catena-Poly[[diaqua­cobalt(II)]bis­[μ-2-(4-carboxyl­atophen­yl)-4,4,5,5-tetra­methyl-4,5-dihydro-1H-imidazol-1-oxyl 3-oxide]]

In the title compound, [Co(C14H16N2O4)2(H2O)2]n, the CoII atom, lying on an inversion center, is coordinated by six O atoms in a distorted octa­hedral geometry. The CoII atoms are bridged by the nitronyl nitroxide ligands into a tape-like structure along the b axis. The tapes are further connected by O—H⋯O hydrogen bonds, forming a layer parallel to the bc plane

Roles of community commitment and community atmosphere: an empirical study of online community success

Online communities have become quite popular, and both practitioners and researchers have focused on the determinants of and the evaluation of online community success. The success of online community is not only embodied in the usage but also on the members’ intention to stick with the community over time. Based on the updated DeLone and McLean’s IS Success Model, and incorporating the theory of organization commitment, we propose a research model to investigate the key factors that lead to online community success. In our model, we depict the roles of community commitment and community atmosphere. On the basis of the existed scales, we develop an instrument and design a questionnaire. We conduct a survey and collect 273 answered responses in Xi’an. We use AMOS7.0 to make an analysis of the collected data. Our result indicates that continuance and affective community commitment have prominent influences on members’ usage behavior; but system quality, information quality, and community atmosphere do not affect the three components of community commitment simultaneously. System quality and information quality help to increase members’ continuance community commitment, and information quality and community atmosphere have a significant effect on affective commitment. This study may enrich the understanding of online community success by considering the roles of community commitment and their relationship with members’ usage

Saikosaponins induced hepatotoxicity in mice via lipid metabolism dysregulation and oxidative stress: a proteomic study

Background Radix Bupleuri (RB) has been popularly used for treating many liver diseases such as chronic hepatic inflammation and viral Hepatitis in China. Increasing clinical and experimental evidence indicates the potential hepatotoxicity of RB or prescriptions containing RB. Recently, Saikosaponins (SS) have been identified as major bioactive compounds isolated from RB, which may be also responsible for RB-induced liver injury. Methods Serum AST, ALT and LDH levels were determined to evaluate SS-induced liver injury in mice. Serum and liver total triglyceride and cholesterol were used to indicate lipid metabolism homeostasis. Liver ROS, GSH, MDA and iNOS were used to examine the oxidative stress level after SS administration. Western blot was used to detect CYP2E1 expression. A 8-Plex iTRAQ Labeling Coupled with 2D LC - MS/MS technique was applied to analyze the protein expression profiles in livers of mice administered with different doses of SS for different time periods. Gene ontology analysis, cluster and enrichment analysis were employed to elucidate potential mechanism involved. HepG2 cells were used to identify our findings in vitro. Results SS dose- and time-dependently induced liver injury in mice, indicated by increased serum AST, ALT and LDH levels. According to proteomic analysis, 487 differentially expressed proteins were identified in mice administrated with different dose of SS for different time periods. Altered proteins were enriched in pathways such as lipid metabolism, protein metabolism, macro molecular transportation, cytoskeleton structure and response to stress. SS enhanced CYP2E1 expression in a time and dose dependent manner, and induced oxidative stress both in vivo and in vitro. Conclusion Our results identified hepatotoxicity and established dose-time course-liver toxicity relationship in mice model of SS administration and suggested potential mechanisms, including impaired lipid and protein metabolism and oxidative stress. The current study provides experimental evidence for clinical safe use of RB, and also new insights into understanding the mechanism by which SS and RB induced liver injury

Microplastics affect soybean rhizosphere microbial composition and function during vegetative and reproductive stages

Microplastics (MPs) are emerging contaminants in agricultural soil, whereas their effects on the rhizosphere microbial ecosystems and biogeochemical nitrogen cycles during plant growth remain unknown. Here, a 70-day greenhouse experiment was carried out with black and fluvo-aquic soil to evaluate the influence of polyamide (PA), polyethylene (PE), polyester (PES), and polyvinyl chloride (PVC) MPs on the bacterial communities and functions in the soybean rhizosphere. The PA treatment consistently affected the rhizobacterial alpha diversity in the fluvo-aquic soil at soybean vegetative and reproductive growth stages, whereas the PE, PES, and PVC treatments had a short-term effect on the bacterial alpha diversity. At two growth stages, 6 and 23 biomarkers were consistently abundant in the PA treatment in the black soil and fluvo-aquic soil, respectively, and order Rhizobiales was found to be a biomarker for PA MPs contamination in both soils. Additionally, PA treatment decreased bacterial network complexity and tightness, whereas the effects of the PE, PES, and PVC on bacterial co-occurrence patterns varied depending on the soil types. Furthermore, PES and PVC treatments inhibited ammonification processes in the soybean rhizosphere, and PE could temporarily inhibit ammonia oxidation and denitrification processes according to the variations of N-cycling gene abundances. These effects on soil N-cycling also varied with soil types and soybean growth stages. This study provides profound information for understanding of MPs residues on the assembly of the soybean rhizosphere communities and function during plant development

The evaluation of ecosystem health based on hybrid TODIM method for Chinese case

The health evaluation of urban ecosystem is the need of urban sustainable development and the construction of urban ecological civilization, in order to scientifically evaluate the ecosystem health, in this paper, we establish the mathematical model based on hybrid multiple attributes decision-making. Firstly, we introduce the original city ecosystem health evaluation indexes which reflect on Vitality, Composition Structure, Recovery Capacity, The Ecological System Continually Offering Service Function, Population Health and Ecosystem Cognition ecosystem health. Then in order to obtain the reasonable weights, we integrate the subjective weights by linguistic AHP method and objective weights by deviation maximization method, and get the combined weights for city ecosystem health evaluation indexes. Further, according to the characteristics of the different indexes, we propose an extended TODIM method to evaluate the city ecosystem health in which the indexes take the form of real number, interval number, and probabilistic linguistic term set. Moreover, with respect to the evaluation values of city ecosystem health in Jinan from 2011 to 2015, this paper evaluates the health status of Jinan ecological system, and analyzes the role of various indicators in the process of city ecological development. Result shows that: (1) Jinan ecosystem health status remained at the sub-health state from 2011 to 2015, and the ecological situation is not optimistic. (2) Prominent problems restricting are lack of investment in environmental protection efforts, increasing pollutant emissions, and imperfect industrial structure. To solve the problems in the healthy development of Jinan urban ecosystem, this paper puts forward corresponding countermeasures and suggestions to improve the healthy development of urban ecosystem.  First published online 17 April 201

High Dimensional Apollonian Networks

We propose a simple algorithm which produces high dimensional Apollonian networks with both small-world and scale-free characteristics. We derive analytical expressions for the degree distribution, the clustering coefficient and the diameter of the networks, which are determined by their dimension