Genomic diversity, population structure, and genome-wide association reveal genetic differentiation and trait improvements in mango.

Mango (Mangifera indica L.) has been widely cultivated as a culturally and economically significant fruit tree for roughly 4000 years. Despite its rich history, little is known about the crop's domestication, genomic variation, and the genetic loci underlying agronomic traits. This study employs the whole-genome re-sequencing of 224 mango accessions sourced from 22 countries, with an average sequencing depth of 16.37×, to explore their genomic variation and diversity. Through phylogenomic analysis, M. himalis J.Y. Liang, a species grown in China, was reclassified into the cultivated mango group known as M. indica. Moreover, our investigation of mango population structure and differentiation revealed that Chinese accessions could be divided into two distinct gene pools, indicating the presence of independent genetic diversity ecotypes. By coupling genome-wide association studies with analyses of genotype variation patterns and expression patterns, we identified several candidate loci and dominant genotypes associated with mango flowering capability, fruit weight, and volatile compound production. In conclusion, our study offers valuable insights into the genetic differentiation of mango populations, paving the way for future agronomic improvements through genomic-assisted breeding.

Combined transcriptome and metabolome analysis reveal that the white and yellow mango pulp colors are associated with carotenoid and flavonoid accumulation, and phytohormone signaling.

Mango (Mangifera indica L.) is a widely appreciated tropical fruit for its rich color and nutrition. However, knowledge on the molecular basis of color variation is limited. Here, we studied HY3 (yellowish-white pulp) and YX4 (yellow pulp), reaped with 24 h gap from the standard harvesting time. The carotenoids and total flavonoids increased with the advance of harvest time (YX4 > HY34). Transcriptome sequencing showed that higher expressions of the core carotenoid biosynthesis genes and flavonoid biosynthesis genes are correlated to their respective contents. The endogenous indole-3-acetic acid and jasmonic acid contents decreased but abscisic acid and ethylene contents increased with an increase in harvesting time (YX4 > HY34). Similar trends were observed for the corresponding genes. Our results indicate that the color differences are related to carotenoid and flavonoid contents, which in turn are influenced by phytohormone accumulation and signaling.

Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data.

Phase unwrapping (PU) is a key step in the reconstruction of digital elevation models (DEMs) and the monitoring of surface deformation from interferometric synthetic aperture radar (SAR, InSAR) data. In this paper, an improved PU method that combines an amended matrix pencil model, an adaptive unscented kalman filter (AUKF), an efficient quality-guided strategy based on heapsort, and a circular median filter is proposed. PU theory and the existing UKFPU method are covered. Then, the improved method is presented with emphasis on the AUKF and the circular median filter. AUKF has been well used in other fields, but it is for the first time applied to interferometric images PU, to the best of our knowledge. First, the amended matrix pencil model is used to estimate the phase gradient. Then, an AUKF model is used to unwrap the interferometric phase based on an efficient quality-guided strategy based on heapsort. Finally, the key results are obtained by filtering the results using a circular median. The proposed method is compared with the minimum cost network flow (MCF), statistical cost network flow (SNAPHU), regularized phase tracking technique (RPTPU), and UKFPU methods using two sets of simulated data and two sets of experimental GF-3 SAR data. The improved method is shown to yield the greatest accuracy in the interferometric phase maps compared to the methods considered in this paper. Furthermore, the improved method is shown to be the most robust to noise and is thus most suitable for PU of GF-3 SAR data in high-noise and low-coherence regions.