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It is widely known that some soils have strong levels of disease suppression and prevent the establishment of pathogens in the rhizosphere of plants. However, what soils are better suppressing disease, and how management can help us to boost disease suppression remain unclear. Here, we used field, greenhouse and laboratory experiments to investigate the effect of management (monocropping and rotation) on the capacity of rhizosphere microbiomes in suppressing peanut root rot disease. Compared with crop rotations, monocropping resulted in microbial assemblies that were less effective in suppressing root rot diseases. Further, the depletion of key rhizosphere taxa in monocropping, which were at a disadvantage in the competition for limited exudates resources, reduced capacity to protect plants against pathogen invasion. However, the supplementation of depleted strains restored rhizosphere resistance to pathogen. Taken together, our findings highlight the role of native soil microbes in fighting disease and supporting plant health, and indicate the potential of using microbial inocula to regenerate the natural capacity of soil to fight disease. Crop rotation helps preventing pathogen infestations compared to monocultures, which may be partly due to root-associated microbes. Here, the authors show that rhizosphere microbiomes in monocultures are less able to suppress fungal pathogens compared to crop rotations, and that inoculating certain microbes can mitigate it.

Kaempferol (kae) and its glycosides are widely distributed in nature and show multiple bioactivities, yet few reports have compared them. In this paper, we report the antitumor, antioxidant and anti-inflammatory activity differences of kae, kae-7-O-glucoside (kae-7-O-glu), kae-3-O-rhamnoside (kae-3-O-rha) and kae-3-O-rutinoside (kae-3-O-rut). Kae showed the highest antiproliferation effect on the human hepatoma cell line HepG2, mouse colon cancer cell line CT26 and mouse melanoma cell line B16F1. Kae also significantly inhibited AKT phosphorylation and cleaved caspase-9, caspase-7, caspase-3 and PARP in HepG2 cells. A kae-induced increase in DPPH and ABTS radical scavenging activity, inhibition of concanavalin A (Con A)-induced activation of T cell proliferation and NO or ROS production in LPS-induced RAW 264.7 macrophage cells were also seen. Kae glycosides were used to produce kae via environment-friendly enzymatic hydrolysis. Kae-7-O-glu and kae-3-O-rut were hydrolyzed to kae by β-glucosidase and/or α-L-rhamnosidase. This paper demonstrates the application of enzymatic catalysis to obtain highly biologically active kae. This work provides a novel and efficient preparation of high-value flavone-related products.

Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

Gymnosperms are a unique lineage of plants that currently lack a high-quality reference genome due to their large genome size and high repetitive sequence content. Here, we report a nearly complete genome assembly for Ginkgo biloba with a genome size of 9.87 Gb, an N50 contig size of 1.58 Mb and an N50 scaffold size of 775 Mb. We were able to accurately annotate 27,832 protein-coding genes in total, superseding the inaccurate annotation of 41,840 genes in a previous draft genome assembly. We found that expansion of the G. biloba genome, accompanied by the notable extension of introns, was mainly caused by the insertion of long terminal repeats rather than the recent occurrence of whole-genome duplication events, in contrast to the findings of a previous report. We also identified candidate genes in the central pair, intraflagellar transport and dynein protein families that are associated with the formation of the spermatophore flagellum, which has been lost in all seed plants except ginkgo and cycads. The newly obtained Ginkgo genome provides new insights into the evolution of the gymnosperm genome. Analyses on a newly assembled, nearly complete genome of Ginkgo biloba revealed the cause of genome expansion and candidate genes associated with the formation of spermatophore flagellum in ginkgo, advancing our understanding about gymnosperm evolution.

Background Ginkgo ( Ginkgo biloba L.) is an excellent landscape species. Its yellow-green leaf mutants are ideal materials for research on pigment synthesis, but the regulatory mechanism of leaf coloration in these ginkgo mutants remains unclear. Results We compared the metabolomes and transcriptomes of green and mutant yellow leaves of ginkgo over the same period in this study. The results showed that the chlorophyll content of normal green leaves was significantly higher than that of mutant yellow leaves of ginkgo. We obtained 931.52M clean reads from different color leaves of ginkgo. A total of 283 substances in the metabolic profiles were finally detected, including 50 significantly differentially expressed metabolites (DEMs). We identified these DEMs and 1361 differentially expressed genes (DEGs), with 37, 4, 3 and 13 DEGs involved in the photosynthesis, chlorophyll, carotenoid, and flavonoid biosynthesis pathways, respectively. Moreover, integrative analysis of the metabolomes and transcriptomes revealed that the flavonoid pathway contained the upregulated DEM (−)-epicatechin. Fourteen DEGs from the photosynthesis pathway were positively or negatively correlated with the DEMs. Conclusions Our findings suggest a complex metabolic network in mutant yellow leaves. This study will provide a basis for studies of leaf color variation and regulation.

The predicted anti-oxidation is related to apoptosis, proliferation, lipid metabolism, cell differentiation, and immune response. There are some differences in the antioxidant capacity of the four typical components of ginkgo biloba extract (EGb) including ginkgo flavone (GF), ginkgolide (G), procyanidins (OPC), and organic acids (OA), and any two members of them can exhibit apparent synergistic effects. The order of DPPH scavenging ability was: OPC > GF > OA > G. The scavenging ability of procyanidins was close to that of VC; the scavenging capacity of ABTS was GF > OPC > OA > G. The GF:OPC (1:9) showed the best synergism in scavenging DPPH and ABTS radicals. The 193 kinds of small molecules reported in EGb were obtained by analyzing the properties of EGb. In order to construct a corresponding biological activity target set, molecular docking and the network pharmacology method were employed to build the molecular action mechanism network of a compound target, and the main biological functions and signaling pathways involved with their antioxidant activities were predicted. The results displayed that the top ten compounds which belonged to the two broad categories, ginkgo flavonoids and proanthocyanidins, could interact closely with several important target proteins (CASP3, SOD2, MAPK1, HSPA4, and NQO1). This would be expected to lay a theoretical foundation for the deep development of Ginkgo biloba extract.

Flowering crabapple is an important ornamental flower. It is vital to understand the floral scent properties and the associated release dynamics for carrying out fragrant flower breeding or floral regulation of crabapple. Static headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was used to detect the volatile compounds in Malus ioensis “Prairie Rose” flowers at different flowering stages and at different day-night time. The results showed that methylheptenone, phenylethanol, geranylacetone, 2-(4-methoxyphenyl)ethanol, α-cedrene were the major compounds in M. ioensis “Prairie Rose”, but the compounds released during different stages and different day-night time were significantly different (P < 0.0001). A total of 25 volatile compounds were identified from the four flowering stages. The floral scents in the initial and flowering stages were the most similar (dissimilarity 0.21). The main compounds in these two stages were geranylacetone and methylheptenone, and the contents of geranylacetone and phenylethanol were positively correlated with the flowering stages. From the bud stage to the end of flowering, the total amount of volatile compounds released showed an initial increase followed by a decrease and the amounts of compounds released during the initial flowering stage were the highest. The aliphatic and benzenoids content was significant higher in the daytime than at night. A total of 15 compounds were detected in the five time periods. Methylheptenone and phenylethanol were particularly released in the 10:00–12:00 and 15:00–17:00 time periods. There were only three common compounds among the five time periods and the types of flower volatiles released during the daytime were obviously higher than those released at night. From the nocturnal to diurnal, the amount of flower volatiles released first increased, then decreased, and the release reached a peak between 10 am and 12 noon, which was consistent with the pollination biological characteristics of Malus flowers. Our findings are important for understanding the mechanism of insect visits to crabapple and the regulation of crabapple flower scent.

Ginkgo biloba leaves have various health benefits due to the presence of bioactive compounds such as polyprenyl acetates, flavonoids, and terpene trilactones. However, there is little literature reported on the aromatic acids in Ginkgo biloba leaves. In this work, five aromatic acids including shikimic acid (SA), 6-hydroxykynurenic acid (6-HKA), protocatechuic acid (PA), gallic acid (GAA), and p-hydroxybenzoic acid (PHBA) were simultaneously extracted from Ginkgo biloba leaves by employing the green deep eutectic solvents (DESs). A DES tailor-made from xylitol, glycolic acid and 1,5-pentanedioic acid at a molar ratio of 1:3:1 with 50% (w/w) water addition, named as NGG50, gave higher extraction yields for the five aromatic acids. Main factors affecting the extraction process were further optimized. The highest extraction yields of SA, GAA, 6-HKA, PA, and PHBA were 94.15 ± 0.96 mg/g, 332.69 ± 5.19 μg/g, 25.90 ± 0.61 μg/g, 429.89 ± 11.47 μg/g and 67.94 ± 0.37 μg/g, respectively. The NGG50-based extraction process developed here was a successful attempt of simultaneously extracting five aromatic acids from Ginkgo biloba leaves for the first time, which could provide a new exploitation direction of Ginkgo biloba leaves.

As a medicinal and edible homologous substance, ginkgo seeds’ historical application in headache management, documented from Diannan Materia Medica to contemporary clinical practice, is based on empirical evidence. This study employed network pharmacology and molecular docking to explore the anti-migraine mechanisms of ginkgo seeds. In total, 10 related signal pathways (cancer pathway, lipid and atherosclerosis, Phosphatidylinositol 3-Kinase–Protein Kinase B (PI3K-AKT) signaling pathway, etc.) and 10 hub genes were identified through Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and in the inprotein–protein interaction (PPI) network. Molecular docking results demonstrated that formononetin, stigmasterol, and beta-sitosterol in ginkgo seeds can interact with 10 core targets (average binding energy ≤ −3.17 kcal/mol). This study analyzed the related pathways and targets of ginkgo seeds in the treatment of migraines, as well as the docking test of small-molecule ligands and target protein receptors, which provides a reference with which to find and explore effective preventive health foods for migraines.

Accurate estimation and extrapolation of forest structural parameters in planted forests are essential for monitoring forest resources, investigating their ecosystem services (e.g., forest structure and functions), as well as supporting decisions for precision silviculture. Advances in unmanned aerial vehicle (UAV)-borne Light Detection and Ranging (LiDAR) technology have enhanced our ability to precisely characterize the 3-D structure of the forest canopy with high flexibility, usually within forest plots and stands. For wall-to-wall forest structure mapping in broader landscapes, samples (transects) of UAV-LiDAR datasets are a cost-efficient solution as an intermediate layer for extrapolation from field plots to full-coverage multispectral satellite imageries. In this study, an advanced two-stage extrapolation approach was established to estimate and map large area forest structural parameters (i.e., mean DBH, dominant height, volume, and stem density), in synergy with field plots and UAV-LiDAR and GF-6 satellite imagery, in a typical planted forest of southern China. First, estimation models were built and used to extrapolate field plots to UAV-LiDAR transects; then, the maps of UAV-LiDAR transects were extrapolated to the whole study area using the wall-to-wall grid indices that were calculated from GF-6 satellite imagery. By comparing with direct prediction models that were fitted by field plots and GF-6-derived spectral indices, the results indicated that the two-stage extrapolation models (R2 = 0.64–0.85, rRMSE = 7.49–26.85%) obtained higher accuracy than direct prediction models (R2 = 0.58–0.75, rRMSE = 21.31–38.43%). In addition, the effect of UAV-LiDAR point density and sampling intensity for estimation accuracy was studied by sensitivity analysis as well. The results showed a stable level of accuracy for approximately 10% of point density (34 pts·m−2) and 20% of sampling intensity. To understand the error propagation through the extrapolation procedure, a modified U-statistics uncertainty analysis was proposed to characterize pixel-level estimates of uncertainty and the results demonstrated that the uncertainty was 0.75 cm for mean DBH, 1.23 m for dominant height, 14.77 m3·ha−1 for volume and 102.72 n·ha−1 for stem density, respectively.