Numerous studies have demonstrated that plant species diversity enhances ecosystem functioning in terrestrial ecosystems, including diversity effects on insect arthropods (herbivores, predators and parasitoids) and plants. Yet, the effects of increased plant diversity across trophic levels in different ecosystems and biomes have not yet been explored on a global scale. Through a global meta-analysis of 2914 observations from 351 studies, we found that increased plant species richness reduced herbivore abundance and damage but increased predator and parasitoid abundance, predation, parasitism, and overall plant performance. Moreover, increased predator/parasitoid performance was correlated with reduced herbivore abundance and enhanced plant performance. We

The generalized leverage of an estimator is de®ned in regression models as a measure of the importance of individual observations. We derive a simple but powerful result, developing an explicit expression for leverage in a general M-estimation problem, of which the maximum likelihood problems are special cases. A variety of applications are considered, most notably to the exponential family non-linear models. The relationship between leverage and local in¯uence is also discussed. Numerical examples are given to illustrate our results.

Numerous researchers have investigated the associations among methylenetetrahydrofolate reductase gene (MTHFR) C677T polymorphism, homocysteine (Hcy) concentration, and hypertension. However, the results are controversial. Thus, a meta‐analysis implementing Mendelian randomization approach was conducted to examine the hypothesis that elevated Hcy concentration plausibly contributes to increased risk of hypertension. Based on several inclusion and exclusion criteria, eligible studies were selected to explore the correlation between MTHFR C677T and hypertension risk, MTHFR C677T and Hcy concentration in hypertension, and Hcy concentration and hypertension, and they were evaluated by odds ratios (ORs), effect size (ES), and standard mean difference with their corresponding 95% confidence intervals (95% CIs), respectively. Moreover, Mendelian randomization was implemented to evaluate the relationship between Hcy and hypertension. Consequently, 14 378 cases and 25 795 controls were involved in this study and the results showed that MTHFR C677T led to an elevated risk of hypertension (for T vs C: OR = 1.27, 95% CI = 1.17‐1.37; for TT vs CC: OR = 1.53, 95% CI = 1.30‐1.79). Additionally, in hypertensive subjects, the pooled Hcy concentration in individuals of TT genotype was 7.74 μmol/L (95% CI: 5.25‐10.23) greater than that in individuals of CC genotype. Moreover, the pooled Hcy concentration in hypertensive was 0.69 μmol/L (95% CI: 0.50‐0.87) greater than that in controls. The estimated causal OR associated with hypertension was 1.32 for 5 μmol/L Hcy increment. Via MTHFR C677T polymorphism, the findings in the present study demonstrated that there exists evidence on causal link between Hcy concentration and the risk of hypertension.

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Ovarian cancer (OV) is one of the leading causes of cancer deaths in women worldwide. Late diagnosis and heterogeneous treatment result to poor survival outcomes for patients with OV. Therefore, we aimed to develop novel biomarkers for prognosis prediction from the potential molecular mechanism of tumorigenesis. Eight eligible data sets related to OV in GEO database were integrated to identify differential expression genes (DEGs) between tumour tissues and normal. Enrichment analyses discovered DEGs were most significantly enriched in G2/M checkpoint signalling pathway. Subsequently, we constructed a multi‐gene signature based on the LASSO Cox regression model in the TCGA database and time‐dependent ROC curves showed good predictive accuracy for 1‐, 3‐ and 5‐year overall survival. Utility in various types of OV was validated through subgroup survival analysis. Risk scores formulated by the multi‐gene signature stratified patients into high‐risk and low‐risk, and the former inclined worse overall survival than the latter. By incorporating this signature with age and pathological tumour stage, a visual predictive nomogram was established, which was useful for clinicians to predict survival outcome of patients. Furthermore, SNRPD1 and EFNA5 were selected from the multi‐gene signature as simplified prognostic indicators. Higher EFNA5 expression or lower SNRPD1 indicated poorer outcome. The correlation between signature gene expression and clinical characteristics was observed through WGCNA. Drug‐gene interaction was used to identify 16 potentially targeted drugs for OV treatment. In conclusion, we established novel gene signatures as independent prognostic factors to stratify the risk of OV patients and facilitate the implementation of personalized therapies.