Broemouritsen3200

Additionally, we identified the presence of hexenyl diglycosides, which indicates that aerial Z-3-HAC is metabolized in the leaves by glycosyltransferases. Together these data indicate that GLV Z-3-HAC is taken up by leaves and incites oxidative stress. This subsequently results in the modulation of the phenylpropanoid pathway and an induction of glycosylation processes.Ascorbate (vitamin C) is an essential multifunctional molecule for both plants and mammals. In plants, ascorbate is the most abundant water-soluble antioxidant that supports stress tolerance. In humans, ascorbate is an essential micronutrient and promotes iron (Fe) absorption in the gut. Engineering crops with increased ascorbate levels have the potential to improve both crop stress tolerance and human health. Here, rice (Oryza sativa L.) plants were engineered to constitutively overexpress the rice GDP-L-galactose phosphorylase coding sequence (35S-OsGGP), which encodes the rate-limiting enzymatic step of the L-galactose pathway. Ascorbate concentrations were negligible in both null segregant (NS) and 35S-OsGGP brown rice (BR, unpolished grain), but significantly increased in 35S-OsGGP germinated brown rice (GBR) relative to NS. Foliar ascorbate concentrations were significantly increased in 35S-OsGGP plants in the vegetative growth phase relative to NS, but significantly reduced at the reproductive growth phase and were associated with reduced OsGGP transcript levels. The 35S-OsGGP plants did not display altered salt tolerance at the vegetative growth phase despite having elevated ascorbate concentrations. Ascorbate concentrations were positively correlated with ferritin concentrations in Caco-2 cells - an accurate predictor of Fe bioavailability in human digestion - exposed to in vitro digests of NS and 35S-OsGGP BR and GBR samples.Phytolith-occluded carbon (PhytOC), a promising long-term biogeochemical carbon sequestration mode, plays a crucial role in the global carbon cycle and the regulation of atmospheric CO2. Previous studies mostly focused on the estimation of the content and storage of PhytOC, while it remains unclear about how the management practices affect the PhytOC content and whether it varies with stand age. Moso bamboo (Phyllostachys heterocycla var. pubescens) has a great potential in carbon sequestration and is rich in PhytOC. Here, we selected four management treatments, including control (CK), compound fertilization (CF), silicon (Si) fertilization (SiF) (monosilicic acid can form phytoliths through silicification), and cut to investigate the variation of phytoliths and PhytOC contents in soil, leaves, and litters, and their storage in Moso bamboo forests. In soil, the SiF fertilizer treatment significantly (P less then 0.05) increased phytolith content, PhytOC content, and storage compared to CK, while there were no significant differences between the treatments of CF and cut. In leaf, compared with CK, phytolith content of the second-degree leaves under SiF and the first-degree leaves under cut treatment significantly increased, and the three treatments significantly increased PhytOC storage for leaves with three age classes. In litter, the phytolith and PhytOC contents under the three treatments were not significantly different from that under the CK treatment. The PhytOC storage increased by 19.33% under SiF treatment, but significantly decreased by 40.63% under the CF treatment. For the entire Moso bamboo forest ecosystems, PhytOC storage of all the three management treatments increased compared with CK, with the largest increase by 102% under the SiF treatment. PROTAC tubulin-Degrader-1 The effects of management practices on the accumulation of PhytOC varied with age. Our study implied that Si fertilization has a greater potential to significantly promote the capacity of sequestration of carbon in Moso bamboo forests.In flowering plants, sugars act as carbon sources providing energy for developing embryos and seeds. Although most studies focus on carbon metabolism in whole seeds, knowledge about how particular sugars contribute to the developmental transitions during embryogenesis is scarce. To develop a quantitative understanding of how carbon composition changes during embryo development, and to determine how sugar status contributes to final seed or embryo size, we performed metabolic profiling of hand-dissected embryos at late torpedo and mature stages, and dormant seeds, in two Arabidopsis thaliana accessions with medium [Columbia-0 (Col-0)] and large [Burren-0 (Bur-0)] seed sizes, respectively. Our results show that, in both accessions, metabolite profiles of embryos largely differ from those of dormant seeds. We found that developmental transitions from torpedo to mature embryos, and further to dormant seeds, are associated with major metabolic switches in carbon reserve accumulation. While glucose, sucrose, and starch predominantly accumulated during seed dormancy, fructose levels were strongly elevated in mature embryos. Interestingly, Bur-0 seeds contain larger mature embryos than Col-0 seeds. Fructose and starch were accumulated to significantly higher levels in mature Bur-0 than Col-0 embryos, suggesting that they contribute to the enlarged mature Bur-0 embryos. Furthermore, we found that Bur-0 embryos accumulated a higher level of sucrose compared to hexose sugars and that changes in sucrose metabolism are mediated by sucrose synthase (SUS), with SUS genes acting non-redundantly, and in a tissue-specific manner to utilize sucrose during late embryogenesis.Traditionally, plant disease recognition has mainly been done visually by human. It is often biased, time-consuming, and laborious. Machine learning methods based on plant leave images have been proposed to improve the disease recognition process. Convolutional neural networks (CNNs) have been adopted and proven to be very effective. Despite the good classification accuracy achieved by CNNs, the issue of limited training data remains. In most cases, the training dataset is often small due to significant effort in data collection and annotation. In this case, CNN methods tend to have the overfitting problem. In this paper, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is combined with label smoothing regularization (LSR) to improve the prediction accuracy and address the overfitting problem under limited training data. Experiments show that the proposed WGAN-GP enhanced classification method can improve the overall classification accuracy of plant diseases by 24.4% as compared to 20.