Panoramic insights into microevolution and macroevolution of a Prevotella copri-containing lineage in primate guts

Prevotella copri and its related taxa are widely detected in mammalian gut microbiomes and have been linked with an enterotype in humans. However, their microevolution and macroevolution among hosts are poorly characterized. In this study, extensively collected marker genes and genomes were analyzed to trace their evolutionary history, host specificity, and biogeographic distribution. Investigations based on marker genes and genomes suggest that a P. copri-containing lineage (PCL) harbors diverse species in higher primates. Firstly, P. copri in the human gut consisted of multiple groups exhibiting high genomic divergence and conspicuous but non-strict biogeographic patterns. Most African strains with high genomic divergence from other strains were phylogenetically located at the root of the species, indicating the co-evolutionary history of P. copri and Homo sapiens. Secondly, although long-term co-evolution between PCL and higher primates was revealed, sporadic signals of co-speciation and extensive host jumping of PCL members were suggested among higher primates. Metagenomic and phylogenetic analyses indicated that P. copri and other PCL species found in domesticated mammals had been recently transmitted from humans. Thirdly, strong evidence was found on the extensively horizontal transfer of genes (e.g., genes encoding carbohydrate-active enzymes) among sympatric P. copri groups and PCL species in the same primate host. Our study provides panoramic insights into the combined effects of vertical and horizontal transmission, as well as potential niche adaptation, on the microevolutionary and macroevolutionary history for an enterotype-representative lineage

Investment decision model via an improved BP neural network

In macro investment, an investment decision model is established by using an improved back propagation (BP) artificial neural network (ANN). In this paper, the relations between elements of investment and output of products are determined, and then the optimal distribution of investment is determined by adjusting the distributions rationally. This model can reflect the highly nonlinear mapping relations among each element of investment by using nonlinear utility functions to improve the architecture of artificial neural network, which can be widely applied in investment problems. ©2010 IEEE