Search

Rosa roxburghii tratt (R. roxburghii) is an important plant resource that is widely distributed in the southwest of China and favored by consumers due to its high nutritional value and healthy functions. Meanwhile, it is a traditional edible and medicinal plant in China. With the deepening research of R. roxburghii, more and more bioactive components and its health care and medicinal value have been discovered and developed in recent years. This review summarizes and discusses the recent advances on main active ingredients such as vitamin, protein, amino acid, superoxide dismutase, polysaccharide, polyphenol, flavonoid, triterpenoid and mineral, and pharmacological activities including antioxidant activity, immunomodulatory activity, anti-tumor activity, glucose and lipid metabolism regulation, anti-radiation effect, detoxification effect, and viscera protection of R. roxbughii, as well as its development and utilization. The research status and existing problems of R. roxburghii development and quality control are also briefly introduced. This review ends with some suggestions on the perspectives and directions for future research and potential applications of R. roxbughii. [ABSTRACT FROM AUTHOR]

The late spring rainfall may account for 15% of the annual total rainfall, which is crucial to early planting in southeastern China. A better understanding of the precipitation variations in the late spring and its predictability not only greatly increase our knowledge of related mechanisms, but it also benefits society and the economy. Four models participating in the North American Multi-Model Ensemble (NMME) were selected to study their abilities to forecast the late spring rainfall over southeastern China and the major sources of heavy rainfall from the perspective of the sea surface temperature (SST) field. We found that the models have better abilities to forecast the heavy rainfall over the middle and lower reaches of the Yangtze River region (MLYZR) with only a 1-month lead time, but they failed for a 3-month lead time since the occurrence of the heavy rainfall was inconsistent with the observations. The observations indicate that the warm SST anomalies in the tropical eastern Indian Ocean are vital to the simultaneously heavy rainfall in the MLYZR in May, but an El Niño event is not a necessary condition for determining the heavy rainfall over the MLYZR. The heavy rainfall over the MLYZR in May is always accompanied by warming of the northeastern Indian Ocean and of the northeastern South China Sea (NSCS) from April to May in the models and observations, respectively. In the models, El Niño events may promote the warming processes over the northeastern Indian Ocean, which leads to heavy rainfall in the MLYZR. However, in the real world, El Niño events are not the main reason for the warming of the NSCS, and further research on the causes of this warming is still needed. [ABSTRACT FROM AUTHOR]

Classical swine fever (CSF), caused by classical swine fever virus (CSFV), is a severe disease that causes huge economic losses in the swine industry worldwide. In China, CSF has been under control due to extensive vaccination since 1954. However, there are still sporadic CSF outbreaks in China. Here, we isolated 27 CSFV strains from three Chinese provinces (Shaanxi, Gansu, and Ningxia) from 2011 to 2018. Phylogenetic analysis based on the full-length envelope glycoprotein E2 coding region revealed that 25 out of 27 CSFV isolates clustered within subgroups 2.1 and 2.2, while two strains from Gansu belonged to subgroup 1.1. The sequence identity among these 27 isolates varied from 79.3% to 99.8% (nucleotides) and from 83.1% to 99.7% (amino acids). Further analysis based on the E2 amino acid sequences showed that these new isolates have consistent amino acid substitutions, including R31K and N34S. [ABSTRACT FROM AUTHOR]

Background: Gut microbiota play important roles in their co-evolution with mammals. However, little is understood about gut bacterial community of Tibetan sheep compared with other sheep breeds. In this study, we investigated the gut bacterial community in 4 different sheep breeds living in the Qinghai-Tibetan Plateau (QTP) of China using high-throughput sequencing (HTS) technique. Results: The results suggested that bacterial community abundance and breeds diversity of Tibetan sheep (TS) were significantly lower than that of the other three breeds of sheep [Dorset sheep (DrS), Dorper sheep (DrS) and Small Tail Han sheep (STHS)] (p < 0.05). Principal coordinates analysis (PCoA) and nonmetric multidimensional scaling (NMDS) analysis indicated that microbiome composition of TS was significantly different from that of other three sheep breeds (p < 0.01). Firmicutes was the most predominant microbial phylum in the gut, followed by Bacteroidetes. The gut bacterial community of TS showed higher proportions of phylum Spirochaetes, Proteobacteria and Verrucomicrobia, compared to the other three sheep breeds, but the Deferribacteres was absent in TS. At the genus level, Treponema, Succinivibrio, 5-7 N15 and Prevotella showed significantly higher abundance in TS than in the other three sheep breeds (p < 0.05). Conclusions: In this study, we first employed HTS to understand the gut microbiomes among different sheep breeds in QTP of China. [ABSTRACT FROM AUTHOR]

In recent years, with the deepening of China's electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model. [ABSTRACT FROM AUTHOR]

Dongjing reservoir with storage capacity of 955 million m and 150 m dam height had been set up in Guizhou province, southeastern China on May in 2005. After filling with water in August 20, 2009, the reservoir-induced earthquake in 20 km took place first in September 2009 at the 440 m water level. When the water level changes, the number of earthquakes is increased rapidly. On January 17, 2010, the largest M 4.4 earthquake with depth of 7 km has happen and month frequency achieved 21 events at the highest water level. M 4.4 earthquake caused rock collapse with the disaster of killed six people and nine injure. After our investigation and study, the reason of higher epicentral intensity of earthquake was the surface effect of near-field elastic wave transmission. The disaster of rock falls certainly depended on the very very shallow earthquake, the height of valley and fault. Comparing as same magnitude of natural earthquake, very shallow earthquake increased 1-2° of epicentral intensity I, more than twice amplitude of S-wave at 200 m height of valley and the largest displacement on fault. The superposition of three factors has increased the epicentral intensity of earthquake and directly caused rock collapse with the disaster of killed six people and nine injure. [ABSTRACT FROM AUTHOR]

After Wenchuan Ms8.O earthquake, the surface fractures have been investigated in Beichuan City and Yingxiu Town. It was found that the Beichuan-Yingxiu earthquake fracture zone trends northeastward, dipping to northwest. The earthquake fracture zone is mainly characterized by thrust faulting with small amounts of strike-slip movement, and demonstrating various complexities for different areas. The phenomena related to dextral strike-slip displacement are found in Beichuan City and those related to left-lateral strike-slip movement in Yingxiu Town. The compressive shortening of the two surface fracture zones in Beichuan City is 2.8-3.9 m and the left-lateral strike-slip displacement of the surface fractures in Yingxiu Town is 0.52 m. [ABSTRACT FROM AUTHOR]

Our study matches multiple rounds of China Family Panel Studies (CFPS) data from 2012 to 2020 with regional macro fiscal and economic data, and explores the impact of regional fiscal decentralization on citizens' satisfaction with public services from the subjective perception of individual respondents. The study found that the increase in regional fiscal decentralization has a significant positive effect on improving the satisfaction of citizens with public services. However, due to differences in individual characteristics among the surveyed individuals, this effect has obvious heterogeneity. Mechanism analysis showed that the enhancement of local government's awareness of public service supply and the increase of regional fiscal transparency have a promoting effect on the improvement of citizens' satisfaction with public services. From the perspective of the convergence in the perception of public service satisfaction among citizens within the same province, the increase of fiscal decentralization has not effectively promoted the equalization development of public services at the provincial level in China. • Explored the impact of fiscal decentralization on citizens' satisfaction with public services from subjective perception • The increase of fiscal decentralization has a positive effect on improving the satisfaction of citizens with public services • The enhancement of government's awareness and fiscal transparency can promote increase of satisfaction with public services • The increase of fiscal decentralization has not effectively promoted equalization of public services at the provincial level [ABSTRACT FROM AUTHOR]

The electricity consumption forecasting (ECF) technology plays a crucial role in the electricity market. The support vector regression (SVR) is a nonlinear prediction model that can be used for ECF. The electricity consumption (EC) data are usually nonlinear and non-Gaussian and present outliers. The traditional SVR with the mean-square error (MSE), however, is insensitive to outliers and cannot correctly represent the statistical information of errors in non-Gaussian situations. To address this problem, a novel robust forecasting method is developed in this work by using the mixture maximum correntropy criterion (MMCC). The MMCC, as a novel cost function of information theoretic, can be used to solve non-Gaussian signal processing; therefore, in the original SVR, the MSE is replaced by the MMCC to develop a novel robust SVR method (called MMCCSVR) for ECF. Besides, the factors influencing users' EC are investigated by a data statistical analysis method. We find that the historical temperature and historical EC are the main factors affecting future EC, and thus these two factors are used as the input in the proposed model. Finally, real EC data from a shopping mall in Guangzhou, China, are utilized to test the proposed ECF method. The forecasting results show that the proposed ECF method can effectively improve the accuracy of ECF compared with the traditional SVR and other forecasting algorithms. [ABSTRACT FROM AUTHOR]

• Combined with the error reconstruction rate, an adaptive VMD technology is developed and used for data preprocessing. • Two mainstream deep learning methods (LSTM, DBN) are used as sub-series predictors, and the classic PSO algorithm is utilized to determine the number of neurons in the DBN network. • A novel hybrid prediction model based on the PSO-DBN nonlinear combination mechanism is proposed. Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation and increasing the competitiveness of power market. In this paper, a novel hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability. Firstly, the variational mode decomposition (VMD) technique is employed to decompose the original wind power series to extract local features. Then, the long short-term memory neural networks (LSTM) and deep belief networks based on particle swarm optimization (PSO-DBN) are utilized to construct sub-series prediction models. Finally, the multiple sub-series forecasting models are integrated by a nonlinear weighted combination method based on PSO-DBN to construct a hybrid model for short-term wind power forecasting. To verify the performance of the developed forecasting model, wind speed data of 10 min from a wind farm in Shaanxi Dingbian, China are selected as case studies. The results show that the proposed method in this paper is more effective than other existing methods. [ABSTRACT FROM AUTHOR]

Nowadays, various wind power forecasting methods have been developed to improve wind power utilization. Most of these techniques are designed based on the mean square error (MSE) loss, which are very suitable for the assumption that the error distribution obeys the Gaussian distribution. However, there are many outliers in real wind power data due to many uncertain factors such as weather, temperature, and other random factors. Meanwhile, the highly nonlinear process of converting wind energy into wind power may changes the statistical characteristics of errors. Therefore, the prediction model established based on the traditional MSE loss may lead to unsatisfactory results. As a result, a robust short-term wind power hybrid forecasting model based on Long Short-term Memory (LSTM) neural network with Correntropy combining an improved variational mode decomposition (IVMD) and Sample Entropy (SE) is proposed in this work. The IVMD in which the parameter K in the IVMD is determined by the Maximum Correntropy Criterion (MCC) is used to decompose the original wind power data and the decomposed subseries is reconstructed by SE to improve the prediction efficiency. Then the MCC is also utilized to replace the MSE in the classic LSTM network to develop a novel robust hybrid model to forecast the wind power. Finally, four experiments were conducted using real data from two wind farms in China at different sampling intervals to evaluate the effectiveness of the proposed method, and the results show that proposed method is more effective than other traditional methods. • An adaptive VMD decomposition algorithm with MCC is proposed. • A new data preprocessing model is proposed by combining with the sample entropy. • The LSTM network with MCC loss is proposed in the forecasting process. • A novel IVMD-SE-MCC-LSTM model is proposed for short-term wind power forecasting. • Experiments are performed to evaluate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

• ORFV genes concentrated in SA00-like and IA82-like clusters. • East and south asia are the major transmission site of ORFV. • Specific motifs suggested multiple survival strategies in ORFV. • The potential epitopes can be used for vaccine design. Contagious ecthyma (CE) is an acute infectious zoonosis caused by orf virus (ORFV) that mainly infects sheep and goats and causes obvious lesions and low market value of livestock, resulting in huge economic losses for farmers. In this study, two strains of ORFV were isolated from Shaanxi Province and Yunnan Province in China, named FX and LX. The two ORFVs were located in the major clades of domestic strains respectively, and exhibited distinct sequence homology. We analyzed the genetic data of core genes (B2L, F1L, VIR, ORF109) and variable genes (GIF, ORF125 and vIL-10) of ORFV to investigate its epidemiological and evolutionary characteristics. The sequences from 2007 to 2018 constituted the majority of the viral population, predominantly concentrated in India and China. Most genes were clustered into SA00-like type and IA82-like type, and the hotspots in East and South Asia were identified in the ORFV transmission trajectories. For these genes, VIR had the highest substitution rate of 4.85 × 10−4, both VIR and vIL-10 suffered the positive selection pressure during ORFV evolution. Many motifs associated with viral survival were distributed among ORFVs. In addition, some possible viral epitopes have been predicted, which still require validation in vivo and in vitro. This work gives more insight into the prevalence and phylogenetic relationships of existing orf viruses and facilitate better vaccine design. [ABSTRACT FROM AUTHOR]