Search

The community structure and vertical distribution of prokaryotes in a deep-sea (ca. 3,191 m) cold sediment sample (ca. 43 cm long) collected at the East Pacific Rise (EPR) ∼13°N were studied with 16SrDNA-based molecular analyses. Total community DNA was extracted from each of four discrete layers EPRDS-1, -2, -3 and -4 (from top to bottom) and 16S rDNA were amplified by PCR. Cluster analysis of DGGE profiles revealed that the bacterial communities shifted sharply between EPRDS-1 and EPRDS-2 in similarity coefficient at merely 49%. Twenty-three sequences retrieved from DGGE bands fell into 11 groups based on BLAST and bootstrap analysis. The dominant groups in the bacterial communities were Chloroflexi, Gamma proteobacteria, Actinobacterium and unidentified bacteria, with their corresponding percentages varying along discrete layers. Pairwise Fst ( F-statistics) values between the archaeal clone libraries indicated that the archaeal communities changed distinctly between EPRDS-2 and EPRDS-3. Sequences from the archaeal libraries were divided to eight groups. Crenarchaea Marine Group I (MGI) was prevalent in EPRDS-1 at 83%, while Uncultured Crenarchaea group II B (UCII B) abounded in EPRDS-4 at 61%. Our results revealed that the vertically stratified distribution of prokaryotic communities might be in response to the geochemical settings and suggested that the sampling area was influenced by hydrothermalism. The copresence of members related to hydrothermalism and cold deep-sea environments in the microbial community indicated that the area might be a transitional region from hydrothermal vents to cold deep-sea sediments. [ABSTRACT FROM AUTHOR]

Abstract A general class of discrete-time recurrent neural networks (DTRNNs) is formulated and studied in this paper. Several sufficient conditions are obtained to ensure the global stability of DTRNNs with delays based on induction principle (not based on the well-known Liapunov methods). The obtained results have neither assumed the symmetry of the connection matrix, nor boundedness, monotonicity or the differentiability of the activation functions. In addition, discrete-time analogues of a general class of continuous-time recurrent neural networks (CTRNNs) are derived and studied. The convergence characteristics of CTRNNs are preserved by the discrete-time analogues without any restriction imposed on the uniform discretization step size. Finally, the simulating results demonstrate the validity and feasibility of our proposed approach. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses a study on particle swarm optimization with diversive curiosity, article on a proposed novel intelligent identification method, and a discussion on the structure of fault information fusion system.