[Habitat characteristics of Octopus ocellatus and their relationship with environmental factors during spring in Haizhou Bay, China]. / æµ·å·žæ¹¾æ˜¥å­£çŸ­è›¸çš„æ –æ¯åˆ†å¸ƒç‰¹å¾åŠå ¶ä¸ŽçŽ¯å¢ƒå› å­çš„å ³ç³».

In recent years, a variety of important fishery resources in China's coastal waters have declined. Octopus ocellatus has the characteristics of short life cycle and rapid growth, with great contributions to fisheries of China's coastal waters. However, we know little about the habitat distribution characteristics of O. ocellatus and its relationship with environmental factors, which is not conducive to better protection and utilization of its resources. Here, we analyzed the distribution characteristics of O. ocellatus and its relationship with environmental factors using three machine learning methods, i.e., random forest model, artificial neural network model, and generalized boosted regression models, based on the survey data of fishery resources and habitat in Haizhou Bay during spring of 2011 and 2013-2017. Among the three models, random forest model had great advantages in the fitting effect and prediction ability. The model analysis results showed that sea bottom temperature, seawater depth and sea bottom salinity had significant effects on the habitat distribution of O. ocellatus. The relative resource density of O. octopus increased first and then decreased with the increases of sea bottom temperature, seawater depth, and sea bottom salinity. Based on environmental data simulated by the FVCOM model, we predicted the habitat distribution of O. ocellatus in Haizhou Bay using random forest model and found that O. ocellatus was mainly distributed in the area between 34.5°-35.8° N and 119.7°-121° E.

Maltose Utilization as a Novel Selection Strategy for Continuous Evolution of Microbes with Enhanced Metabolite Production.

We have developed a novel selection circuit based on carbon source utilization that establishes and sustains growth-production coupling over several generations in a medium with maltose as the sole carbon source. In contrast to traditional antibiotic resistance-based circuits, we first proved that coupling of cell fitness to metabolite production by our circuit was more robust with a much lower escape risk even after many rounds of selection. We then applied the selection circuit to the optimization of L-tryptophan (l-Trp) production. We demonstrated that it enriched for specific mutants with increased l-Trp productivity regardless of whether it was applied to a small and defined mutational library or a relatively large and undefined one. From the latter, we identified four novel mutations with enhanced l-Trp output. Finally, we used it to select for several high l-Trp producers with randomly generated genome-wide mutations and obtained strains with up to 65% increased l-Trp production. This selection circuit provides new perspectives for the optimization of microbial cell factories for diverse metabolite production and the discovery of novel genotype-phenotype associations at the single-gene and whole-genome levels.

[Characteristics of ichthyoplankton assemblages in Yangtze Estuary and adjacent waters in spring].

Based on the investigation data of ichthyoplankton assemblages and environmental factors in Yangtze Estuary and adjacent waters in May 1999 and 2001, the characteristics of ichthyoplankton assemblages in these areas in spring were studied by using TWINSPAN (two-way indicator species analysis) and CCA (canonical correspondence analysis). A total of 11 540 ichthyoplankton individuals were taxonomically identified, belonging to 11 orders, 18 families and 32 species, of which, Coilia mystus, Engraulis japonicus, Chaeturichthys hexanema, Allanetta bleekeri, and Trachidermis fasciatus were the dominant species. The ichthyoplankton communities were classified into three assemblages by using TWINSPAN, i.e., estuarine assemblage dominated by C. mystus, coastal assemblage dominated by A. bleekeri and T. fasciatus; and shelf assemblage featured by E. japonicus and C. hexanema. The CCA ordination of the interrelations among the three assemblages and their correlations to the environmental variables revealed that salinity, depth, dissolved oxygen, and total suspended particulate matter were the major factors affecting the ichthyoplankton assemblages in the study areas.