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

Previous studies show that mortalin, a HSP70 family member, contributes to the development and progression of ovarian cancer. However, details of the transcriptional regulation of mortalin remain unknown. We aimed to determine whether NF‐κB p65 participates in the regulation of mortalin expression in ovarian cancer cells and to elucidate the underlying mechanism. Chromatin immunoprecipitation and luciferase reporter assay were used to identify mortalin gene sequences, to which NF‐κB p65 binds. Results indicated that NF‐κB p65 binds to the mortalin promoter at a site with the sequence ‘CGGGGTTTCA’. Using lentiviral pLVX‐NF‐κB‐puro and Lentivirus‐delivered NF‐κB short hairpin RNA (shRNA), we created ovarian cancer cell lines in which NF‐κB p65 was stably up‐regulated and down‐regulated. Using these cells, we found that downregulation of NF‐κB p65 inhibits the growth and migration of ovarian cancer cells. Further experimental evidence indicated that downregulation of NF‐κB p65 reduced mortalin, and upregulation of mortalin rescued the proliferation and migration of ovarian cancer cells reduced by NF‐κB p65 knockdown. In conclusion, NF‐κB p65 binds to the mortalin promoter and promotes ovarian cancer cells proliferation and migration via regulating mortalin.

Overexpression of Flap endonuclease 1 (FEN1) has been previously implicated in hepatocellular carcinoma (HCC), while its expression features and mechanisms remain unclear. In the current study, differential expression genes (DEGs) were screened in HCC tissues and normal liver tissues in 4 Gene Expression Omnibus (GEO) datasets. FEN1, one of the hub co-overexpressed genes, was further determined overexpressed in HCC tissues in TCGA, local HCC cohorts, and hepatocarcinogenesis model. In addition, high expression of FEN1 indicated poor prognosis of HCC patients. Loss-of-function and gain-of-function assays demonstrated that FEN1 enhanced the proliferation, cell cycle phage transition, migration/ invasion, therapy resistance, xenograft growth, and epithelial-mesenchymal transition (EMT) process of HCC cells. Mechanically, FEN1 could inactivate P53 signaling by preventing the ubiquitination and degradation of mouse double minute 2 (MDM2) via recruiting ubiquitin-specific protease 7 (USP7). Interfering USP7 with P22077 significantly reversed the malignant phenotypes activated by FEN1. In conclusion, this study suggests FEN1 as a robust prognostic biomarker and potential target for HCC.

A divide and conquer algorithm for exploiting policy function monotonicity is proposed and analyzed. To solve a discrete problem with n states and n choices, the algorithm requires at most nlog2(n)+5n objective function evaluations. In contrast, existing methods for nonconcave problems require n2 evaluations in the worst case. For concave problems, the solution technique can be combined with a method exploiting concavity to reduce evaluations to 14n+2log2(n). A version of the algorithm exploiting monotonicity in two‐state variables allows for even more efficient solutions. The algorithm can also be efficiently employed in a common class of problems that do not have monotone policies, including problems with many state and choice variables. In the sovereign default model of Arellano (2008) and in the real business cycle model, the algorithm reduces run times by an order of magnitude for moderate grid sizes and orders of magnitude for larger ones. Sufficient conditions for monotonicity and code are provided.

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