A Deep Learning-Based Algorithm for Identifying Precipitation Clouds Using Fengyun-4A Satellite Observation Data.

Rapid and accurate identification of precipitation clouds from satellite observations is essential for the research of quantitative precipitation estimation and precipitation nowcasting. In this study, we proposed a novel Convolutional Neural Network (CNN)-based algorithm for precipitation cloud identification (PCINet) in the daytime, nighttime, and nychthemeron. High spatiotemporal and multi-spectral information from the Fengyun-4A (FY-4A) satellite is utilized as the inputs, and a multi-scale structure and skip connection constraint strategy are presented in the framework of the algorithm to improve the precipitation cloud identification. Moreover, the effectiveness of visible/near-infrared spectral information in improving daytime precipitation cloud identification is explored. To evaluate this algorithm, we compare it with five other deep learning models used for image segmentation and perform qualitative and quantitative analyses of long-time series using data from 2021. In addition, two heavy precipitation events are selected to analyze the spatial distribution of precipitation cloud identification. Statistics and visualization of the experiment results show that the proposed model outperforms the baseline models in this task, and adding visible/near-infrared spectral information in the daytime can effectively improve model performance. More importantly, the proposed model can provide accurate and near-real-time results, which has important application in observing precipitation clouds.

Accelerated decline of snow cover in China from 1979 to 2018 observed from space.

Snow cover is an important indicator of climate change. Variations of snow integrate the competing effect of increasing temperature and precipitation. In this study, based on Theil-Sen Median (TSM) and Mann-Kendall (M-K) methods, observational evidence from space was used to investigate the variation of snow parameters in China from 1979 to 2018, and some meaningful conclusions were found. (1) The downward trend of snow depth (SD) with a median of -0.02 cm/year was generally found in the high altitude mountains, and the upward trend of SD with a median of 0.01 cm/year occurs in the plains. A widespread and accelerated decrease of SD was observed in the latest period (2009-2018) in NC and NX. (2) The mean annual areas with snow cover days (SCDs) greater than 150 days (d) accounted for 17.8%, 24.73% and 38.14% in NC, NX and QTP. SCDs in NC and Northern QTP were widely reduced, but the longest snow season with more than eight months is still maintained on QTP. (3) The downward trend of snow storage (SS) was found in all three snow areas. The difference of snow phenology is reflected in the slowest accumulation and melting rate of SS on QTP; the largest peak value of SS and the shortest snow season in NC; the slow accumulation and rapid melting of snow in the NX, and the peak value is achieved at the latest. The trend of maximum temperature was judged as the most important factor affecting the change of SD, while longitude and latitude are closely related to the change of SCD.

Characterization of the complete chloroplast genome of Arachis pintoi Krapov. & W.C.Greg., a perennial leguminous forage.

Arachis pintoi Krapov. & W.C.Greg. is an important leguminous forage grass species that have extremely wide ranges of distribution in the tropical and sub-tropical regions. It has high feeding value and horticultural value. In this study, we sequenced and assembled the complete chloroplast genome of A. pintoi. The chloroplast genome is 156,185 bp in length, containing a pair of inverted repeated (IR) regions of 25,820 bp that are separated by a large single copy (LSC) region of 85,637 bp, and a small single copy (SSC) region of 18,908 bp. The complete chloroplast genome contains 112 unique genes, including 80 protein-coding genes, 28 transfer RNA genes (tRNAs), and four ribosomal RNA genes (rRNAs). The overall GC content was 36.4%. The phylogenetic analysis demonstrated that A. pintoi formed a single branch among genus Arachis. The whole chloroplast genome of A. pintoi will be a useful resource for future studies on phylogeny and conservation in Arachis.

Characterization of the complete chloroplast genome of an annual herb, Chenopodium album (Amaranthaceae).

Chenopodium album is an annual herb from Amaranthaceae with worldwide distribution. It is a leafy vegetable as well as an important subsidiary grain crop with high nutritional value and medicinal value. In this study, we reported the complete chloroplast genome of C. album. The total chloroplast genome was 152,167 bp in length, containing a large single-copy region (LSC, 83,676 bp), a small single-copy region (SSC, 18,105 bp), and a pair of inverted repeat regions (IRs, 25,193 bp). The complete chloroplast genome contains 110 genes, including 78 protein-coding genes, 28 transfer RNA (tRNA) genes, and 4 ribosomal RNA (rRNA) genes with an overall GC content of 37.3%. Phylogenetic analysis showed that C. album was sister to C. acuminatum within Chenopodioideae. The complete chloroplast genome of C. album will provide useful resources for the development and utilization of this species and the phylogenetic study of Amaranthaceae.

High-density linkage map construction and QTL analyses for fiber quality, yield and morphological traits using CottonSNP63K array in upland cotton (Gossypium hirsutum L.).

BACKGROUND: Improving fiber quality and yield are the primary research objectives in cotton breeding for enhancing the economic viability and sustainability of Upland cotton production. Identifying the quantitative trait loci (QTL) for fiber quality and yield traits using the high-density SNP-based genetic maps allows for bridging genomics with cotton breeding through marker assisted and genomic selection. In this study, a recombinant inbred line (RIL) population, derived from cross between two parental accessions, which represent broad allele diversity in Upland cotton, was used to construct high-density SNP-based linkage maps and to map the QTLs controlling important cotton traits. RESULTS: Molecular genetic mapping using RIL population produced a genetic map of 3129 SNPs, mapped at a density of 1.41 cM. Genetic maps of the individual chromosomes showed good collinearity with the sequence based physical map. A total of 106 QTLs were identified which included 59 QTLs for six fiber quality traits, 38 QTLs for four yield traits and 9 QTLs for two morphological traits. Sub-genome wide, 57 QTLs were mapped in A sub-genome and 49 were mapped in D sub-genome. More than 75% of the QTLs with favorable alleles were contributed by the parental accession NC05AZ06. Forty-six mapped QTLs each explained more than 10% of the phenotypic variation. Further, we identified 21 QTL clusters where 12 QTL clusters were mapped in the A sub-genome and 9 were mapped in the D sub-genome. Candidate gene analyses of the 11 stable QTL harboring genomic regions identified 19 putative genes which had functional role in cotton fiber development. CONCLUSION: We constructed a high-density genetic map of SNPs in Upland cotton. Collinearity between genetic and physical maps indicated no major structural changes in the genetic mapping populations. Most traits showed high broad-sense heritability. One hundred and six QTLs were identified for the fiber quality, yield and morphological traits. Majority of the QTLs with favorable alleles were contributed by improved parental accession. More than 70% of the mapped QTLs shared the similar map position with previously reported QTLs which suggest the genetic relatedness of Upland cotton germplasm. Identification of QTL clusters could explain the correlation among some fiber quality traits in cotton. Stable and major QTLs and QTL clusters of traits identified in the current study could be the targets for map-based cloning and marker assisted selection (MAS) in cotton breeding. The genomic region on D12 containing the major stable QTLs for micronaire, fiber strength and lint percentage could be potential targets for MAS and gene cloning of fiber quality traits in cotton.

Topological Data Analysis as a Morphometric Method: Using Persistent Homology to Demarcate a Leaf Morphospace.

Current morphometric methods that comprehensively measure shape cannot compare the disparate leaf shapes found in seed plants and are sensitive to processing artifacts. We explore the use of persistent homology, a topological method applied as a filtration across simplicial complexes (or more simply, a method to measure topological features of spaces across different spatial resolutions), to overcome these limitations. The described method isolates subsets of shape features and measures the spatial relationship of neighboring pixel densities in a shape. We apply the method to the analysis of 182,707 leaves, both published and unpublished, representing 141 plant families collected from 75 sites throughout the world. By measuring leaves from throughout the seed plants using persistent homology, a defined morphospace comparing all leaves is demarcated. Clear differences in shape between major phylogenetic groups are detected and estimates of leaf shape diversity within plant families are made. The approach predicts plant family above chance. The application of a persistent homology method, using topological features, to measure leaf shape allows for a unified morphometric framework to measure plant form, including shapes, textures, patterns, and branching architectures.

Modifications to a LATE MERISTEM IDENTITY1 gene are responsible for the major leaf shapes of Upland cotton (Gossypium hirsutum L.).

Leaf shape varies spectacularly among plants. Leaves are the primary source of photoassimilate in crop plants, and understanding the genetic basis of variation in leaf morphology is critical to improving agricultural productivity. Leaf shape played a unique role in cotton improvement, as breeders have selected for entire and lobed leaf morphs resulting from a single locus, okra (l-D1), which is responsible for the major leaf shapes in cotton. The l-D1 locus is not only of agricultural importance in cotton, but through pioneering chimeric and morphometric studies, it has contributed to fundamental knowledge about leaf development. Here we show that an HD-Zip transcription factor homologous to the LATE MERISTEM IDENTITY1 (LMI1) gene of Arabidopsis is the causal gene underlying the l-D1 locus. The classical okra leaf shape allele has a 133-bp tandem duplication in the promoter, correlated with elevated expression, whereas an 8-bp deletion in the third exon of the presumed wild-type normal allele causes a frame-shifted and truncated coding sequence. Our results indicate that subokra is the ancestral leaf shape of tetraploid cotton that gave rise to the okra allele and that normal is a derived mutant allele that came to predominate and define the leaf shape of cultivated cotton. Virus-induced gene silencing (VIGS) of the LMI1-like gene in an okra variety was sufficient to induce normal leaf formation. The developmental changes in leaves conferred by this gene are associated with a photosynthetic transcriptomic signature, substantiating its use by breeders to produce a superior cotton ideotype.

Chiral bifunctional ferrocenylphosphine catalyzed highly enantioselective [3 + 2] cycloaddition reaction.

A series of air-stable ferrocenylphosphines LB1-LB8 were designed and prepared in high yields. (R,SFc)-ferrocenylphosphine LB5 was found to efficiently promote the asymmetric [3 + 2] cycloaddition of Morita-Baylis-Hillman carbonates with maleimides to afford the corresponding bicyclic imides with 84-99% ee and 67-99% yield. Interestingly, the configuration of these products was contrary to those reported in the literature.