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We used the 4C-Seq technique to characterize the genome-wide patterns of spatial contacts of several CpG islands located on chromosome 14 in cultured chicken lymphoid and erythroid cells. We observed a clear tendency for the spatial clustering of CpG islands present on the same and different chromosomes, regardless of the presence or absence of promoters within these CpG islands. Accordingly, we observed preferential spatial contacts between Sp1 binding motifs and other GC-rich genomic elements, including the DNA sequence motifs capable of forming G-quadruplexes. However, an anchor placed in a gene/CpG island-poor area formed spatial contacts with other gene/CpG island-poor areas on chromosome 14 and other chromosomes. These results corroborate the two-compartment model of the spatial organization of interphase chromosomes and suggest that the clustering of CpG islands constitutes an important determinant of the 3D organization of the eukaryotic genome in the cell nucleus. Using the ChIP-Seq technique, we mapped the genome-wide CTCF deposition sites in the chicken lymphoid and erythroid cells that were used for the 4C analysis. We observed a good correlation between the density of CTCF deposition sites and the level of 4C signals for the anchors located in CpG islands but not for an anchor located in a gene desert. It is thus possible that CTCF contributes to the clustering of CpG islands observed in our experiments.

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This report presents a generalized design strategy of intelligent robust control systems based on quantum/soft computing technologies that enhance robustness of fuzzy controllers by supplying a selforganizing capability. It is demonstrated that fuzzy controllers prepared to maintain control object in the prescribed conditions are often fail to control when such a conditions are dramatically changed. We propose the solution of such kind of problems by introducing a generalization of strategies in fuzzy inference from a set of pre-defined fuzzy controllers by new Quantum Fuzzy Inference based systems (prototype of Intelligent System of Systems Engineering). We stress our attention on the robustness features of intelligent control systems.