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SummaryThe plant PTR/NRT1 (peptide transporter/nitrate transporter 1) gene family comprises di/ tripeptide and low-affinity nitrate transporters; some members also recognize other substrates such as carboxylates, phytohormones (auxin and abscisic acid), or defence compounds (glucosinolates). Little is known about the members of this gene family in rice (Oryza sativa L.). Here, we report the influence of altered OsPTR9 expression on nitrogen utilization efficiency, growth, and grain yield. OsPTR9 expression is regulated by exogenous nitrogen and by the daynight cycle. Elevated expression of OsPTR9 in transgenic rice plants resulted in enhanced ammonium uptake, promotion of lateral root formation and increased grain yield. On the other hand, down-regulation of OsPTR9 in a T-DNA insertion line (osptr9) and in OsPTR9-RNAi rice plants had the opposite effect. These results suggest that OsPTR9 might hold potential for improving nitrogen utilization efficiency and grain yield in rice breeding.

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The Pearson correlation coefficient is used by many researchers to construct complex financial networks. However, it is difficult to capture the structural characteristics of financial markets that have extreme fluctuations. To solve this problem, we resort to tail dependence networks. We first build the edge information of the stock network by adopting Pearson's correlation coefficient and the symmetrized Joe–Clayton copula model, respectively. By using the planar maximally filtered graph method, we filter the edge information, obtain Pearson's correlation coefficient and tail dependence network, and compare their efficiencies. The community structure of the constructed networks is investigated. We find that the global efficiency of tail‐dependent networks is higher than that of the Pearson correlation networks. Further analysis of the nodes in the upper‐ and lower‐tail dependence networks reveals that the European markets are more influential than Asian and African markets during a booming market and a recession market. In addition, different cliques are found in the two tail dependence networks. The finding indicates that financial risks will impact geographically adjacent markets.

Studies showed that the bromodomain binds to acetyl-lysines on histone tails, which is involved in deciphering the histone codes. BRD7, a novel bromodomain gene, is the first described bromodomain gene involved in nasopharyngeal carcinoma (NPC). Previous studies showed that ectopic expression of BRD7 inhibited cell growth and cell cycle progression from G1 to S phase in HNE1 cells (a NPC cell line) by transcriptionally regulating some cell cycle related genes including E2F3 gene. In the present study, we revealed the co-localization between acetylated H3 and BRD7 and found that the bromodomain of BRD7 is required for this co-localization. More importantly, wild-type BRD7 interacted with H3 peptide acetylated at Lys14, while the bromodomain deleted mutant lost this ability. We also found that the mutant BRD7 failed to regulate E2F3 promoter activity and inhibit cell cycle progression. These results indicated that the transcriptional regulation role of BRD7 was achieved by binding to acetylated histone H3 and that the bromodomain was essential for this role. In addition, no obvious changes were observed in the acetylated level of histone H3 after transfection with BRD7, indicating that chromatin remodeling, not chromatin modification, is the major mechanism of BRD7 mediated gene transcription. Taken together, the present work shed light on the fact that a novel bromodomain gene, BRD7, is of importance in transcriptional regulation and cellular events including cell cycle.

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The foreign exchange (FX) market, one of the important components of the financial market, is a typical complex system. In this paper, by resorting to the complex network method, we use the daily closing prices of 41 FX markets to build the dynamical networks and their minimum spanning tree (MST) maps by virtue of a moving window correlation coefficient. The properties of FX networks are characterized by the normalized tree length, node degree distributions, centrality measures and edge survival ratios. Empirical results show that: (i) the normalized tree length plays a role in identifying crises and is negatively correlated with the market return and volatility; (ii) 83% of FX networks follow power-law node degree distribution, which means that the FX market is a typical heterogeneous market, and a few hub nodes play key roles in the market; (iii) the highest centrality measures reveal that the USD, EUR and CNY are the three most powerful currencies in FX markets; and (iv) the edge survival ratio analysis implies that the FX structure is relatively stable.

Studies on investor sentiment are mostly focused on the stock market, but little attention has been paid to the effect of investor sentiment on the return of a specific industry. This paper constructs a proxy variable to examine the relationship between investor sentiment and the return of a specific industry, using the Principle Component Analysis, and finds that investor sentiment is positively correlated with the industry return of the current period and negatively correlated with that of one lag period; we classify investor sentiment as optimistic state and pessimistic state and find that optimistic investor sentiment has a positive effect on stock returns of most industries, while pessimistic investor sentiment has no effect on them; this paper further builds a two-state Markov regime switching model and finds that sentiment has different effect on different industries returns on different states of market.