Degradation of phenanthrene by consortium 5H under hypersaline conditions.

PAHs have been widely detected to accumulate in saline and hypersaline environments. Moderately halophilic microbes are considered the most suitable player for the elimination of PAHs in such environments. In this study, consortium 5H was enriched under 5% salinity and completely degraded phenanthrene in 5 days. By high-throughput sequencing, consortium 5H was identified as being mainly composed of Methylophaga, Marinobacter and Thalassospira. Combined with the investigation of intermediates and enzymatic activities, the degradation pathway of consortium 5H on phenanthrene was proposed. Consortium 5H was identified as having the ability to tolerate a wide range of salinities (1%-10%) and initial PAH concentrations (50 mg/L to 400 mg/L). It was also able to function under neutral to weak alkaline conditions (pH from 6 to 9) and the phytotoxicity of the produced intermediates showed no significant difference with distilled water. Furthermore, the metagenome of consortium 5H was measured and analyzed, which showed a great abundance of catabolic genes contained in consortium 5H. This study expanded the knowledge of PAH-degradation under hypersaline environments and consortium 5H was proposed to have good potential for the elimination of PAH pollution in saline/hypersaline environments.

Analyzing knowledge entities about COVID-19 using entitymetrics.

COVID-19 cases have surpassed the 109 + million markers, with deaths tallying up to 2.4 million. Tens of thousands of papers regarding COVID-19 have been published along with countless bibliometric analyses done on COVID-19 literature. Despite this, none of the analyses have focused on domain entities occurring in scientific publications. However, analysis of these bio-entities and the relations among them, a strategy called entity metrics, could offer more insights into knowledge usage and diffusion in specific cases. Thus, this paper presents an entitymetric analysis on COVID-19 literature. We construct an entity-entity co-occurrence network and employ network indicators to analyze the extracted entities. We find that ACE-2 and C-reactive protein are two very important genes and that lopinavir and ritonavir are two very important chemicals, regardless of the results from either ranking.