fostering, parity and within-litter variability especially the weight of the individual piglet relative to the average of the litter (P<0.05). Presence of a mummified or stillborn littermate, which could be a proxy for unfavourable uterine environment or trauma during the birth process, did not influence pre-weaning mortality. Variability within a litter and the deviation of the weight of an individual piglet from the litter mean, influenced survival to weaning. It is, therefore, advisable for breeders to include uniformity within the litter as a selection criterion. The recording of various variables by farmers seems to be a useful management practice to identify piglets at risk so as to establish palliative measures. Further, farmers should know which litters and which piglets within a litter are at risk and require more attention.

Longitud cervical como predictor de exito en induccion de trabajo de parto en nuliparas

La inducción del trabajo de parto ha demostrado aumentar simultáneamente las tasas de cesárea, especialmente en nulíparas con cérvix clínicamente desfavorables. Ya que la valoración clínica del cérvix es un método subjetivo, aunque ampliamente utilizado, el objetivo del presente estudio fue determinar la utilidad de la medición ecográfica de la longitud cervical comparándola con el puntaje de Bishop, en la predicción del éxito de la inducción del parto en las pacientes nulíparas en el servicio de Obstetricia del Hospital Universitario Clínica San Rafael, Bogotá. Materiales y métodos: Se realizó un estudio observacional, evaluando una cohorte prospectiva de 80 gestantes a quienes se les realizó valoración ultrasonográfica y clínica del cérvix antes de iniciar la inducción del trabajo de parto. Resultados: El análisis bivariado demostró que las pacientes con longitud cervical >20mm tienen 1.57 veces la probabilidad de tener parto por cesárea (RR 1.57 IC95% 1.03-2.39 p <0.05). De manera similar las pacientes con puntaje de Bishop 0 a 3 tienen 2.33 veces la probabilidad de tener parto por cesárea (RR 2.33 IC95% 1.28-4.23 p <0.05). La regresión logística binaria demostró que la edad materna y la longitud cervical fueron los únicos parámetros independientes con significancia estadística para predecir el éxito de la inducción. Conclusiones: La medición ecográfica de la longitud cervical tiene mayor utilidad que la valoración clínica del cérvix en la predicción del éxito de la inducción del parto en nulíparas.

Modelling European winter wind storm losses in current and future climate

Severe wind storms are one of the major natural hazards in the extratropics and inflict substantial economic damages and even casualties. Insured storm-related losses depend on (i) the frequency, nature and dynamics of storms, (ii) the vulnerability of the values at risk, (iii) the geographical distribution of these values, and (iv) the particular conditions of the risk transfer. It is thus of great importance to assess the impact of climate change on future storm losses. To this end, the current study employs—to our knowledge for the first time—a coupled approach, using output from high-resolution regional climate model scenarios for the European sector to drive an operational insurance loss model. An ensemble of coupled climate-damage scenarios is used to provide an estimate of the inherent uncertainties. Output of two state-of-the-art global climate models (HadAM3, ECHAM5) is used for present (1961–1990) and future climates (2071–2100, SRES A2 scenario). These serve as boundary data for two nested regional climate models with a sophisticated gust parametrizations (CLM, CHRM). For validation and calibration purposes, an additional simulation is undertaken with the CHRM driven by the ERA40 reanalysis. The operational insurance model (Swiss Re) uses a European-wide damage function, an average vulnerability curve for all risk types, and contains the actual value distribution of a complete European market portfolio. The coupling between climate and damage models is based on daily maxima of 10 m gust winds, and the strategy adopted consists of three main steps: (i) development and application of a pragmatic selection criterion to retrieve significant storm events, (ii) generation of a probabilistic event set using a Monte-Carlo approach in the hazard module of the insurance model, and (iii) calibration of the simulated annual expected losses with a historic loss data base. The climate models considered agree regarding an increase in the intensity of extreme storms in a band across central Europe (stretching from southern UK and northern France to Denmark, northern Germany into eastern Europe). This effect increases with event strength, and rare storms show the largest climate change sensitivity, but are also beset with the largest uncertainties. Wind gusts decrease over northern Scandinavia and Southern Europe. Highest intra-ensemble variability is simulated for Ireland, the UK, the Mediterranean, and parts of Eastern Europe. The resulting changes on European-wide losses over the 110-year period are positive for all layers and all model runs considered and amount to 44% (annual expected loss), 23% (10 years loss), 50% (30 years loss), and 104% (100 years loss). There is a disproportionate increase in losses for rare high-impact events. The changes result from increases in both severity and frequency of wind gusts. Considerable geographical variability of the expected losses exists, with Denmark and Germany experiencing the largest loss increases (116% and 114%, respectively). All countries considered except for Ireland (−22%) experience some loss increases. Some ramifications of these results for the socio-economic sector are discussed, and future avenues for research are highlighted. The technique introduced in this study and its application to realistic market portfolios offer exciting prospects for future research on the impact of climate change that is relevant for policy makers, scientists and economists.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology

Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as efficient mixed model analysis (EMMA). These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. To address this concern, we proposed a random-SNP-effect MLM (RMLM) and a multi-locus RMLM (MRMLM) for GWAS. The RMLM simply treats the SNP-effect as random, but it allows a modified Bonferroni correction to be used to calculate the threshold p value for significance tests. The MRMLM is a multi-locus model including markers selected from the RMLM method with a less stringent selection criterion. Due to the multi-locus nature, no multiple test correction is needed. Simulation studies show that the MRMLM is more powerful in QTN detection and more accurate in QTN effect estimation than the RMLM, which in turn is more powerful and accurate than the EMMA. To demonstrate the new methods, we analyzed six flowering time related traits in Arabidopsis thaliana and detected more genes than previous reported using the EMMA. Therefore, the MRMLM provides an alternative for multi-locus GWAS.

Additive genetic relationships between scrotal circumference, heifer pregnancy, and stayability in Nellore cattle

Scrotal circumference data from 47,605 Nellore young bulls, measured at around 18 mo of age (SC18), were analyzed simultaneously with 27,924 heifer pregnancy (HP) and 80,831 stayability (STAY) records to estimate their additive genetic relationships. Additionally, the possibility that economically relevant traits measured directly in females could replace SC18 as a selection criterion was verified. Heifer pregnancy was defined as the observation that a heifer conceived and remained pregnant, which was assessed by rectal palpation at 60 d. Females were exposed to sires for the first time at about 14 mo of age (between 11 and 16 mo). Stayability was defined as whether or not a cow calved every year up to 5 yr of age, when the opportunity to breed was provided. A Bayesian linear-threshold-threshold analysis via Gibbs sampler was used to estimate the variance and covariance components of the multitrait model. Heritability estimates were 0.42 +/- 0.01, 0.53 +/- 0.03, and 0.10 +/- 0.01, for SC18, HP, and STAY, respectively. The genetic correlation estimates were 0.29 +/- 0.05, 0.19 +/- 0.05, and 0.64 +/- 0.07 between SC18 and HP, SC18 and STAY, and HP and STAY, respectively. The residual correlation estimate between HP and STAY was -0.08 +/- 0.03. The heritability values indicate the existence of considerable genetic variance for SC18 and HP traits. However, genetic correlations between SC18 and the female reproductive traits analyzed in the present study can only be considered moderate. The small residual correlation between HP and STAY suggests that environmental effects common to both traits are not major. The large heritability estimate for HP and the high genetic correlation between HP and STAY obtained in the present study confirm that EPD for HP can be used to select bulls for the production of precocious, fertile, and long-lived daughters. Moreover, SC18 could be incorporated in multitrait analysis to improve the prediction accuracy for HP genetic merit of young bulls.

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background: Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods: We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results: Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion: The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring.

O MBA brasileiro na gestão de recursos humanos

Este estudo objetivou analisar de que forma as empresas brasileiras de grande porte utilizamse dos cursos de pós-graduação lato sensu brasileiros na área de Administração de Empresas, como critério de seleção e ferramenta de formação e atualização para executivos, assim como as principais características esperadas de profissionais oriundos destes cursos. Os resultados da pesquisa, realizada junto a profissionais da área de Recursos Humanos das maiores empresas brasileiras, indicaram que a maioria das empresas se utiliza destes cursos como critério de seleção para contratação de profissionais, inclusive sendo a instituição de ensino onde o mesmo foi realizado, diferencial para obtenção da vaga. Da mesma forma, concluiu-se que a maior parte das empresas deste grupo utilizam-se com freqüência destes cursos como ferramenta de atualização e treinamento de profissionais, sendo o curso escolhido geralmente pelo próprio profissional.

Genetic associations between age at first calving and heifer body weight and scrotal circumference in Nelore cattle

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimativa de parâmetros genéticos da habilidade de permanência aos 48, 60 e 72 meses de idade em vacas da raça Caracu

Objetivou-se estimar a herdabilidade da característica habilidade de permanência (HP) em um rebanho de bovinos da raça Caracu visando sua utilização como critério de seleção. A característica em estudo foi definida como a probabilidade de a vaca estar presente no rebanho aos 48 (HP48), aos 60 (HP60) e aos 72 (HP72) meses, desde que possuíssem registros de pelo menos duas lactações nas específicas idades. Observações binárias, com zero (0 = fracasso) e um (1 = sucesso) foram designadas aos animais. Para análise da HP, foram utilizados dados de 5.487, 4.947 e 4.308 animais aos 48, 60 e 72 meses, respectivamente. Os componentes de variância e as herdabilidades foram estimados mediante inferência Bayesiana, via amostragem de Gibbs, pelo programa MTGSAM - threshold, utilizando-se um modelo touro. Foram utilizadas como variáveis explanatórias grupo de contemporâneos, classe de produção de leite na primeira lactação, classe de idade ao primeiro parto e sua interação. As análises forneceram estimativas médias de herdabilidade iguais a 0,28 ± 0,07 para HP48, 0,27 ± 0,07 para HP60 e 0,23 ± 0,07 para HP72. Os resultados evidenciaram que a característica HP apresentou variação aditiva em todas as idades estudadas e, portanto, pode ser empregada como critério de seleção para longevidade produtiva.

Heritabilities and genetic correlations for reproductive traits in an F2 reciprocal cross chicken population

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

Genetic parameters and environmental effects on temperament score and reproductive traits of Nellore cattle

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic associations of sexual precocity with growth traits and visual scores of conformation, finishing, and muscling in Nelore cattle

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