Gardnerkarstensen2962

. fruticosa responded to low-water and fertilizer-shortage by upregulating defensive mechanism to avoid damage induced by such deficiencies. Finally, our findings provide effective strategies for revegetation of coal-contaminated spoils with A. fruticosa using appropriate doses of water and N-P fertilizers.BACKGROUND Genomic selection (GS) or genomic prediction is considered as a promising approach to accelerate tree breeding and increase genetic gain by shortening breeding cycle, but the efforts to develop routines for operational breeding are so far limited. We investigated the predictive ability (PA) of GS based on 484 progeny trees from 62 half-sib families in Norway spruce (Picea abies (L.) Karst.) for wood density, modulus of elasticity (MOE) and microfibril angle (MFA) measured with SilviScan, as well as for measurements on standing trees by Pilodyn and Hitman instruments. RESULTS GS predictive abilities were comparable with those based on pedigree-based prediction. Marker-based PAs were generally 25-30% higher for traits density, MFA and MOE measured with SilviScan than for their respective standing tree-based method which measured with Pilodyn and Hitman. Prediction accuracy (PC) of the standing tree-based methods were similar or even higher than increment core-based method. 78-95% of the maximal PAs of density, MFA and MOE obtained from coring to the pith at high age were reached by using data possible to obtain by drilling 3-5 rings towards the pith at tree age 10-12. CONCLUSIONS This study indicates standing tree-based measurements is a cost-effective alternative method for GS. PA of GS methods were comparable with those pedigree-based prediction. The highest PAs were reached with at least 80-90% of the dataset used as training set. Selection for trait density could be conducted at an earlier age than for MFA and MOE. Operational breeding can also be optimized by training the model at an earlier age or using 3 to 5 outermost rings at tree age 10 to 12 years, thereby shortening the cycle and reducing the impact on the tree.BACKGROUND The α2-adrenergic agonist dexmedetomidine (DEX) is a sedative and can be used as an adjunct to hypnotics. The study sought to evaluate the effects of different doses of DEX on the requirements for propofol for loss of consciousness (LOC) in patients monitored via the bispectral index (BIS). selleck kinase inhibitor METHODS In this randomized, double-blind, three arm parallel group design and placebo-controlled trial, 73 patients aged between 18 and ~ 65 years with a BMI range of 18.0-24.5 kg·m- 2 and an American Society of Anesthesiologists (ASA) grade I or II who were scheduled for general anesthesia at the General Hospital of Ningxia Medical University were included in this study. Anesthesiologists and patients were blinded to the syringe contents. All patients were randomly assigned in a 111 ratio to receive a 0.5 μg·kg- 1 DEX infusion (0.5 μg·kg- 1 DEX group; n = 24), a 1.0 μg·kg- 1 DEX infusion (1.0 μg·kg- 1 DEX group; n = 25) or a saline infusion (control group; n = 24) for 10 min. Propofol at a concentration of 20 mp (67.5 ± 3.5 for group 0.5 μg·kg- 1 DEX vs. 60.5 ± 3.8 for the control group; difference, 7.04 [95% CI, 4.85 to 9.23]; P = 0.0001) (68.4 ± 4.1 for group 1.0 μg·kg- 1 DEX vs. 60.5 ± 3.8 for the control group; difference, 7.58 [95% CI, 5.41 to 9.75]; P = 0.0001). CONCLUSION The study showed that DEX (both 0.5 and 1.0 μg·kg- 1 DEX) reduced the propofol requirements for LOC. DEX pre-administration increased the BIS value for LOC induced by propofol. CLINICAL TRIAL REGISTRATION The study was registered at ClinicalTrials.gov (trial ID NCT02783846 on May 26, 2016).BACKGROUND Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene interactions, and (b) the need for highly interpretable glass-box models. We use the theory of high dimensional model representation (HDMR) to build interpretable low dimensional approximations of the log-likelihood ratio accounting for the effects of each individual gene as well as gene-gene interactions. We propose two algorithms approximating the second order HDMR expansion, and a hypothesis test based on the HDMR formulation to identify significantly dysregulated pairwise interactions. The theory is seen as flexible and requiring only a mild set of assumptions. RESULTS We apply our approach to gene expression data from both synthetic and real (breast and lung cancer) datasets comparing it also against several popular state-of-the-art methods. The analyses suggest the proposed algorithms can be used to obtain interpretable prediction rules with high prediction accuracies and to successfully extract significantly dysregulated gene-gene interactions from the data. They also compare favorably against their competitors across multiple synthetic data scenarios. CONCLUSION The proposed HDMR-based approach appears to produce a reliable classifier that additionally allows one to describe how individual genes or gene-gene interactions affect classification decisions. Both real and synthetic data analyses suggest that our methods can be used to identify gene networks with dysregulated pairwise interactions, and are therefore appropriate for differential networks analysis.BACKGROUND Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model's utility and potential applications. Estimating the quality of a single model predicts the model's quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool. RESULTS We introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.