Quantifying N response and N use efficiency in Rice-Wheat (RW) cropping systems under different water management

About 0·10 of the food supply in China is produced in rice¿wheat (RW) cropping systems. In recent decades, nitrogen (N) input associated with intensification has increased much more rapidly than N use in these systems. The resulting nitrogen surplus increases the risk of environmental pollution as well as production costs. Limited information on N dynamics in RW systems in relation to water management hampers development of management practices leading to more efficient use of nitrogen and water. The present work studied the effects of N and water management on yields of rice and wheat, and nitrogen use efficiencies (NUEs) in RW systems. A RW field experiment with nitrogen rates from 0 to 300 kg N/ha with continuously flooded and intermittently irrigated rice crops was carried out at the Jiangpu experimental station of Nanjing Agricultural University of China from 2002 to 2004 to identify improved nitrogen management practices in terms of land productivity and NUE. Nitrogen uptake by rice and wheat increased with increasing N rates, while agronomic NUE (kg grain/kg N applied) declined at rates exceeding 150 kg N/ha. The highest combined grain yields of rice and wheat were obtained at 150 and 300 kg N/ha per season in rice and wheat, respectively. Carry-over of residual N from rice to the subsequent wheat crop was limited, consistent with low soil nitrate after rice harvest. Total soil N hardly changed during the experiment, while soil nitrate was much lower after wheat than after rice harvest. Water management did not affect yield and N uptake by rice, but apparent N recovery was higher under intermittent irrigation (II). In one season, II management in rice resulted in higher yield and N uptake in the subsequent wheat season. Uptake of indigenous soil N was much higher in rice than in wheat, while in rice it was much higher than values reported in the literature, which may have consequences for nitrogen fertilizer recommendations based on indigenous N suppl

Variations in protein concentration and nitrogen sources in different positions of grain in wheat

The distribution patterns of total protein and protein components in different layers of wheat grain were investigated using the pearling technique, and the sources of different protein components and pearling fractions were identified using (15)N isotope tracing methods. It was found that N absorbed from jointing to anthesis (JA) and remobilized to the grain after anthesis was the principal source of grain N, especially in the outer layer. For albumin and globulin, the amount of N absorbed during different stages all showed a decreasing trend from the surface layer to the center part. Whereas, for globulin and glutenin, the N absorbed after anthesis accounted for the main part indicating that for storage protein, the utilization of N assimilated after anthesis is greater than that of the stored N assimilated before anthesis. It is concluded that manipulation of the N application rate during different growth stages could be an effective approach to modulate the distribution of protein fractions in pearled grains for specific end-uses

Metabolic Profiling Study of Yang Deficiency Syndrome in Hepatocellular Carcinoma by H

This study proposes a 1H NMR-based metabonomic approach to explore the biochemical characteristics of Yang deficiency syndrome in hepatocellular carcinoma (HCC) based on serum metabolic profiling. Serum samples from 21 cases of Yang deficiency syndrome HCC patients (YDS-HCC) and 21 cases of non-Yang deficiency syndrome HCC patients (NYDS-HCC) were analyzed using 1H NMR spectroscopy and partial least squares discriminant analysis (PLS-DA) was applied to visualize the variation patterns in metabolic profiling of sera from different groups. The differential metabolites were identified and the biochemical characteristics were analyzed. We found that the intensities of six metabolites (LDL/VLDL, isoleucine, lactate, lipids, choline, and glucose/sugars) in serum of Yang deficiency syndrome patients were lower than those of non-Yang deficiency syndrome patients. It implies that multiple metabolisms, mainly including lipid, amino acid, and energy metabolisms, are unbalanced or weakened in Yang deficiency syndrome patients with HCC. The decreased intensities of metabolites including LDL/VLDL, isoleucine, lactate, lipids, choline, and glucose/sugars in serum may be the distinctive metabolic variations of Yang deficiency syndrome patients with HCC. And these metabolites may be potential biomarkers for diagnosis of Yang deficiency syndrome in HCC

Mechano-stimulated modifications in the chloroplast antioxidant system and proteome changes are associated with cold response in wheat

BACKGROUND: Mechanical wounding can cause morphological and developmental changes in plants, which may affect the responses to abiotic stresses. However, the mechano-stimulation triggered regulation network remains elusive. Here, the mechano-stimulation was applied at two different times during the growth period of wheat before exposing the plants to cold stress (5.6 °C lower temperature than the ambient temperature, viz., 5.0 °C) at the jointing stage. RESULTS: Results showed that mechano-stimulation at the Zadoks growth stage 26 activated the antioxidant system, and substantially, maintained the homeostasis of reactive oxygen species. In turn, the stimulation improved the electron transport and photosynthetic rate of wheat plants exposed to cold stress at the jointing stage. Proteomic and transcriptional analyses revealed that the oxidative stress defense, ATP synthesis, and photosynthesis-related proteins and genes were similarly modulated by mechano-stimulation and the cold stress. CONCLUSIONS: It was concluded that mechano-stimulated modifications of the chloroplast antioxidant system and proteome changes are related to cold tolerance in wheat. The findings might provide deeper insights into roles of reactive oxygen species in mechano-stimulated cold tolerance of photosynthetic apparatus, and be helpful to explore novel approaches to mitigate the impacts of low temperature occurring at critical developmental stages. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12870-015-0610-6) contains supplementary material, which is available to authorized users

Identification of rifampin-regulated functional modules and related microRNAs in human hepatocytes based on the protein interaction network

BACKGROUND: In combination with gene expression profiles, the protein interaction network (PIN) constructs a dynamic network that includes multiple functional modules. Previous studies have demonstrated that rifampin can influence drug metabolism by regulating drug-metabolizing enzymes, transporters, and microRNAs (miRNAs). Rifampin induces gene expression, at least in part, by activating the pregnane X receptor (PXR), which induces gene expression; however, the impact of rifampin on global gene regulation has not been examined under the molecular network frameworks. METHODS: In this study, we extracted rifampin-induced significant differentially expressed genes (SDG) based on the gene expression profile. By integrating the SDG and human protein interaction network (HPIN), we constructed the rifampin-regulated protein interaction network (RrPIN). Based on gene expression measurements, we extracted a subnetwork that showed enriched changes in molecular activity. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG), we identified the crucial rifampin-regulated biological pathways and associated genes. In addition, genes targeted by miRNAs that were significantly differentially expressed in the miRNA expression profile were extracted based on the miRNA-gene prediction tools. The miRNA-regulated PIN was further constructed using associated genes and miRNAs. For each miRNA, we further evaluated the potential impact by the gene interaction network using pathway analysis. RESULTS AND DISCCUSSION: We extracted the functional modules, which included 84 genes and 89 interactions, from the RrPIN, and identified 19 key rifampin-response genes that are associated with seven function pathways that include drug response and metabolism, and cancer pathways; many of the pathways were supported by previous studies. In addition, we identified that a set of 6 genes (CAV1, CREBBP, SMAD3, TRAF2, KBKG, and THBS1) functioning as gene hubs in the subnetworks that are regulated by rifampin. It is also suggested that 12 differentially expressed miRNAs were associated with 6 biological pathways. CONCLUSIONS: Our results suggest that rifampin contributes to changes in the expression of genes by regulating key molecules in the protein interaction networks. This study offers valuable insights into rifampin-induced biological mechanisms at the level of miRNAs, genes and proteins

Hydrogen Peroxide and Abscisic Acid Mediate Salicylic Acid-Induced Freezing Tolerance in Wheat

Salicylic acid (SA) can induce plant resistance to biotic and abiotic stresses through cross talk with other signaling molecules, whereas the interaction between hydrogen peroxide (H2O2) and abscisic acid (ABA) in response to SA signal is far from clear. Here, we focused on the roles and interactions of H2O2 and ABA in SA-induced freezing tolerance in wheat plants. Exogenous SA pretreatment significantly induced freezing tolerance of wheat via maintaining relatively higher dark-adapted maximum photosystem II quantum yield, electron transport rates, less cell membrane damage. Exogenous SA induced the accumulation of endogenous H2O2 and ABA. Endogenous H2O2 accumulation in the apoplast was triggered by both cell wall peroxidase and membrane-linked NADPH oxidase. The pharmacological study indicated that pretreatment with dimethylthiourea (H2O2 scavenger) completely abolished SA-induced freezing tolerance and ABA synthesis, while pretreatment with fluridone (ABA biosynthesis inhibitor) reduced H2O2 accumulation by inhibiting NADPH oxidase encoding genes expression and partially counteracted SA-induced freezing tolerance. These findings demonstrate that endogenous H2O2 and ABA signaling may form a positive feedback loop to mediate SA-induced freezing tolerance in wheat

Beta Distribution-Based Cross-Entropy for Feature Selection

Analysis of high-dimensional data is a challenge in machine learning and data mining. Feature selection plays an important role in dealing with high-dimensional data for improvement of predictive accuracy, as well as better interpretation of the data. Frequently used evaluation functions for feature selection include resampling methods such as cross-validation, which show an advantage in predictive accuracy. However, these conventional methods are not only computationally expensive, but also tend to be over-optimistic. We propose a novel cross-entropy which is based on beta distribution for feature selection. In beta distribution-based cross-entropy (BetaDCE) for feature selection, the probability density is estimated by beta distribution and the cross-entropy is computed by the expected value of beta distribution, so that the generalization ability can be estimated more precisely than conventional methods where the probability density is learnt from data. Analysis of the generalization ability of BetaDCE revealed that it was a trade-off between bias and variance. The robustness of BetaDCE was demonstrated by experiments on three types of data. In the exclusive or-like (XOR-like) dataset, the false discovery rate of BetaDCE was significantly smaller than that of other methods. For the leukemia dataset, the area under the curve (AUC) of BetaDCE on the test set was 0.93 with only four selected features, which indicated that BetaDCE not only detected the irrelevant and redundant features precisely, but also more accurately predicted the class labels with a smaller number of features than the original method, whose AUC was 0.83 with 50 features. In the metabonomic dataset, the overall AUC of prediction with features selected by BetaDCE was significantly larger than that by the original reported method. Therefore, BetaDCE can be used as a general and efficient framework for feature selection

Mirrored coprime array design using sum‐difference coarray optimisation

Abstract The sum‐difference coarray (SDCA) is the union of the sum coarray (SCA) and difference coarray (DCA), which has higher degrees‐of‐freedom (DOF) than that of the DCA, resulting in a better direction‐of‐arrival (DOA) estimation performance. However, existing passive sparse arrays require spatial and temporal information to construct SDCA. In this study, a mirrored coprime array (MCA) is designed to implement SDCA using only spatial information. First, the SCA and DCA are recovered from the vectorised covariance matrix via the transform matrix. A Tikhonov regularisation method is proposed to reduce the rank‐deficiency effect of the transform matrix. The SCA has the potential to fill the holes in the DCA by adjusting the mirror position since the mirror determines the virtual sensor locations of the SCA. Then, the closed‐form expressions of the mirror position and virtual array aperture are derived for the hole‐free SDCA. The consecutive lags of the optimised SDCA are much larger than those of the DCA, significantly increasing the DOF. Numerical simulations verify that the MCA outperforms the non‐mirrored one with respect to the DOA estimation accuracy and resolution