Development and application of whole-chromosome painting of chromosomes 7A and 8A of Arachis duranensis based on chromosome-specific single-copy oligonucleotides.

For peanut, the lack of stable cytological markers is a barrier to tracking specific chromosomes, elucidating the genetic relationships between genomes and identifying chromosomal variations. Chromosome mapping using single-copy oligonucleotide (oligo) probe libraries has unique advantages for identifying homologous chromosomes and chromosomal rearrangements. In this study, we developed two whole-chromosome single-copy oligo probe libraries, LS-7A and LS-8A, based on the reference genome sequences of chromosomes 7A and 8A of Arachis duranensis. Fluorescence in situ hybridization (FISH) analysis confirmed that the libraries could specifically paint chromosomes 7 and 8. In addition, sequential FISH and electronic localization of LS-7A and LS-8A in A. duranensis (AA) and A. ipaensis (BB) showed that chromosomes 7A and 8A contained translocations and inversions relative to chromosomes 7B and 8B. Analysis of the chromosomes of wild Arachis species using LS-8A confirmed that this library could accurately and effectively identify A genome species. Finally, LS-7A and LS-8A were used to paint the chromosomes of interspecific hybrids and their progenies, which verified the authenticity of the interspecific hybrids and identified a disomic addition line. This study provides a model for developing specific oligo probes to identify the structural variations of other chromosomes in Arachis and demonstrates the practical utility of LS-7A and LS-8A.

A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.