965 resultados para Selection index
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CETA works--for business : a selection of employment and training programs with the private sector /
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Drought is a major constraint for rice production in the rainfed lowlands in Southeast Asia and Eastern India. The breeding programs for tainted lowland rice in these regions focus on adaptation to a range of drought conditions. However, a method of selection of drought tolerant genotypes has not been established and is considered to be one of the constraints faced by rice breeders. Drought response index (DRI) is based on grain yield adjusted for variation in potential yield and flowering date, and has been used recently, but its consistency among drought environments and hence its usefulness is not certain. In order to establish a selection method and subsequently to identify donor parents for drought resistance breeding, a series of experiments with 15 contrasting genotypes was conducted under well-watered and managed drought conditions at two sites for 5 years in Cambodia. Water level in the field was recorded and used to estimate the relative water level (WLREL) around flowering as an index of the severity of water deficit at the time of flowering for each entry. This was used to determine if DRI or yield reduction was due to drought tolerance or related to the amount of available water at flowering, i.e. drought escape. Grain yield reduction due to drought ranged from 12 to 46%. The drought occurred mainly during the reproductive phase, while four experiments had water stress from the early vegetative stage. There was significant variation for water availability around flowering among the nine experiments and this was associated with variation in mean yield reduction. Genotypic variation in DRI was consistent among most experiments, and genotypic mean DRI ranged from -0.54 to 0.47 (LSD 5% = 0.47). Genotypic variation in DRI was not related to WLREL around flowering in the nine environments. It is concluded that selection for DRI under drought conditions would allow breeders to identify donor lines with high drought tolerance as an important component of breeding better adapted varieties for the rainfed lowlands; two genotypes were identified with high DRI and low yield reduction and were subsequently used in the breeding program in Cambodia. (c) 2006 Elsevier B.V. All rights reserved.
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Genetic parameters for performance traits in a pig population were estimated using a multi-trait derivative-free REML algorithm. The 2590 total data included 922 restrictively fed male and 1668 ad libitum fed female records. Estimates of heritability (standard error in parentheses) were 0.25 (0.03), 0.15 (0.03), and 0.30 (0.05) for lifetime daily gain, test daily gain, and P2-fat depth in males, respectively; and 0.27 (0.04) and 0.38 (0.05) for average daily gain and P2-fat depth in females, respectively. The genetic correlation between P2-fat depth and test daily gain in males was -0.17 (0.06) and between P2-fat and lifetime average daily gain in females 0.44 (0.09). Genetic correlations between sexes were 0.71 (0.11) for average daily gain and -0.30 (0.10) for P2-fat depth. Genetic response per standard deviation of selection on an index combining all traits was predicted at $AU120 per sow per year. Responses in daily gain and backfat were expected to be higher when using only male selection than when using only female selection. Selection for growth rate in males will improve growth rate and carcass leanness simultaneously.
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The research leading to these results has received funding from the European Union's Seventh Framework Programme (KBBE.2013.1.2-10) under grant agreement n° 613611 FISHBOOST. Moreover, the original data collection was supported by the European Union, Project PROGRESS Q5RS-2001-00994. The staff at Tervo station, Ossi Ritola and Tuija Paananen, are highly acknowledged for fish management. A. Ka., A. Ki., S. M., D. H. and K. R. designed research and wrote the paper; A.Ka analyzed the data and had primary responsibility for the final content. All authors have read and approved the manuscript. The authors declare no conflicts of interest.
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Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space), and the challenge arise in defining an algorithm with low communication, theoretical guarantees and excellent practical performance in general settings. For sample space partitioning, I propose a MEdian Selection Subset AGgregation Estimator ({\em message}) algorithm for solving these issues. The algorithm applies feature selection in parallel for each subset using regularized regression or Bayesian variable selection method, calculates the `median' feature inclusion index, estimates coefficients for the selected features in parallel for each subset, and then averages these estimates. The algorithm is simple, involves very minimal communication, scales efficiently in sample size, and has theoretical guarantees. I provide extensive experiments to show excellent performance in feature selection, estimation, prediction, and computation time relative to usual competitors.
While sample space partitioning is useful in handling datasets with large sample size, feature space partitioning is more effective when the data dimension is high. Existing methods for partitioning features, however, are either vulnerable to high correlations or inefficient in reducing the model dimension. In the thesis, I propose a new embarrassingly parallel framework named {\em DECO} for distributed variable selection and parameter estimation. In {\em DECO}, variables are first partitioned and allocated to m distributed workers. The decorrelated subset data within each worker are then fitted via any algorithm designed for high-dimensional problems. We show that by incorporating the decorrelation step, DECO can achieve consistent variable selection and parameter estimation on each subset with (almost) no assumptions. In addition, the convergence rate is nearly minimax optimal for both sparse and weakly sparse models and does NOT depend on the partition number m. Extensive numerical experiments are provided to illustrate the performance of the new framework.
For datasets with both large sample sizes and high dimensionality, I propose a new "divided-and-conquer" framework {\em DEME} (DECO-message) by leveraging both the {\em DECO} and the {\em message} algorithm. The new framework first partitions the dataset in the sample space into row cubes using {\em message} and then partition the feature space of the cubes using {\em DECO}. This procedure is equivalent to partitioning the original data matrix into multiple small blocks, each with a feasible size that can be stored and fitted in a computer in parallel. The results are then synthezied via the {\em DECO} and {\em message} algorithm in a reverse order to produce the final output. The whole framework is extremely scalable.
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This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.
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Different selection objectives within the Quarter Horse breed led to the formation of groups with distinct skills, including the racing and cutting lines. With a smaller population size in Brazil, but of great economic representativeness, the racing line is characterized by animals that can reach high speeds over short distances and within a short period of time. The cutting line is destined for functional tests, exploring skills such as agility and obedience. Although the athletic performance of horses is likely to be influenced by a large number of genes, few genetic variants have so far been related to this trait and this was done exclusively in Thoroughbreds, including the g.38973231G>A singlenucleotide polymorphism in the PDK4 gene and the g.22684390C>T single-nucleotide polymorphism in the COX4I2 gene. The results of the present study demonstrate the presence of polymorphic PDK4 and COX4I2 genes in Quarter Horses. The analysis of 296 racing animals and 68 cutting animals revealed significant differences in allele and genotype frequencies between the two lines. The same was not observed when these frequencies were compared between extreme racing performance phenotypes. There were also no significant associations between alleles of the two polymorphisms and the speed index. These results suggest that the alleles of the PDK4 and COX4I2 genes, which are related to better racecourse performance in Thoroughbreds, are probably associated with beneficial adaptations in aerobic metabolism and therefore play secondary roles in sprint racing performance in Quarter Horses, which is mainly anaerobic.
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An experiment was conducted to study the effect of eggs shape index on embryo mortalities, unhatched egg and day old duck abnormalities of selected and control Tegal duck. A total of 1428 fertile eggs obtained from a selected duck group and a control group were divided in to 3 group according to their eggs shape index (ESI), i.e. small (ESI ,79 percent), medium ( ESI ,82 percent ) and large (ESI ,85 percent ). The ESI was measured as a ratio of wide and length of eggs and percentage. Three batches of incubation with 7 day interval were carried out as replicates. Parameter measured were embryo mortalities, number of unhatched eggs and number of abnormal day old duck. Candling of eggs were performed at 6, 14 and days of incubation and mortalities of embryo were detected by loup. Results of the experiment showed that lowest embryos mortalities was occurred on medium eggs (24 percent) and significantly ( p< 0,05 ) affected by eggs size. Abnormal DOD from medium eggs was significantly (p ,0,05 ) lower (13 percent ) than the small (23 percent ) and large (21 percent ) eggs. Similar trends on eggs from selected duck and control ducks. This may be due to short term of selection program and young age of the female duck (24 weeks ). These results conclude that medium eggs (ESI ,82 percent) were the best size for hatching eggs. (Animal Production 2(1): 25-32 (2000)Key word : duck , hatching eggs, selection, eggs shape index