Comparative physiological, transcriptomic, and WGCNA analyses reveal the key genes and regulatory pathways associated with drought tolerance in Tartary buckwheat.

Drought stress is one of the major abiotic stress factors that affect plant growth and crop productivity. Tartary buckwheat is a nutritionally balanced and flavonoid-rich pseudocereal crop and also has strong adaptability to different adverse environments including drought. However, little is known about its drought tolerance mechanism. In this study, we performed comparative physiological and transcriptomic analyses of two contrasting drought-resistant Tartary buckwheat genotypes under nature drought treatment in the reproductive stage. Under drought stress, the drought-tolerant genotype XZSN had significantly higher contents of relative water, proline, and soluble sugar, as well as lower relative electrolyte leakage in the leaves than the drought-susceptible LK3. A total of 5,058 (2,165 upregulated and 2,893 downregulated) and 5,182 (2,358 upregulated and 2,824 downregulated) potential drought-responsive genes were identified in XZSN and LK3 by transcriptome sequencing analysis, respectively. Among the potential drought-responsive genes of XZSN, 1,206 and 1,274 genes were identified to be potential positive and negative contributors for XZSN having higher drought resistance ability than LK3. Furthermore, 851 out of 1,206 positive drought-resistant genes were further identified to be the core drought-resistant genes of XZSN based on WGCNA analysis, and most of them were induced earlier and quicker by drought stress than those in LK3. Functional annotation of the 851 core drought-resistant genes found that a large number of stress-responsive genes were involved in TFs, abscisic acid (ABA) biosynthesis, signal transduction and response, non-ABA signal molecule biosynthesis, water holding, oxygen species scavenging, osmotic adjustment, cell damage prevention, and so on. Transcriptional regulatory network analyses identified the potential regulators of these drought-resistant functional genes and found that the HD-ZIP and MYB TFs might be the key downstream TFs of drought resistance in Tartary buckwheat. Taken together, these results indicated that the XZSN genotype was more drought-tolerant than the LK3 genotype as evidenced by triggering the rapid and dramatic transcriptional reprogramming of drought-resistant genes to reduce water loss, prevent cell damage, and so on. This research expands our current understanding of the drought tolerance mechanisms of Tartary buckwheat and provides important information for its further drought resistance research and variety breeding.

The prediction of fluctuation in the order-driven financial market.

Risk prediction is one of the important issues that draws much attention from academia and industry. And the fluctuation-absolute value of the change of price, is one of the indexes of risk. In this paper, we focus on the relationship between fluctuation and order volume. Based on the observation that the price would move when the volume of order changes, the prediction of price fluctuation can be converted into the prediction of order volume. Modelling the trader's behaviours-order placement and order cancellation, we propose an order-based fluctuation prediction model. And our model outperforms better than baseline in OKCoin and BTC-e datasets.

Danqi Tablet () Regulates Energy Metabolism in Ischemic Heart Rat Model through AMPK/SIRT1-PGC-1α Pathway.

OBJECTIVE: To investigate the cardioprotective effect of Danqi Tablet (DQT, ) on ischemic heart model rats and the regulative effect on energy metabolism through peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α). METHODS: Rat ischemic heart model was induced by ligation of left anterior descending coronary artery. Totally 40 Sprague-Dawley rats were randomly divided into sham group, model group, DQT group (1.5 mg/kg daily) and trimetazidine (TMZ) group (6.3 mg/kg daily) according to a random number table, 10 rats in each group. Twenty-eight days after continuous administration, cardiac function was assessed by echocardiography and the structures of myocardial cells were observed by hematoxylin-eosin staining. The level of adenosine triphosphate (ATP) in myocardial cells was measured by ATP assay kit. Expressions level of key transcriptional regulators, including PGC-1α, Sirtuin 1 (SIRT1), AMP-activated protein kinase (AMPK), and downstream targets of PGC-1α, such as mitofusin 1 (MFN1), mitofusin 2 (MFN2) and superoxide dismutase 2 (SOD2) were measured by Western blot. Expression level of PGC-1α was examined by immunohistochemical staining. RESULTS: The rat ischemic heart model was successfully induced and the heart function in model group was compromised. Compared with the model group, DQT exerted cardioprotective effects, up-regulated the ATP production in myocardial cells and inhibited the infiltration of inflammatory cells in the margin area of infarction of the myocardial tissues (P<0.01). The expressions of PGC-1α, SIRT1 and AMPK were increased in the DQT group (all P<0.05). Furthermore, the downstream targets, including MFN1, MFN2 and SOD2 were up-regulated (P<0.05 or P<0.01). Compared with the TMZ group, the expression levels of PGC-1α, MFN1 and SOD2 were increased by DQT treatment (P<0.05 or P<0.01). CONCLUSION: DQT regulated energy metabolism in rats with ischemic heart model through AMPK/SIRT1 -PGC-1α pathway. PGC-1α might serve as a promising target in the treatment of ischemic heart disease.

Vascular endothelial growth factor-loaded poly-lactic-co-glycolic acid nanoparticles with controlled release protect the dopaminergic neurons in Parkinson's rats.

Vascular endothelial growth factor (VEGF) had neuroprotective effects on dopaminergic (DA) neurons. In order to overcome the gastrointestinal digestion and bioaccessibility, VEGF was encapsulated with poly-lactic-co-glycolic acid nanospheres (NS) in order to prevent the VEGF degradation until its release. The caudal administration of VEGF and NS encapsulated VEGF at different doses (1.0, 10.0, and 100.0 ng/ml) on the rats with Parkinson's disease lesion was evaluated. Intravenous injected VEGF at the dose of 1 ng/ml displayed the strongest neuroprotective effect than other groups as well as the stereotaxic group. The NS encapsulated with VEGF can pass through blood-brain barrier and protect the DA neurons. There was no significant difference between intravenous injection method and stereotaxic method, while the first method is simpler and convenient. Injection of NS encapsulated with VEGF may become a valuable neurorescuing therapeutic approach for Parkinson's disease.

Anomaly detection in Bitcoin market via price return analysis.

The Bitcoin market becomes the focus of the economic market since its birth, and it has attracted wide attention from both academia and industry. Due to the absence of regulations in the Bitcoin market, it may be easier to bring some kinds of illegal behaviors. Thus, it raises an interesting question: Is there abnormity or illegal behavior in Bitcoin platforms? To answer this question, we investigate the abnormity in five leading Bitcoin platforms. By analyzing the financial index, i.e. the normalized logarithmic price return, we find that the properties of price return in bitFlyer are completely different from others. To find the possible reasons, we find that the abnormal ask price and bid price appear simultaneously in bitFlyer, which may be potentially linked to either price manipulation or money laundering. It verifies our conjecture that there may be abnormity or price manipulation in Bitcoin platforms. Furthermore, our findings in price return could also provide an innovative and effective method to detect the abnormity in Bitcoin platforms.

Market Confidence Predicts Stock Price: Beyond Supply and Demand.

Stock price prediction is an important and challenging problem in stock market analysis. Existing prediction methods either exploit autocorrelation of stock price and its correlation with the supply and demand of stock, or explore predictive indictors exogenous to stock market. In this paper, using transaction record of stocks with identifier of traders, we introduce an index to characterize market confidence, i.e., the ratio of the number of traders who is active in two successive trading days to the number of active traders in a certain trading day. Strong Granger causality is found between the index of market confidence and stock price. We further predict stock price by incorporating the index of market confidence into a neural network based on time series of stock price. Experimental results on 50 stocks in two Chinese Stock Exchanges demonstrate that the accuracy of stock price prediction is significantly improved by the inclusion of the market confidence index. This study sheds light on using cross-day trading behavior to characterize market confidence and to predict stock price.

[Research on rat models of acute liver injury with syndrome of liver depression and spleen deficiency].

This paper aimed to establish animal models which are suitable for the activity found, efficacy evaluation of herbs resistant to acute liver injury with syndrome of liver depression and spleen deficiency and new drug research and development based on corresponding of formula and syndrome. The symptoms that are suitable for evaluating the rat models of acute liver injury with syndrome of liver depression and spleen deficiency were extracted according to the evolution rule of the etiology and pathogenesis in traditional Chinese medicine and the modern pathological mechanism. Xiaoyao pill and silibin meglumine tablets were used as drug counter evidence for models in accordence with the principle of consistence of prescription and syndrome. Rats model were fed with high-lipid and low-protein fodder of different proportion and induced by intraperitoneal injection with pig serum, intragastric administration with edible alcohol once a day for 7 days. Daily record of body weight, daily food intake and daily water intake were conducted day after day in experimental session. Symptoms were also observed and evaluated by score at the same time. The contents of ALT, AST, PA, TBIL and TBA in serum were detected and histopathological changes of liver were checked at the ending of experiment. Obvious acute liver injury occurred to all rats in model groups at 1 week following model induction. Both main symptoms and secondary symptoms were consistent with syndrome manifestation of liver depression and spleen deficiency. Compared with normal control group, the activity of ALT,AST and contents of TBIL,TBA in serum increased and the content of PA decreased. Liver tissue pathological morphology showed inflammatory cells infiltration, eosinophilic or eosinophilic adipose change in hepatocytes of rats in model groups. All the above lesions manifestation could be improved by drug counterevidence. By the disproof of medicine, rat models of acute liver injury with syndrome of liver depression and spleen deficiency could be induced by fed with high-lipid and low-protein fodder which contained 89.5% cornstarch, 10% lard and 0.5% cholesterol, intraperitoneal injected with pig serum, intragastric administrated with edible alcohol for 7 days. The rat models with a low mortality could be induced in a short time and animal status were similar to syndrome performance of patients. So the rats models are suitable for the activity found, efficacy evaluation and drug discovery of herbs resistant to acute liver injury with syndrome of liver depression and spleen deficiency, and also can be used in the research of correlation between prescription and syndrome and its mechanism.

Trading network predicts stock price.

Stock price prediction is an important and challenging problem for studying financial markets. Existing studies are mainly based on the time series of stock price or the operation performance of listed company. In this paper, we propose to predict stock price based on investors' trading behavior. For each stock, we characterize the daily trading relationship among its investors using a trading network. We then classify the nodes of trading network into three roles according to their connectivity pattern. Strong Granger causality is found between stock price and trading relationship indices, i.e., the fraction of trading relationship among nodes with different roles. We further predict stock price by incorporating these trading relationship indices into a neural network based on time series of stock price. Experimental results on 51 stocks in two Chinese Stock Exchanges demonstrate the accuracy of stock price prediction is significantly improved by the inclusion of trading relationship indices.

Degree-strength correlation reveals anomalous trading behavior.

Manipulation is an important issue for both developed and emerging stock markets. Many efforts have been made to detect manipulation in stock markets. However, it is still an open problem to identify the fraudulent traders, especially when they collude with each other. In this paper, we focus on the problem of identifying the anomalous traders using the transaction data of eight manipulated stocks and forty-four non-manipulated stocks during a one-year period. By analyzing the trading networks of stocks, we find that the trading networks of manipulated stocks exhibit significantly higher degree-strength correlation than the trading networks of non-manipulated stocks and the randomized trading networks. We further propose a method to detect anomalous traders of manipulated stocks based on statistical significance analysis of degree-strength correlation. Experimental results demonstrate that our method is effective at distinguishing the manipulated stocks from non-manipulated ones. Our method outperforms the traditional weight-threshold method at identifying the anomalous traders in manipulated stocks. More importantly, our method is difficult to be fooled by colluded traders.