Weight Analysis for Multiattribute Group Decision-Making with Interval Grey Numbers Based on Decision-Makers’ Psychological Criteria

open access articleTo address the problem of multiattribute group decision-making with interval grey numbers, decision matrices are adjusted using kernels of interval grey numbers to reduce the psychological effects of decision-makers. The comprehensive weights of attributes are obtained by aggregating the subjective weights with objective weights, which are calculated based on the accuracy and difference of attributes. Considering the consistent, best, and worst decision-making abilities of decision-makers, grey incidence models are established to obtain the consistency weights and individual bipolar weights of decision-makers; then, the comprehensive weights of decision-makers are determined. A clustering approach of interval grey numbers is presented, and overall evaluations are obtained. Finally, an example is provided and its validity is tested to verify the feasibility of the proposed method

Z-ligustilide reduces cisplatin-induced nephrotoxicity via activation of NRF2/HO-1 signaling pathways

Purpose: To investigate the effect of Z-ligustilide (Z-lig) on cisplatin-induced nephrotoxicity and examine whether NRF2 signaling mediates the underlying mechanism of action.Methods: Human proximal tubular epithelial cells (HK-2) were pretreated with 20 or 100 μM Z-lig for 2 h, followed by 10 μM cisplatin treatment for 24 h. Cell viability was measured using (3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. A commercial kit was used todetermine lactate dehydrogenase (LDH) release. Apoptosis was determined by flow cytometry while Western blotting was used to evaluate protein levels. Levels of malondialdehyde (MDA), superoxide dismutase (SOD), glutathione (GSH), and glutathione peroxidase (GSH-Px) were assessed by enzymelinked immunosorbent assay (ELISA).Results: Cisplatin decreased HK-2 cell viability and increased LDH release, while Z-lig increased cell viability and decreased LDH release in a dose-dependent manner (p < 0.05). Moreover, Z-lig reduced cisplatin-induced apoptosis (p < 0.01), and alleviated cellular oxidative stress caused by cisplatin (p < 0.05). Furthermore, Z-lig activated NRF2/HO-1 signaling in cells treated with cisplatin (p < 0.05).Conclusion: Z-lig reduces cisplatin-induced nephrotoxicity via activation of NRF2/HO-1 signaling. Thus, Z-lig is a potential drug for the treatment of nephrotoxicity caused by cisplatin. Keywords: Z-ligustilide, Cisplatin, Nephrotoxicity, Oxidative stress, Apoptosis, Nuclear factor erythroid 2-related factor 2, Heme oxygenase-

Values and strategies of literary aesthetic appreciation in college English teaching in Chinese campuses

Literary aesthetic appreciation is an indispensable part of college English teaching. As an important content of aesthetic education as well as one of the basic qualities of the compound-type foreign language learners, literary aesthetic appreciation is also an essential part of the college teaching innovation, curriculum construction and training objectives. In the course of students' acquiring aesthetic knowledge, college English teachers need to combine moral education and highlight the value of aesthetic education, combine intellectual education and penetrate aesthetic education and combine interest stimulus and protrude the art function of literary aesthetic appreciation

Metabolome response to temperature-induced virulence gene expression in two genotypes of pathogenic Vibrio parahaemolyticus

Relative concentration of metabolites identified in Vibrio parahaemolyticus ATCC17802. (XLSX 113 kb)

SDSS-IV MaNGA: The Roles of AGNs and Dynamical Processes in Star Formation Quenching in Nearby Disk Galaxies

We study how star formation (SF) is quenched in low-redshift disk galaxies with integral-field spectroscopy. We select 131 face-on spiral galaxies with stellar mass greater than $\rm 3\times10^{10}M_\odot$, and with spatially resolved spectrum from MaNGA DR13. We subdivide the sample into four groups based on the offset of their global specific star formation rate (SFR) from the star-forming main sequence and stack the radial profiles of stellar mass and SFR. By comparing the stacked profiles of quiescent and star-forming disk galaxies, we find that the decrease of the global SFR is caused by the suppression of SF at all radii, but with a more significant drop from the center to the outer regions following an inside-out pattern. As the global specific SFR decreases, the central stellar mass, the fraction of disk galaxies hosting stellar bars, and active galactic nuclei (AGNs; including both LINERs and Seyferts) all increase, indicating dynamical processes and AGN feedback are possible contributors to the inside-out quenching of SF in the local universe. However, if we include only Seyferts, or AGNs with ${\rm EW(H\alpha)>3\AA}$, the increasing trend of AGN fraction with decreasing global sSFR disappears. Therefore, if AGN feedback is contributing to quenching, we suspect that it operates in the low-luminosity AGN mode, as indicated by the increasing large bulge mass of the more passive disk galaxies.Comment: 12 pages, 7 figures, published in ApJ, typos corrected, references update

Combining Sparse Group Lasso and Linear Mixed Model Improves Power to Detect Genetic Variants Underlying Quantitative Traits

Genome-Wide association studies (GWAS), based on testing one single nucleotide polymorphism (SNP) at a time, have revolutionized our understanding of the genetics of complex traits. In GWAS, there is a need to consider confounding effects such as due to population structure, and take groups of SNPs into account simultaneously due to the “polygenic” attribute of complex quantitative traits. In this paper, we propose a new approach SGL-LMM that puts together sparse group lasso (SGL) and linear mixed model (LMM) for multivariate associations of quantitative traits. LMM, as has been often used in GWAS, controls for confounders, while SGL maintains sparsity of the underlying multivariate regression model. SGL-LMM first sets a fixed zero effect to learn the parameters of random effects using LMM, and then estimates fixed effects using SGL regularization. We present efficient algorithms for hyperparameter tuning and feature selection using stability selection. While controlling for confounders and constraining for sparse solutions, SGL-LMM also provides a natural framework for incorporating prior biological information into the group structure underlying the model. Results based on both simulated and real data show SGL-LMM outperforms previous approaches in terms of power to detect associations and accuracy of quantitative trait prediction