A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping

Regional remote sensing image products are playing an important role in an increasing number of application fields. Aiming at multi-satellite imaging task planning for large-area image acquisition, this paper proposes a multi-objective modeling method. First, we analyzed the core requirements of regional mapping for multi-satellite imaging mission planning: Full coverage of the target area and low consumption of satellite resources. Second, an optimization model with two objective functions, namely the maximum target area coverage and minimum satellite resource utilization, was established. Using the selection of imaging strips and their swing angles as two types of decision variables, the regional decomposition and satellite resource allocation were integrated into the planning model. Third, two efficient algorithms, Vatti and non-dominated sorting genetic algorithm (NSGA-II), were used for objective function calculation and model solving, respectively. Finally, the experiments used Hubei, Finland, and Congo as the target areas and GF1, GF6, ZY1-02C, and ZY3 as imaging satellites to verify the modeling method proposed in this paper. The experiments showed that the proposed multi-objective modeling method could complete the coverage of regional targets with fewer satellite resources and improve the satellite application efficiency significantly

Whole-exome sequencing identifies OR2W3 mutation as a cause of autosomal dominant retinitis pigmentosa

Retinitis pigmentosa (RP), a heterogeneous group of inherited ocular diseases, is a genetic condition that causes retinal degeneration and eventual vision loss. Though some genes have been identified to be associated with RP, still a large part of the clinical cases could not be explained. Here we reported a four-generation Chinese family with RP, during which 6 from 9 members of the second generation affected the disease. To identify the genetic defect in this family, whole-exome sequencing together with validation analysis by Sanger sequencing were performed to find possible pathogenic mutations. After a pipeline of database filtering, including public databases and in-house databases, a novel missense mutation, c. 424 C>. T transition (p.R142W) in OR2W3 gene, was identified as a potentially causative mutation for autosomal dominant RP. The mutation co-segregated with the disease phenotype over four generations. This mutation was validated in another independent three-generation family. RT-PCR analysis also identified that OR2W3 gene was expressed in HESC-RPE cell line. The results will not only enhance our current understanding of the genetic basis of RP, but also provide helpful clues for designing future studies to further investigate genetic factors for familial RP