Clonal diversity and genealogical relationships of gibel carp in four hatcheries

To conserve and utilize the genetic pool of gynogenetic gibel carp (Carassius auratus gibelio), the Fangzheng and Qihe stock hatcheries have been established in China. However, little information is available on the amount of genetic variation within and between these populations. In this study, clonal diversity in 101 fish from these two stock hatcheries and 35 fish from two other hatcheries in Wuhan and Pengze respectively was analysed for variation in serum transferrin. Thirteen clones were found in Fangzheng and Qihe, of which 12 were novel. Six clones were specific to Fangzheng and three specific to Qihe, whereas four were shared among the Fangzheng and Qihe fish. To obtain more knowledge on genetic diversity and genealogical relationships within gibel carp, the complete mitochondrial DNA (mtDNA) control region (similar to 920 bp) was sequenced in 64 individuals representing all 14 clones identified in the four hatcheries. Differences in the mtDNA sequences varied remarkably among hatcheries, with the Fangzheng and Qihe lines demonstrating high diversity and Wuhan and Pengze showing no variation. The Fangzheng and Qihe lines might represent two distinct matrilineal sources. One of the Qihe samples carried the haplotype shared by a most widely cultivated Fangzheng clone, indicating that a Fangzheng clone escaped from cultivated ponds and moved into the Qihe hatchery. Four Fangzheng samples clustered within the lineage formed mainly by Qihe samples, most likely reflecting historical gene flow from Qihe to Fangzheng. It is suggested that clones in Wuhan originated from Fangzheng, consistent with their introduction history, supporting the hypothesis that gibel carp in Pengze were domesticated from individuals in the Fangzheng hatchery.

基于克隆选择算法的非线性优化迭代学习控制；Clonal selection algorithm based nonlinear optimal iterative learning control

为实现对模型不确定的有约束非线性系统在特定时间域上输出轨迹的有效跟踪,将改进的克隆选择算法用于求解迭代学习控制中的优化问题。提出基于克隆选择算法的非线性优化迭代学习控制。在每次迭代运算后,一个克隆选择算法用于求解下次迭代运算中的最优输入,另一个克隆选择算法用于修正系统参考模型。仿真结果表明,该方法比GA-ILC具有更快的收敛速度,能够有效处理输入上的约束以及模型不确定问题,通过少数几次迭代学习就能取得满意的跟踪效果。

Computação da Seleção Genômica Ampla (GWS).

Métodos para GWS; Teoria dos métodos de regressão; Computação do método Random (Ridge) Regression BLUP (RR-BLUP/GWS); Fenótipos corrigidos; Frequências alélicas, variância dos marcadores e herdabilidade; Marcadores codominantes (SNP) ? Modelo genotípico; Marcadores dominantes (DArT) - Modelo genotípico; Marcadores codominantes (SNP) ? Modelo gamético ou alélico; Número de marcadores com efeitos significativos; Populações de estimação, validação e seleção; População de validação e Jacknife; Correlação e regressão entre valores genéticos preditos e fenótipos na população de validação; Análise de associação na GWAS; Software Selegen Genômica: Random (Ridge) Regression BLUP: RR-BLUP/GWS; Exemplo aplicado ao melhoramento do eucalipto.