The interactive effects of ocean acidification and warming on bioeroding sponge Spheciospongia vesparium microbiome indicated by metatranscriptomics.

Global climate change will cause coral reefs decline and is expected to increase the reef erosion potential of bioeroding sponges. Microbial symbionts are essential for the overall fitness and survival of sponge holobionts in changing ocean environments. However, we rarely know about the impacts of ocean warming and acidification on bioeroding sponge microbiome.

The impacts of ocean acidification, warming and their interactive effects on coral prokaryotic symbionts.

Background: Reef-building corals, the foundation of tropical coral reefs, are vulnerable to climate change e.g. ocean acidification and elevated seawater temperature.

Complete genome of Mycetocola spongiae MSC19 isolated from deep-sea sponge Cacospongia mycofijiensis indicates the adaptation to deep-sea environment and sponge-microbe symbioses.

Genome of Mycetocola spongiae MSC19, a novel marine sponge-associated Actinobacteria isolated from the Mariana Trench sponge Cacospongia mycofijiensis, was sequenced. The genome has one circular chromosome of 3,196,754 bp, with an average GC content of 66.43 mol%, and 2887 coding sequences.

Flexible Symbiotic Associations of With Five Typical Coral Species in Tropical and Subtropical Reef Regions of the Northern South China Sea.

The coral symbiont plays important roles in the adaptation of coral to environmental changes. However, coral- symbiotic associations are not well-understood in the South China Sea (SCS) whilst considering environmental factors and host taxa. In this study, next-generation sequencing of the internal transcribed spacer region 2 (ITS2) marker gene was used to explore the symbiotic associations between and five typical coral species across tropical and subtropical reef regions of the SCS.

[Study on meteorological factors-based neural network model of malaria].

Objective: In order to provide reliable data for strategies development on prevention, a meteorological factors-based predicating model for malaria forecast was studied.

Methods: Data on malaria occurrence and climate changes from 1994 to 1999 in counties in Yunnan province was collected and analyzed with software packages of FoxPro 6.0 and Excel 5.