Response of instream animal communities to a short-term extreme event and to longer-term cumulative impacts in a strategic water resource area,South Africa

Disturbance plays an integral part in generating heterogeneity required for ecosystem persistence, but the increased amplitude and duration of disturbances linked to drivers of global change could result in ecosystem shifts or collapse. Biomonitoring over time provides insights into trajectories of ecosystem change. The responses of two instream animal taxa to two contrasting disturbance events, a major flood event and the long-term cumulative effects of land-use changes, were assessed in 1999–2012 by quantifying variation and change in abundance of functional groups based on flow rate sensitivity, water quality and metrics of ecological condition. All metrics recovered to pre-flood conditions within seven months after the flood event. Similarly, cumulative impacts of land use effected significant decreases in some but not all metrics. Indices that did not change, including SASS total score and ASPT, were the result of insufficient consideration of the decrease in the abundance of sensitive taxa specifically, and the abundance of all taxa in general. The decrease in abundance of sensitive taxa could signal imminent collapse in certain metrics. Evidence is also provided for a shift in the structure of fish assemblages linked to the decrease and loss of taxa sensitive to ecosystem degradation caused by the longer-term impacts of land-use change.     

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.