Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

<p>Abstract</p> <p>Background</p> <p>Microarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data.</p> <p>Results</p> <p>In this study, we propose a new discretization method "bikmeans", and compare its performance with four other widely-used discretization methods using different datasets, modeling algorithms and number of intervals. Sensitivities, specificities and total accuracies were calculated and statistical analysis was carried out. Bikmeans method always gave high total accuracies.</p> <p>Conclusions</p> <p>Our results indicate that proper discretization methods can consistently improve gene regulatory network inference independent of network modeling algorithms and datasets. Our new method, bikmeans, resulted in significant better total accuracies than other methods.</p

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

Sequential recommendations have made great strides in accurately predicting the future behavior of users. However, seeking accuracy alone may bring side effects such as unfair and overspecialized recommendation results. In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity. On the one hand, it aims to provide fairer recommendations whose preference distributions are consistent with users' historical behaviors. On the other hand, it can improve the diversity of recommendations to a certain degree. But existing methods for calibration have mainly relied on the post-processing on the candidate lists, which require more computation time in generating recommendations. In addition, they fail to establish the relationship between accuracy and calibration, leading to the limitation of accuracy. To handle these problems, we propose an end-to-end framework to provide both accurate and calibrated recommendations for sequential recommendation. We design an objective function to calibrate the interests between recommendation lists and historical behaviors. We also provide distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. In addition, we design a decoupled-aggregated model to improve the recommendation. The framework assigns two objectives to two individual sequence encoders, and aggregates the outputs by extracting useful information. Experiments on benchmark datasets validate the effectiveness of our proposed model

GsAPK, an ABA-Activated and Calcium-Independent SnRK2-Type Kinase from G. soja, Mediates the Regulation of Plant Tolerance to Salinity and ABA Stress

Plant Snf1 (sucrose non-fermenting-1) related protein kinase (SnRK), a subfamily of serine/threonine kinases, has been implicated as a crucial upstream regulator of ABA and osmotic signaling as in many other signaling cascades. In this paper, we have isolated a novel plant specific ABA activated calcium independent protein kinase (GsAPK) from a highly salt tolerant plant, Glycine soja (50109), which is a member of the SnRK2 family. Subcellular localization studies using GFP fusion protein indicated that GsAPK is localized in the plasma membrane. We found that autophosphorylation and Myelin Basis Protein phosphorylation activity of GsAPK is only activated by ABA and the kinase activity also was observed when calcium was replaced by EGTA, suggesting its independence of calcium in enzyme activity. We also found that cold, salinity, drought, and ABA stress alter GsAPK gene transcripts and heterogonous overexpression of GsAPK in Arabidopsis alters plant tolerance to high salinity and ABA stress. In summary, we demonstrated that GsAPK is a Glycine soja ABA activated calcium independent SnRK-type kinase presumably involved in ABA mediated stress signal transduction

Identification and determination of the major constituents in traditional Chinese medicine Longdan Xiegan Pill by HPLC-DAD-ESI-MS

AbstractA novel and sensitive HPLC-UV method has been developed for the simultaneous determination of twelve major compounds in Longdan Xiegan Pill. The chemical profile of the twelve compounds, including geniposidic acid (1), geniposide(2), gentiopicroside(3), liquiritin(4), crocin(5), baicalin(6), wogonoside(7), baicalein(8), glycyrrhizic acid (9), wogonin (10), oroxylin A (11) and aristolochic acid A (12), was acquired using high-performance liquid chromatography-diode array detector coupled with an electrospray tandem mass spectrometer (HPLC-DAD-ESI-MS). The analysis was performed on a Dikma Platisil ODS C18 column (250mm × 4. 6mm, 5μm) with a gradient solvent system of acetonitrile-0. 1% aqueous formic acid. The validation was carried out and the linearities (r>0. 9996), repeatability (RSD<1. 8%), intra- and inter-day precision (RSD< 1. 3%), and recoveries (ranging from 96. 6% to 103. 4%) were acceptable. The limits of detection (LOD) of these compounds ranged from 0.29 to 4. 17ng. Aristolochic acid A, which is the toxic ingredient, was not detected in all the batches of Longdan Xiegan Pill. Furthermore, hierarchical cluster analysis was used to evaluate the variation of the herbal prescription. The proposed method is simple, effective and suitable for the quality control of this traditional Chinese medicine (TCM)

Linking nutrient strategies with plant size along a grazing gradient: Evidence from Leymus chinensis in a natural pasture

AbstractStudying the changes in nutrient use strategies induced by grazing can provide insight into the process of grassland degradation and is important for improving grassland quality and enhancing ecosystem function. Dominant species in meadow steppe can optimize their use of limiting resources; however, the regulation of nutrient use strategies across grazing gradients is not fully understood. Therefore, in this study, we report an in situ study in which the impact of grazing rates on nutrient use strategies of Leymus chinensis, the dominant plant species in eastern Eurasian temperate steppes, was investigated. We conducted a large randomized controlled experiment (conducted continuously for five years in grassland plots in a natural pasture in Hailar, eastern Mongolia Plateau, China) to assess the effects of grazing rate treatments (0.00, 0.23, 0.34, 0.46, 0.69, and 0.92 adult cattle unit (AU) ha−1) on L. chinensis along a grazing gradient and employed a random sampling approach to compare the accumulation, allocation, and stoichiometry of C, N, and P in leaves and stems. Our findings demonstrated the follows: (i) The height of L. chinensis decreased with an increase in the grazing gradient, and the concentrations of C, N, and P significantly increased; (ii) the accumulation of C, N, and P per individual was negatively correlated with the concentration of aboveground tissues, suggesting that there was a tradeoff in L. chinensis between nutrient accumulation and concentration at the individual scale; (iii) the leaf-to-stem ratio of C, N, and P accumulation increased with grazing intensity, indicating a tradeoff in nutrient allocation and plant size at the individual plant level; and (iv) grazing rates were negatively correlated with the ratios of C:N and C:P in the stem; however, these ratios in leaves significantly increased with grazing intensity. Our findings suggest that L. chinensis in meadow steppe adapts to grazing disturbance through tradeoffs between plant size and nutrient use strategies. Moreover, our results imply that grazing produces a compensatory effect on nutrient use efficiency between the stems and leaves of L. chinensis