5/Zastosowanie techniki ćwierćwiązek z użyciem jednego izocentrum w radioterapii chorych na raka piersi

Cel pracyCelem pracy jest przedstawienie nowej techniki radioterapii zastosowanej u chorych na raka piersi, po zabiegach operacyjnych.MetodaNapromienianie wszystkich pól przeprowadza się bez zmiany ułożenia chorej, z wykorzystaniem jednego izocentrum, które jest zlokalizowane na granicy pól tangencjalnych i nadobojczykowo-pachowych. Stosuje się pola asymetryczne. Pola tangencjalne napromienia się ćwierćwiązką, a pola nadobojczykowe - półwiązką.Wnioski1. Prezentowana technika radioterapii pozwala na lepszą odtwarzalność leczenia w porównaniu do stosowanych standardowych technik radioterapii. 2. Opisana technika, pomimo większego nakładu pracy przy planowaniu leczenia jest mniej pracochłonna i zajmuje mniej czasu przy codziennym napromienianiu. 3. Poprawie ulega jednorodność rozkładu dawki na granicy obszarów

Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs

Background: Most developments in quantitative genetics theory focus on the study of intra-breed/line concepts. With the availability of massive genomic information, it becomes necessary to revisit the theory for crossbred populations. We propose methods to construct genomic covariances with additive and non-additive (dominance) inheritance in the case of pure lines and crossbred populations. Results: We describe substitution effects and dominant deviations across two pure parental populations and the crossbred population. Gene effects are assumed to be independent of the origin of alleles and allelic frequencies can differ between parental populations. Based on these assumptions, the theoretical variance components (additive and dominant) are obtained as a function of marker effects and allelic frequencies. The additive genetic variance in the crossbred population includes the biological additive and dominant effects of a gene and a covariance term. Dominance variance in the crossbred population is proportional to the product of the heterozygosity coefficients of both parental populations. A genomic BLUP (best linear unbiased prediction) equivalent model is presented. We illustrate this approach by using pig data (two pure lines and their cross, including 8265 phenotyped and genotyped sows). For the total number of piglets born, the dominance variance in the crossbred population represented about 13 % of the total genetic variance. Dominance variation is only marginally important for litter size in the crossbred population. Conclusions: We present a coherent marker-based model that includes purebred and crossbred data and additive and dominant actions. Using this model, it is possible to estimate breeding values, dominant deviations and variance components in a dataset that comprises data on purebred and crossbred individuals. These methods can be exploited to plan assortative mating in pig, maize or other species, in order to generate superior crossbred individuals in terms of performance

The Dimensionality of Genomic Information and Its Effect on Genomic Prediction

The genomic relationship matrix (GRM) can be inverted by the algorithm for proven and young (APY) based on recursion on a random subset of animals. While a regular inverse has a cubic cost, the cost of the APY inverse can be close to linear. Theory for the APY assumes that the optimal size of the subset (maximizing accuracy of genomic predictions) is due to a limited dimensionality of the GRM, which is a function of the effective population size (N(e)). The objective of this study was to evaluate these assumptions by simulation. Six populations were simulated with approximate effective population size (N(e)) from 20 to 200. Each population consisted of 10 nonoverlapping generations, with 25,000 animals per generation and phenotypes available for generations 1–9. The last 3 generations were fully genotyped assuming genome length L = 30. The GRM was constructed for each population and analyzed for distribution of eigenvalues. Genomic estimated breeding values (GEBV) were computed by single-step GBLUP, using either a direct or an APY inverse of GRM. The sizes of the subset in APY were set to the number of the largest eigenvalues explaining x% of variation (EIGx, x = 90, 95, 98, 99) in GRM. Accuracies of GEBV for the last generation with the APY inverse peaked at EIG98 and were slightly lower with EIG95, EIG99, or the direct inverse. Most information in the GRM is contained in ∼N(e)L largest eigenvalues, with no information beyond 4N(e)L. Genomic predictions with the APY inverse of the GRM are more accurate than by the regular inverse

Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: a simulation study.

International audienceAbstractBackgroundThe dimensionality of genomic information is limited by the number of independent chromosome segments (Me), which is a function of the effective population size. This dimensionality can be determined approximately by singular value decomposition of the gene content matrix, by eigenvalue decomposition of the genomic relationship matrix (GRM), or by the number of core animals in the algorithm for proven and young (APY) that maximizes the accuracy of genomic prediction. In the latter, core animals act as proxies to linear combinations of Me. Field studies indicate that a moderate accuracy of genomic selection is achieved with a small dataset, but that further improvement of the accuracy requires much more data. When only one quarter of the optimal number of core animals are used in the APY algorithm, the accuracy of genomic selection is only slightly below the optimal value. This suggests that genomic selection works on clusters of Me.ResultsThe simulation included datasets with different population sizes and amounts of phenotypic information. Computations were done by genomic best linear unbiased prediction (GBLUP) with selected eigenvalues and corresponding eigenvectors of the GRM set to zero. About four eigenvalues in the GRM explained 10% of the genomic variation, and less than 2% of the total eigenvalues explained 50% of the genomic variation. With limited phenotypic information, the accuracy of GBLUP was close to the peak where most of the smallest eigenvalues were set to zero. With a large amount of phenotypic information, accuracy increased as smaller eigenvalues were added.ConclusionsA small amount of phenotypic data is sufficient to estimate only the effects of the largest eigenvalues and the associated eigenvectors that contain a large fraction of the genomic information, and a very large amount of data is required to estimate the remaining eigenvalues that account for a limited amount of genomic information. Core animals in the APY algorithm act as proxies of almost the same number of eigenvalues. By using an eigenvalues-based approach, it was possible to explain why the moderate accuracy of genomic selection based on small datasets only increases slowly as more data are added

Estimation of dominance variance in purebred Yorkshire swine

peer reviewedWe used 179,485 Yorkshire reproductive and 239,354 Yorkshire growth records to estimate additive and dominance variances by Method Fraktur R. Estimates were obtained for number born alive (NBA), 21-d litter weight (LWT), days to 104.5 kg (DAYS), and backfat at 104.5 kg (BF). The single-trait models for NBA and LWT included the fixed effects of contemporary group and regression on inbreeding percentage and the random effects mate within contemporary group, animal permanent environment, animal additive, and parental dominance. The single-trait models for DAYS and BF included the fixed effects of contemporary group, sex, and regression on inbreeding percentage and the random effects litter of birth, dam permanent environment, animal additive, and parental dominance. Final estimates were obtained from six samples for each trait. Regression coefficients for 10% inbreeding were found to be -.23 for NBA, -.52 kg for LWT, 2.1 d for DAYS, and 0 mm for BF. Estimates of additive and dominance variances expressed as a percentage of phenotypic variances were, respectively, 8.8 +/- .5 and 2.2 +/- .7 for NBA, 8.1 +/- 1.1 and 6.3 +/- .9 for LWT, 33.2 +/- .4 and 10.3 +/- 1.5 for DAYS, and 43.6 +/- .9 and 4.8 +/- .7 for BF. The ratio of dominance to additive variances ranged from .78 to .11

On the implications of aerosol liquid water and phase separation for modeled organic aerosol mass

Water is an important component of PM2.5 Many traditional SOA species are highly soluble and thus can be considered extractable Water can influence the partitioning of compounds traditionally considered insoluble in models Organic aerosol takes up water according to RH Organic aerosol interacts with inorganic water Deviations in ideality (solubility) must be considered

Studies on the Value of Incorporating Effect of Dominance in Genetic Evaluations of Dairy Cattle, Beef Cattle, and Swine

Potential gains from including the dominance effect in genetic evaluations include “purification” of additive values and availability of specific combining abilities for each pair of prospective parents. The magnitude of such gains was tested for dairy and beef cattle and for swine by estimating variance components for several traits and by analyzing changes in additive evaluations when the parental dominance effect was added to the model. Estimates of dominance variance for dairy and beef cattle and for swine were up to 10% of phenotypic variance; estimates were larger for growth traits. As a percentage of additive variance, the estimate of dominance variance reached 78% for 21-day litter weight of swine and 47% for postweaning weight of beef cattle. Changes in additive evaluations after considering dominance are largest for dams of a single large family. These changes were found to be important for dairy cattle especially for dams of full-sibs, but less important for swin