Surpassing the Line: Urban-Oriented Strategies in the Development of Business Complexes in Poland

Development trends regarding the business-related urban complexes seem to evolve from the "big-box" towards the more "multi-use" types of structures. Within it, the special role is reserved for places, which - due to economic, political, and geographical reasons - have not been previously considered as major business hubs. Only recently, places like cities in Central and Eastern Europe have become attractive locations for business complexes. These could offer centrally located and attractive locations for new structures, which resulted in the development of the new type of commercial centers - in the form of multi-use districts, walkable, and complemented by other uses. Therefore, to some extent, these cities "surpassed the development line" of the commercial and business complexes, and have become home to something much more advanced. Within the article, the cases from Poland, including Gdańsk, Warsaw, Cracow, and Wroclaw, are discussed. Not only is the urban arrangement of selected complexes presented, but the planning and socio-economic, legal, and infrastructural aspects of these developments are also discussed

Extension of the bayesian alphabet for genomic selection

<p>Abstract</p> <p>Background</p> <p>Two Bayesian methods, BayesC<it>π </it>and BayesD<it>π</it>, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability <it>π </it>that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.</p> <p>Results</p> <p>Estimates of <it>π </it>from BayesC<it>π</it>, in contrast to BayesD<it>π</it>, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesC<it>π </it>than for BayesD<it>π</it>, and longest for our implementation of BayesA.</p> <p>Conclusions</p> <p>Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesC<it>π </it>has merit for routine applications.</p

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

<p>Abstract</p> <p>Background</p> <p>The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.</p> <p>Methods</p> <p>The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.</p> <p>Results</p> <p>Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.</p

Genomic Prediction and QTL Mapping Using Bayesian Methods

Several genomic selection methods were applied to a data set that was simulated for the 2010 QTLMAS workshop to predict the genomic breeding values (GEBV) of the offspring generation and to map the QTL. The GEBV had an accuracy of 0.894 with very small bias. QTL were detected based on the variance of 10 SNP windows. Using a threshold chosen for a 10% chromosome-wise type-I error rate, most of the large QTL were successfully detected with few false positives. Results for both prediction of breeding values and detection of QTL were among the best among all analyses of this data set by groups across the globe. Genomic selection method BayesCπ was identified to be appropriate for the 2010 QTLMAS dataset and also applicable to real cases with similar settings

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model

<p>Abstract</p> <p>Background</p> <p>Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line.</p> <p>Methods</p> <p>The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).</p> <p>The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV.</p> <p>Results</p> <p>Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.</p

The value of expanding the training population to improve genomic selection models in tetraploid potato

<p>Genomic selection (GS) is becoming increasingly applicable to crops as the genotyping costs continue to decrease, which makes it an attractive alternative to traditional selective breeding based on observed phenotypes. With genome-wide molecular markers, selection based on predictions from genotypes can be made in the absence of direct phenotyping. The reliability of predictions depends strongly on the number of individuals used for training the predictive algorithms, particularly in a highly genetically diverse organism such as potatoes; however, the relationship between the individuals also has an enormous impact on prediction accuracy. Here we have studied genomic prediction in three different panels of potato cultivars, varying in size, design, and phenotypic profile. We have developed genomic prediction models for two important agronomic traits of potato, dry matter content and chipping quality. We used genotyping-by-sequencing to genotype 1,146 individuals and generated genomic prediction models from 167,637 markers to calculate genomic estimated breeding values with genomic best linear unbiased prediction. Cross-validated prediction correlations of 0.75–0.83 and 0.39–0.79 were obtained for dry matter content and chipping quality, respectively, when combining the three populations. These prediction accuracies were similar to those obtained when predicting performance within each panel. In contrast, but not unexpectedly, predictions across populations were generally lower, 0.37–0.71 and 0.28–0.48 for dry matter content and chipping quality, respectively. These predictions are not limited by the number of markers included, since similar prediction accuracies could be obtained when using merely 7,800 markers (<5%). Our results suggest that predictions across breeding populations in tetraploid potato are presently unreliable, but that individual prediction models within populations can be combined in an additive fashion to obtain high quality prediction models relevant for several breeding populations.</p

Validation and Further Characterization of a Major Quantitative Trait Locus Associated with Host Response to Experimental Infection with Porcine Reproductive and Respiratory Syndrome Virus

Infectious diseases are costly to the swine industry; porcine reproductive and respiratory syndrome (PRRS) is the most devastating. In earlier work, a quantitative trait locus associated with resistance/susceptibility to PRRS virus was identified on Sus scrofa chromosome 4 using approximately 560 experimentally infected animals from a commercial cross. The favorable genotype was associated with decreased virus load and increased weight gain (WG). The objective here was to validate and further characterize the association of the chromosome 4 region with PRRS resistance using data from two unrelated commercial crossbred populations. The validation populations consisted of two trials each of approximately 200 pigs sourced from different breeding companies that were infected with PRRS virus and followed for 42 days post-infection. Across all five trials, heritability estimates were 0.39 and 0.34 for viral load (VL; area under the curve of log-transformed viremia from 0 to 21 days post-infection) and WG to 42 days post-infection respectively. Effect estimates of SNP WUR10000125 in the chromosome 4 region were in the same directions and of similar magnitudes in the two new trials as had been observed in the first three trials. Across all five trials, the 1-Mb region on chromosome 4 explained 15 percent of genetic variance for VL and 11 percent for WG. The effect of the favorable minor allele at SNP WUR10000125 was dominant. Ordered genotypes for SNP WUR10000125 showed that the effect was present irrespective of whether the favorable allele was paternally or maternally inherited. These results demonstrate that selection for host response to PRRS virus infection could reduce the economic impact of PRRS

Accuracy of Genomic EBV Using an Evenly Spaced, Low-density SNP Panel in Broiler Chickens

Whole-genome or genomic selection is based on associations of large number of markers across the genome with phenotype but will require use of small SNP panels to be cost effective in chickens. The potential loss of accuracy of genotyping selection candidates with an evenly-spaced low-density marker panel and imputation of high-density SNP genotypes was evaluated in a commercial broiler chicken line. Several methods were used to estimate marker effects. The loss in accuracy was less than 5% for different methods and traits. Thus, genomic selection using evenly-spaced low-density marker panels is a cost-effective choice for implementation of genomic selection