Protective Effect of Tetrahydroxystilbene Glucoside on 6-OHDA-Induced Apoptosis in PC12 Cells through the ROS-NO Pathway

Oxidative stress plays an important role in the pathogenesis of neurodegenerative diseases, such as Parkinson's disease. The molecule, 2,3,5,4′-tetrahydr- oxystilbene-2-O-β-D-glucoside (TSG), is a potent antioxidant derived from the Chinese herb, Polygonum multiflorum Thunb. In this study, we investigated the protective effect of TSG against 6-hydroxydopamine-induced apoptosis in rat adrenal pheochromocytoma PC12 cells and the possible mechanisms. Our data demonstrated that TSG significantly reversed the 6-hydroxydopamine-induced decrease in cell viability, prevented 6-hydroxydopamine-induced changes in condensed nuclei and decreased the percentage of apoptotic cells in a dose-dependent manner. In addition, TSG slowed the accumulation of intracellular reactive oxygen species and nitric oxide, counteracted the overexpression of inducible nitric oxide syntheses as well as neuronal nitric oxide syntheses, and also reduced the level of protein-bound 3-nitrotyrosine. These results demonstrate that the protective effects of TSG on rat adrenal pheochromocytoma PC12 cells are mediated, at least in part, by the ROS-NO pathway. Our results indicate that TSG may be effective in providing protection against neurodegenerative diseases associated with oxidative stress

Deep Bootstrap for Bayesian Inference

For a Bayesian, the task to define the likelihood can be as perplexing as the task to define the prior. We focus on situations when the parameter of interest has been emancipated from the likelihood and is linked to data directly through a loss function. We survey existing work on both Bayesian parametric inference with Gibbs posteriors as well as Bayesian non-parametric inference. We then highlight recent bootstrap computational approaches to approximating loss-driven posteriors. In particular, we focus on implicit bootstrap distributions defined through an underlying push-forward mapping. We investigate iid samplers from approximate posteriors that pass random bootstrap weights trough a trained generative network. After training the deep-learning mapping, the simulation cost of such iid samplers is negligible. We compare the performance of these deep bootstrap samplers with exact bootstrap as well as MCMC on several examples (including support vector machines or quantile regression). We also provide theoretical insights into bootstrap posteriors by drawing upon connections to model mis-specification

Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN

Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory (LSTM) network method

Functional Analysis of Three miRNAs in Agropyron mongolicum Keng under Drought Stress

Agropyron mongolicum Keng, a perennial diploid grass with high drought tolerance, belongs to the genus Agropyron, tribe Triticeae. It has made tremendous contributions toward reseeding natural pasture and seeding artificial grassland in China, especially in the arid and semi-arid area of northern China. As a wild relative of wheat, A. mongolicum is also a valuable resource for the genetic improvement of wheat crops. MicroRNAs are small non-coding RNA molecules ubiquitous in plants, which have been involved in responses to a wide variety of stresses including drought, salinity, chilling temperature. To date, little research has been done on drought-responsive miRNAs in A. mongolicum. In this study, two miRNA libraries of A. mongolicum under drought and normal conditions were constructed, and drought-responsive miRNAs were screened via Solexa high throughput sequencing and bioinformatic analysis. A total of 114 new miRNAs were identified in A. mongolicum including 53 conservative and 61 unconservative miRNAs, and 1393 target genes of 98 miRNAs were predicted. Seventeen miRNAs were found to be differentially expressed under drought stress, seven (amo-miR21, amo-miR62, amo-miR82, amo-miR5, amo-miR77, amo-miR44 and amo-miR17) of which were predicted to target on genes involved in drought tolerance. QRT-PCR analysis confirmed the expression changes of the seven drought related miRNAs in A. mongolicum. We then transformed the seven miRNAs into Arabidopsis thaliana plants, and three of them (amo-miR21, amo-miR5 and amo-miR62) were genetically stable. The three miRNAs demonstrated the same expression pattern in A. thaliana as that in A. mongolicum under drought stress. Findings from this study will better our understanding of the molecular mechanism of miRNAs in drought tolerance and promote molecular breeding of forage grass with improved adaption to drought

A lack of complementarity for water acquisition limits yield advantage of oats/vetch intercropping in a semi-arid condition

Oats (Avena sativa L.) and hairy vetch (Vicia villosa) are well adapted crop species for production in semi-arid environments, such as in Inner Mongolia, China, where due to variable rainfall, farmers do not apply fertilizer. We hypothesized that the use of a mixture of a cereal and a legume could enhance yields under these low input conditions, because integrating an N-fixing legume in the system could mitigate N limitation for the cereal and enhance its growth. A nine-year (2008–2016) field experiment was set up with three treatments: sole oats, sole vetch and oats/vetch strip intercropping. These cropping systems were grown continuously in the same plots, to allow accrual of long-term effects. Yields and water use were quantified in years 7–9 of the experiment (2014 to 2016). With a 50/50 ratio of the area sown to the two species, the intercropped oats had a relative yield of 0.59 and intercropped vetch had a relative yield of 0.45. Oats was the dominant crop characterized by a relative yield per plant of 1.18, compared to a relative yield per plant of vetch of 0.89. However, the land equivalent ratio (LER), expressing the comparative efficiency of land use in intercropping, and the water equivalent ratio (WER), the comparative system level water use efficiency of the intercrop relative to sole crops, were both not significantly different from one. Thus we reject the hypothesis that oat/vetch intercropping increases land productivity and water use efficiency. From differences in results in years with more rainfall and years with less rainfall, we infer that yields of both species are mostly limited by water availability. On average over the three years, the yield disadvantage of vetch was fully compensated by the yield advantage of oats, due to a lack of complementarity for water acquisition. This conclusion can be generalized to the testable prediction that species selection for productive intercropping should focus on achieving complementarity for traits that interact with the factor most constraining productivity, which was rainfall in this particular crop system under the conditions of the study.</p

Root plasticity and interspecific complementarity improve yields and water use efficiency of maize/soybean intercropping in a water-limited condition

Intercropping maize and soybean is renowned for improving crop production and resource use efficiencies. Interspecific competition and complementarity with respect to root plasticity is essential knowledge for understanding the mechanisms of overyielding and optimizing intercropping species selection. We conducted a three-year field experiment (2017–2019) to quantify land and water productivities in relation to above- and below-ground interspecific interactions, root growth and distribution under different nitrogen rates in maize/soybean intercropping. The land productivity in terms of land equivalent ratio (LER) in maize/soybean intercropping was 1.10 across all years and N rates. The yield increase in intercropped maize was mainly gained from an 45% increase in kernel numbers, while the yield loss of intercropped soybean was caused mainly by an 35% decrease in the pod numbers compared to sole cropping. The system level water use efficiency, defined as water equivalent ratio (WER) was also 1.10. Compared with sole stands, intercropped maize consumed more water during the vegetative stage, but intercropped soybean took up more during the reproductive stage. That indicated a temporal complementarity of water use in the intercrop, which benefited maize kernel formation and partially offset the negative shading effect of soybean grain filling. Soybean showed a marked increase in root length density (RLD). Compared to the produced aboveground biomass (DM) in the intercrop, the intercropped soybean invested more assimilates into root than shoot, as defined as root plasticity, the RLD/DM ratio of soybean in the intercrop was 76% more than sole system. However, the intercrop did not change root plasticity of maize. The overlap of maize and soybean roots, i.e. interspecific interaction interface, occurred mainly within the position between two border rows and at first soybean row. Under interspecific competition, soybean in the intercropping created both temporal and spatial differentiation for water uptake, which might be a key reason for enhancing intercropping land and water productivities. Our results contribute to understanding the mechanism of interspecific interaction for maximizing land and water productivities in rain-fed intercropping