Limits of propidium iodide as a cell viability indicator for environmental bacteria.

BACKGROUND: Viability measurements of individual bacteria are applied in various scopes of research and industry using approaches where propidium iodide (PI) serves as dead cell indicator. The reliability of PI uptake as a cell viability indicator for dead (PI permeable) and viable (PI impermeable) bacteria was tested using two soil bacteria, the gram(-) Sphingomonas sp. LB126 and the gram(+) Mycobacterium frederiksbergense LB501T. METHODS: Bacterial proliferation activities observed viaDAPI and Hoechst 33342 staining were linked to the energy charge and the proportion of dead cells as obtained by diOC(6) (3)-staining and PI-uptake, respectively. Calibration and verification experiments were performed using batch cultures grown on different substrates. RESULTS: PI uptake depended on the physiological state of the bacterial cells. Unexpectedly, up to 40% of both strains were stained by PI during early exponential growth on glucose when compared to 2-5% of cells in the early stationary phase of growth. CONCLUSIONS: The results question the utility of PI as a universal indicator for the viability of (environmental) bacteria. It rather appears that in addition to nonviable cells, PI also stains growing cells of Sphingomonas sp. and M. frederiksbergense during a short period of their life cycle.     

Impacts of glutathione peroxidase-1 knockout on the protection by injected selenium against the pro-oxidant-induced liver aponecrosis and signaling in selenium-deficient mice

Previous research has suggested that repletion of cellular glutathione peroxidase (GPX1) activity by a single injection of Se was dissociated from the Se protection against the pro-oxidant-induced liver necrosis in Se-deficient rodents. Using the GPX1 knockout (GPX1-/-) mice, TUNEL assay, and apoptosis gene expression microarray, we have demonstrated strikingly different impacts of GPX1 knockout on hepatotoxicity and the related signaling induced by an intraperitoneal injection of 12.5 mg paraquat/kg body weight (b.wt.). In both Se-deficient GPX1-/- and wild-type (WT) mice, the paraquat did not induce typical liver necrosis, rather aponecrosis or necrapoptosis, a syncretic process of cell death sharing characteristics of both apoptosis and necrosis. The severity of liver aponecrosis and the associated mortality were reduced to a much greater extent by an injection of Se (ip, 50 microg/kg b.wt. as Na2SeO3) prior to paraquat stress in the WT mice, compared with the GPX1-/- mice. The induced liver aponecrosis seemed to be more apoptotic in the GPX1-/- mice but more necrotic in the WT mice. The paraquat-mediated gene or protein expression of proapoptotic Bax, Bcl-w, and Bcl-X(S), cell survival/death factors GADD45, MDM2, c-Myc, and caspase-3 was upregulated, but that of antiapoptotic Bcl-2 was downregulated in the GPX1-/- mice vs. the WT mice. Overall, these differences between the two groups of mice were related to a low level of liver GPX1 activity in the WT mice that represented < 4% of the normal physiological level. Therefore, the low level of GPX1 activity in the Se-deficient mice can exert a potent role in defending against liver aponecrosis induced by moderate oxidative stress.     

基于LSMM与MSPA的深圳市绿色景观连通性研究

基于线性光谱混合模型(LSMM,Linear Spectral Mixture Model),引入形态学空间格局分析(MSPA,Morphological Spatial Pattern Analysis)进行城市地域绿色景观连通性评价。根据城市绿色景观特点和MSPA方法中的7种连通性类型的涵义,定义了城市绿色景观连通性功能类型。以深圳市1986年、1995年、2000年、2005年及2010年五期Landsat TM影像为数据源,应用线性光谱混合模型提取植被覆盖率,得到深圳市植被覆盖图。在此基础上,提取出高、全植被覆盖作为目标像元进行MSPA处理,分析植被覆盖状况与绿色景观功能类型的时序总体特征及空间梯度动态。结果表明:深圳市绿色景观破碎程度较高,表现为对结构连通性贡献最小的斑块类型总数最大。城市内部东西部连通性呈现出不同变化的趋势;右侧外圈层的大鹏半岛结构连通性最佳;在同一城市化发展梯度上,东部的样带连通性水平比西部要好。在城市化过程中,深圳市高、全覆被植被像元连通性大小受以下因素的影响:城市化程度,地形因素及区域定位。在同一城市化程度上,地形因素对景观连通性的影响较大。从整体的时间变化和空间梯度动态分析可知,在快速城市化过程中植被覆盖率和连通性功能均下降,而到稳定城市化阶段植被覆盖率和连通性均得到改善。研究表明线性光谱混合模型与形态学空间格局分析相结合可以较好的表征城市绿色景观连通性类型时空分布特征,进而明晰城市化过程与区域内绿色景观数量及连通性动态变化关系。     

Aspirin Use and Lung Cancer Risk: A Possible Relationship? Evidence from an Updated Meta-Analysis

Background and Purpose

Growing evidence has emerged and controversial results reported on possible relationship between aspirin use and lung cancer risk. We, therefore, conducted this updated and comprehensive meta-analysis to evaluate this issue, with focus on dose-risk and duration-risk relationships.

Methods

We searched electronic databases including PUBMED, EMBASE and Cochrane library to identify eligible studies. Relative risk (RR) and its 95% confidence interval (CI) were used for cohort studies, while odds ratio (OR) were employed for case-control studies. The random effects and fixed effects models were used for analyses.

Results

18 studies were identified including 19835 lung cancer cases, which were eligible for inclusion in the present meta-analysis. Pooled data from case-control studies showed a significant inverse association between regular aspirin use and lung cancer risk. But for cohort studies, insignificant association was detected with little evidence of heterogeneity (RR: 1.05, 95%CI: 0.95 – 1.16; I²: 10.3%, p value: 0.351). In case-control studies, standard aspirin use (>325mg) was related to lower lung cancer incidence, compared with low-dose aspirin use (75–100mg). A similar trend was observed in cohort studies. Besides, when analysis was restricted to long time regular aspirin use (>5 years), insignificant results were reported in both cohort and case-control studies. Finally, regular aspirin use might result in higher reduction of non-small cell lung cancer incidence among men.

Conclusions

Our findings do not support the protective effect of regular aspirin use on lung cancer risk. Long time aspirin use, sex, dose and type of lung cancer might alter the effect of aspirin use on lung cancer risk. More well-designed studies are needed to further clarify these associations.     

Differential Phosphorylation of Smad1 Integrates BMP and Neurotrophin Pathways through Erk/Dusp in Axon Development

1. Download : Download full-size image

     

Stable Li Metal Anode Enabled by Space Confinement and Uniform Curvature through Lithiophilic Nanotube Arrays

The application of lithium (Li) metal anodes in rechargeable batteries is primarily restricted by Li dendrite growth on the metal's surface, which leads to shortened cycle life and safety concerns. Herein, well‐spaced nanotubes with ultrauniform surface curvature are introduced as a Li metal anode structure. The ultrauniform nanotubular surface generates uniform local electric fields that evenly attract Li‐ions to the surface, thereby inducing even current density distribution. Moreover, the well‐defined nanotube spacing offers Li diffusion pathways to the electroactive areas as well as the confined spaces to host deposited Li. These structural attributes create a unique electrodeposition manner; i.e., Li metal homogenously deposits on the nanotubular wall, causing each Li nanotube to grow in circumference without obvious sign of dendritic formation. Thus, the full‐cell battery with the spaced Li nanotubes exhibits a high specific capacity of 132 mA h g^?1 at 1 C and an excellent coulombic efficiency of ≈99.85% over 400 cycles.     

Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods

Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large. In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation. Firstly, we propose sufficient conditions for causal effect invariance under different selection mechanisms using two-sample MR methods. In the simulation study, we consider 49 possible selection mechanisms in two-sample MR, which depend on genetic variants (G), exposures (X), outcomes (Y) and their combination. We further compare eight pleiotropy-robust methods under different selection mechanisms. Results of simulation reveal that nonrandom selection in sample II has a larger influence on biases and type I error rates than those in sample I. Furthermore, selections depending on X+Y, G+Y, or G+X+Y in sample II lead to larger biases than other selection mechanisms. Notably, when selection depends on Y, bias of causal estimation for non-zero causal effect is larger than that for null causal effect. Especially, the mode based estimate has the largest standard errors among the eight methods. In the absence of pleiotropy, selections depending on Y or G in sample II show nearly unbiased causal effect estimations when the casual effect is null. In the scenarios of balanced pleiotropy, all eight MR methods, especially MR-Egger, demonstrate large biases because the nonrandom selections result in the violation of the Instrument Strength Independent of Direct Effect (InSIDE) assumption. When directional pleiotropy exists, nonrandom selections have a severe impact on the eight MR methods. Application demonstrates that the nonrandom selection in sample II (coronary heart disease patients) can magnify the causal effect estimation of obesity on HbA1c levels. In conclusion, nonrandom selection in two-sample MR exacerbates the bias of causal effect estimation for pleiotropy-robust MR methods.     

Auto-adaptive Alpha Allocation: A Strategy to Mitigate Risk on Study Assumptions

In some clinical development programs, there are potential biomarkers with promising but uncertain predictive effect, while the probability of success in the overall population cannot be readily dismissed. It is risky to focus only on the overall population, or just the biomarker subpopulation. In 2009, Chen and Beckman proposed a Bayesian decision framework to optimize the type I error rate (alpha) allocation in a Phase III clinical study with possible predictive subset effect. The utilization of internal data in this framework is of particular interest because it provides an opportunity to mitigate the potential risk of misspecified study assumptions using an auto-adaptive strategy. In this paper, we examine this auto-adaptive strategy in detail through extensive numerical case studies and provide guidance on the appropriate use of partial current trial (internal) data in this data-driven optimization framework. We show that internal data can be used to inform the alpha allocation to hypothesis testing in the overall population and the subgroup. The resulting adaptive testing strategy is robust with respect to the uncertainty in the predictive subgroup effect and biomarker prevalence.