Virus-induced gene silencing (VIGS) is an RNA-mediated reverse genetics technology that has evolved into an indispensable approach for analyzing the function of genes. It downregulates endogenous genes by utilizing the posttranscriptional gene silencing (PTGS) machinery of plants to prevent systemic viral infections. Based on recent advances, VIGS can now be used as a high-throughput tool that induces heritable epigenetic modifications in plants through the viral genome by transiently knocking down targeted gene expression. As a result of the progression of DNA methylation induced by VIGS, new stable genotypes with desired traits are being developed in plants. In plants, RNA-directed DNA methylation (RdDM) is a mechanism where epigenetic modifiers are guided to target loci by small RNAs, which play a major role in the silencing of the target gene. In this review, we described the molecular mechanisms of DNA and RNA-based viral vectors and the knowledge obtained through altering the genes in the studied plants that are not usually accessible to transgenic techniques. We showed how VIGS-induced gene silencing can be used to characterize transgenerational gene function(s) and altered epigenetic marks, which can improve future plant breeding programs.

In this paper, the random forests method and the support vector machine in machine learning are explored and compared to the traditional statistical-based maximum likelihood method with 126 features from Sentinel-2A images. The spectral reflectance of 12 bands, 96 texture parameters, 7 vegetation indices, and 11 phenological parameters are successfully extracted from Sentinel-2A images in 2017. The classification result shows that the optimal combination of 13 features yields overall accuracies of traditional classification and machine learning classification of 88.96% and 98%, respectively. Short-wave infrared information shows a significant effect on distinguishing rice, corn, and soybean. The water vapor band plays a significant role in distinguishing between corn and rice. In the multiclassification problem, the machine learning methods have robustness with the identification accuracy of greater than 95% for each crop type, whereas the traditional classification result shows imbalanced accuracies for different crops.

Vegetation phenology is a key biological indicator for monitoring terrestrial ecosystems and global change, and regions with the most obvious phenological changes in vegetation are primarily located at high latitudes and altitudes. Over the past three decades, investigations of obvious phenological changes in vegetation at middle and high latitudes in the Northern Hemisphere have provided significant contributions to understanding global climate change. In this study, phenological parameters were extracted from the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) to analyze the spatial and temporal characteristics of vegetation phenological changes above 40°N in the Northern Hemisphere from 1982–2013. The results showed that the start of season (SOS) was significantly advanced (−2.2 ± 0.6 days·decade⁻¹, p < 0.05) and that the end of season (EOS) was slightly delayed (0.78 ± 0.6 days·decade⁻¹, p = 0.21) over the entire study area in the initial 21 years (1982–2002). When the time scale was extended to 2013, the change rate of the SOS and EOS was significantly reduced; in addition, the SOS was delayed (3.2 ± 1.7 days·decade⁻¹, p < 0.05), and the EOS was advanced (4.5 ± 0.9 days·decade⁻¹, p < 0.05) over the entire study area in the last 11 years (2003–2013). The trends of advanced SOS and delayed EOS over the past three decades were slower than those over the initial two decades on a hemispheric scale. The change trends showed obvious variability with different vegetation types and were greater for woody plants than for herbaceous plants. For broad-leaved forest, the SOS was significantly advanced (2 ± 0.5 days·decade⁻¹, p < 0.05) and the EOS was significantly delayed (2.7 ± 0.6 days·decade⁻¹, p < 0.05) from 1982–2013. The trend of delayed EOS was greater than that of advanced SOS for different vegetation types. With respect to the spatial distribution of phenological trends in the Northern Hemisphere, the trends of advanced SOS and delayed EOS were strongest in Europe followed by North America, and the trends were least significant in Asia. Coniferous forest, shrub forest, grassland, and the entire study area have the same change trends for the two time periods (1982–2002 and 2003–2013), and the increased rate of the phenology parameters has decelerated over the most recent decade. The length of season (LOS) of broad-leaved forest and mixed forest over the past 32 years shows a strong increased trend, and simultaneously, the SOS and EOS show an advanced trend and a delayed trend, respectively

Seed germination is crucial for the life cycle of plants and maximum crop production. This critical developmental step is regulated by diverse endogenous [hormones, reactive oxygen species (ROS)] and exogenous (light, temperature) factors. Reactive oxygen species promote the release of seed dormancy by biomolecules oxidation, testa weakening and endosperm decay. Reactive oxygen species modulate metabolic and hormone signaling pathways that induce and maintain seed dormancy and germination. Endosperm provides nutrients and senses environmental signals to regulate the growth of the embryo by secreting timely signals. The growing energy demand of the developing embryo and endosperm is fulfilled by functional mitochondria. Mitochondrial matrix-localized heat shock protein GhHSP24.7 controls seed germination in a temperature-dependent manner. In this review, we summarize comprehensive view of biochemical and molecular mechanisms, which coordinately control seed germination. We also discuss that the accurate and optimized coordination of ROS, mitochondria, heat shock proteins is required to permit testa rupture and subsequent germination.

Background: Chinese cabbage (Brassica rapa ssp. pekinensis) experienced a whole-genome triplication event and thus has three subgenomes: least fractioned, medium fractioned, and most fractioned subgenome. Environmental changes affect leaf development, which in turn influence the yield. To improve the yield and resistance to different climate scenarios, a comprehensive understanding of leaf development is required including insights into the full diversity of cell types and transcriptional networks underlying their specificity. Results: Here, we generate the transcriptional landscape of Chinese cabbage leaf at single-cell resolution by performing single-cell RNA sequencing of 30,000 individual cells. We characterize seven major cell types with 19 transcriptionally distinct cell clusters based on the expression of the reported marker genes. We find that genes in the least fractioned subgenome are predominantly expressed compared with those in the medium and most fractioned subgenomes in different cell types. Moreover, we generate a single-cell transcriptional map of leaves in response to high temperature. We find that heat stress not only affects gene expression in a cell type-specific manner but also impacts subgenome dominance. Conclusions: Our study highlights the transcriptional networks in different cell types and provides a better understanding of transcriptional regulation during leaf development and transcriptional response to heat stress in Chinese cabbage.