STUDY ON THE EXTRACTION PROCESS OF TOTAL ANTHRAQUINONES IN RADIX ET RHIZOMA RHEI AND THEIR ANTILIPEMIC EFFECTS

Background: Radix et Rhizoma Rhei has a gastric mucosal protective effect, major anti-gastritis and anti-peptic ulcer active constituents were emodin, aloe-emodin, chrysophanol, rhein, etc. The objective of the study was the extraction process of total anthraquinones in Radix et Rhizoma rhei and their antilipemic effects. Materials and Methods: Orthogonal experiment, UV spectrophotometry and conventional antilipemic effect determination method were used to optimize the extraction process, and to determine the total anthraquinone content, as well as blood levels of total cholesterol, triglycerides, HDL and LDL. Results: Ethanol concentration, extraction time and ethanol amount had significant influences on the extraction of total Radix et Rhizoma rhei anthraquinones, total Radix et Rhizoma rhei anthraquinones could significantly reduce blood levels of total cholesterol, triglycerides, HDL and LDL. Conclusion: The optimum extraction process was two times extraction of Radix et Rhizoma rhei with 70% ethanol, the amounts of solvent of 8 folds and 5 folds, successively, and the extraction time of 60 min each. In addition, this extract has an antilipemic effect in mice

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Background: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

Soil Moisture but Not Warming Dominates Nitrous Oxide Emissions During Freeze–Thaw Cycles in a Qinghai–Tibetan Plateau Alpine Meadow With Discontinuous Permafrost

Large quantities of organic matter are stored in frozen soils (permafrost) within the Qinghai–Tibetan Plateau (QTP). The most of QTP regions in particular have experienced significant warming and wetting over the past 50 years, and this warming trend is projected to intensify in the future. Such climate change will likely alter the soil freeze–thaw pattern in permafrost active layer and toward significant greenhouse gas nitrous oxide (N2O) release. However, the interaction effect of warming and altered soil moisture on N2O emission during freezing and thawing is unclear. Here, we used simulation experiments to test how changes in N2O flux relate to different thawing temperatures (T5–5°C, T10–10°C, and T20–20°C) and soil volumetric water contents (VWCs, W15–15%, W30–30%, and W45–45%) under 165 F–T cycles in topsoil (0–20 cm) of an alpine meadow with discontinuous permafrost in the QTP. First, in contrast to the prevailing view, soil moisture but not thawing temperature dominated the large N2O pulses during F–T events. The maximum emissions, 1,123.16–5,849.54 μg m–2 h–1, appeared in the range of soil VWC from 17% to 38%. However, the mean N2O fluxes had no significant difference between different thawing temperatures when soil was dry or waterlogged. Second, in medium soil moisture, low thawing temperature is more able to promote soil N2O emission than high temperature. For example, the peak value (5,849.54 μg m–2 h–1) and cumulative emissions (366.6 mg m–2) of W30T5 treatment were five times and two to four times higher than W30T10 and W30T20, respectively. Third, during long-term freeze–thaw cycles, the patterns of cumulative N2O emissions were related to soil moisture. treatments; on the contrary, the cumulative emissions of W45 treatments slowly increased until more than 80 cycles. Finally, long-term freeze–thaw cycles could improve nitrogen availability, prolong N2O release time, and increase N2O cumulative emission in permafrost active layer. Particularly, the high emission was concentrated in the first 27 and 48 cycles in W15 and W30, respectively. Overall, our study highlighted that large emissions of N2O in F–T events tend to occur in medium moisture soil at lower thawing temperature; the increased number of F–T cycles may enhance N2O emission and nitrogen mineralization in permafrost active layer

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

Morphology, photosynthetic physiology and biochemistry of nine herbaceous plants under water stress

Global climate warming and shifts in rainfall patterns are expected to trigger increases in the frequency and magnitude of drought and/or waterlogging stress in plants. To cope with water stress, plants develop diverse tactics. However, the adoption capability and mechanism vary depending upon the plant species identity as well as stress duration and intensity. The objectives of this study were to evaluate the species-dependent responses of alpine herbaceous species to water stress. Nine herbaceous species were subjected to different water stresses (including moderate drought and moderate waterlogging) in pot culture using a randomized complete block design with three replications for each treatment. We hypothesized that water stress would negatively impact plant growth and metabolism. We found considerable interspecies differences in morphological, physiological, and biochemical responses when plants were exposed to the same water regime. In addition, we observed pronounced interactive effects of water regime and plant species identity on plant height, root length, root/shoot ratio, biomass, and contents of chlorophyll a, chlorophyll b, chlorophyll (a+b), carotenoids, malondialdehyde, soluble sugar, betaine, soluble protein and proline, implying that plants respond to water regime differently. Our findings may cast new light on the ecological restoration of grasslands and wetlands in the Qinghai-Tibetan Plateau by helping to select stress-tolerant plant species

Variation in biomass and nutrients allocation of Corydalis hendersonii on the Tibetan Plateau with increasing rainfall continentality and altitude

International audienceThe Tibetan Plateau is considered as one of most sensitive region to global change. Nutrient (N and P) availability is an important limiting ecological factor in cold terrestrial ecosystems such as the alpine belt of the Tibetan Plateau. We focused on Corydallis hendersonii, an endemic alpine species of the Tibetan Plateau. Exploring the N and P below- and above-ground responses of C. hendersonii to climatic factors is crucial for biodiversity conservation of the alpine Tibetan plateau under global change. We used the Outlying Mean Index and regression analyses to assess N and P stoichiometry and biomass responses in leaves and roots of C. hendersonii along climatic gradients. We found that investment and allocation of nutrient and biomass in C. hendersonii were mainly driven by rainfall continentality. In the eastern less-continental wet area of the Tibetan plateau, C. hendersonii had higher biomass in leaf, and lower N and P investment in roots than in the western more continental dry part. Specifically, 300 mm year−1 Mean annual precipitation (MAP) and ca. 80° Rainfall continentality index (GAMS) were threshold values of climate stress inducing strong nutrient limitation for C. hendersonii across the Tibetan Plateau. Our results suggest that rainfall continentality is the primary climatic driver of variation in biomass and nutrients allocation of C. hendersonii on the Tibetan Plateau. Thus, global warming and drying should induce a decrease in total biomass, a reduction in leaf N and P concentrations and an increase in root/shoot ratio in the alpine region of the Tibetan Plateau

Effect of Sodium Selenite Concentration and Culture Time on Extracellular and Intracellular Metabolite Profiles of Epichloë sp. Isolated from Festuca sinensis in Liquid Culture

Selenium (Se) is not only an essential trace element critical for the proper functioning of an organism, but it is also an abiotic stressor that affects an organism’s growth and metabolite profile. In this study, Epichloë sp. from Festuca sinensis was exposed to increasing concentrations of Na2SeO3 (0, 0.1, and 0.2 mmol/L) in a liquid media for eight weeks. The mycelia and fermentation broth of Epichloë sp. were collected from four to eight weeks of cultivation. The mycelial biomass decreased in response to increased Se concentrations, and biomass accumulation peaked at week five. Using gas chromatography-mass spectrometry (GC-MS), approximately 157 and 197 metabolites were determined in the fermentation broth and mycelia, respectively. Diverse changes in extracellular and intracellular metabolites were observed in Epichloë sp. throughout the cultivation period in Se conditions. Some metabolites accumulated in the fermentation broth, while others decreased after different times of Se exposure compared to the control media. However, some metabolites were present at lower concentrations in the mycelia when cultivated with Se. The changes in metabolites under Se conditions were dynamic over the experimental period and were involved in amino acids, carbohydrates, organic acids, fatty acids, and nucleotides. Based on these results, we conclude that selenite concentrations and culture time influence the growth, extracellular and intracellular metabolite profiles of Epichloë sp. from F. sinensis