Biological characteristics and genomic analysis of a novel Vibrio parahaemolyticus phage phiTY18 isolated from the coastal water of Xiamen China.

Vibrio parahaemolyticus is a common pathogen usually controlled by antibiotics in mariculture. Notably, traditional antibiotic therapy is becoming less effective because of the emergence of bacterial resistance, hence new strategies need to be found to overcome this challenge. Bacteriophages, a class of viruses that lyse bacteria, can help us control drug-resistant bacteria. In this study, a novel Vibrio parahaemolyticus phage phiTY18 isolated from the coastal water of Xiamen was explored. Transmission electron microscopy showed that phiTY18 had an icosahedral head of 130.0 ± 1.2 nm diameter and a contractile tail of length of 66.7 ± 0.6 nm. The phage titer could reach 7.2×1010 PFU/mL at the optimal MOI (0.01). The phage phiTY18 had a degree of tolerance to heat and acid and base. At the temperature of 50°C (pH7.0, 1h) the survival phages reached 1.28×106 PFU/mL, and at pH 5-9 (30°C, 1h), the survival phages was greater than 6.37×107 PFU/mL Analysis of the phage one-step growth curve revealed that it had a latent period of 10min, a rise period of 10min, and an average burst size of the phage was 48 PFU/cell. Genome sequencing and analysis drew that phage phiTY18 had double-stranded DNA (191,500 bp) with 34.90% G+C content and contained 117 open reading frames (ORFs) and 24 tRNAs. Phylogenetic tree based on major capsid protein (MCP) revealed that phage phiTY18 (MW451250) was highly related to two Vibrio phages phiKT1024 (OM249648) and Va1 (MK387337). The NCBI alignment results showed that the nucleotide sequence identity was 97% and 93%, respectively. In addition, proteomic tree analysis indicated that phage phiTY18, phiKT1024, and Va1 were belong to the same virus sub-cluster within Myoviridae. This study provides a theoretical basis for understanding the genomic characteristics and the interaction between Vibrio parahaemolyticus phages and their host.

Dynamic guided metric representation learning for multi-view clustering.

Multi-view clustering (MVC) is a mainstream task that aims to divide objects into meaningful groups from different perspectives. The quality of data representation is the key issue in MVC. A comprehensive meaningful data representation should be with the discriminant characteristics in a single view and the correlation of multiple views. Considering this, a novel framework called Dynamic Guided Metric Representation Learning for Multi-View Clustering (DGMRL-MVC) is proposed in this paper, which can cluster multi-view data in a learned latent discriminated embedding space. Specifically, in the framework, the data representation can be enhanced by multi-steps. Firstly, the class separability is enforced with Fisher Discriminant Analysis (FDA) within each single view, while the consistence among different views is enhanced based on Hilbert-Schmidt independence criteria (HSIC). Then, the 1st enhanced representation is obtained. In the second step, a dynamic routing mechanism is introduced, in which the location or direction information is added to fulfil the expression. After that, a generalized canonical correlation analysis (GCCA) model is used to get the final ultimate common discriminated representation. The learned fusion representation can substantially improve multi-view clustering performance. Experiments validated the effectiveness of the proposed method for clustering tasks.