Background: N6-methyladenosine (m6A) is the most prevalent modification of mammalian RNA. Emerging evidence suggest that m6A has critical roles in multiple biological activities, but little is known about its roles in cancer pathogenesis. Herein, we report the expression profiles and prognostic relevance of twelve m6A-related genes in hepatocellular carcinoma (HCC) by analyzing four independent datasets. Materials and methods: RNA levels of twelve m6A-related genes were detected in samples of 162 HCC patients who underwent curative resection (the Guangdong General Hospital dataset). We additionally analyzed the expression profiles of m6A-related genes in The Cancer Genome Atlas liver HCC dataset and two Gene Expression Omnibus datasets (GSE14520, GSE63898). Prognostic value of genes was evaluated by Kaplan–Meier curves of overall survival (OS) with the log-rank test and multivariate Cox regression analysis. Gene set enrichment analysis (GSEA) was conducted to identify associated KEGG pathways. Results: Five genes (METTL3, YTHDF1, YTHDF2, YTHDF3, and EIF3) showed consistent upregulation in all four datasets. Abnormal expressions of either METTL3 or YTHDF1 but not the other ten genes were associated with OS. Protein expression of METTL3 and YTHDF1 were confirmed in HCC tissues by immunohistochemical staining. Multivariate Cox regression analysis confirmed the independent predictive value of both METTL3 and YTHDF1 on OS. We further divided patients into three groups based on the median expression values of METTL3 and YTHDF1. In all datasets, the low METTL3/low YTHDF1 group showed a consistent better prognosis than other groups. GSEA revealed that both METTL3 and YTHDF1 regulate HCC cell cycle, RNA splicing, DNA replication, base excision repair, and RNA degradation. Conclusion: Both METTL3 and YTHDF1 were upregulated in HCC, and they were independent poor prognostic factors. Combination of METTL3 and YTHDF1 can be regarded as the biological marker that reflect malignant degree and evaluate prognosis in HCC.

Our objective was to compare the reliability and responsiveness of the original Steinbrocker's (OS), our modified Steinbrocker's (MS) and Larsen's (L) radiological scoring methods for detecting radiological change in psoriatic arthritis over time. Two sets of radiographs of the hands and feet at least 2 yr apart were selected from 68 patients. Films were randomly presented and scored independently by a rheumatologist (DDG) and a radiologist (DS), in a blinded fashion using all methods. The index of reliability was the intraclass coefficient (ICC) and the responsiveness was assessed using plots and regression analyses. All three radiological scoring methods have excellent interobserver and good intra-observer reliability. L and MS are equally responsive and superior to OS in detecting change in joint damage over time. Thus, the L or MS radiological scoring methods can be used to monitor disease progression in psoriatic arthritis.

Regional estimates of biogenic carbon fluxes over North America from both atmospheric inversions (“top‐down” approach) and terrestrial biosphere models (“bottom‐up”) remain highly uncertain. We merge these approaches with an ensemble‐based, regional modeling system able to diagnose and quantify the causes of uncertainties in top‐down atmospheric estimates of the terrestrial sink over North America. Our ensemble approach quantifies and partitions the uncertainty stemming from atmospheric transport, the biosphere, and large‐scale CO2 boundary inflow (boundary conditions). We use meteorological data, CO2 fluxes, and CO2 mole fraction measurements to assure the reliability of the ensemble system. Our results show that all uncertainty components have clear seasonal variations. The biogenic flux component dominates modeled boundary layer CO2 uncertainty, ranging from 2.5 ppm in summer and winter to 1.5 ppm in fall and spring. Spatially, it remains highly uncertain in the U.S. Corn Belt regions. Transport uncertainty reaches a maximum of 2.5 ppm in the summer months and stays at 1.2 ppm for the rest of the year and is highly correlated with the biogenic CO2 fluxes. Boundary conditions play the smallest role in atmospheric boundary layer CO2 uncertainty with a magnitude smaller than 1 ppm. However, boundary conditions are the most important uncertainty component in column‐averaged CO2 (XCO2). The spatiotemporal variations of the uncertainties in modeled XCO2 are similar to those in atmospheric boundary layer CO2.

Central Asia comprises a large fraction of the world's drylands, known to be vulnerable to climate change. We analyzed the inter-annual trends and the impact of climate variability in the vegetation greenness for Central