Binarization of ESPI fringe patterns based on an M-net convolutional neural network.

The fringe skeleton method is the most straightforward method to estimate phase terms in electronic speckle pattern interferometry (ESPI). It usually needs to binarize the fringe patterns. However, the massive inherent speckle noise and intensity inhomogeneity in ESPI fringe patterns make it difficult to binarize the ESPI fringe patterns. In this paper, we propose a binarization method for ESPI fringe patterns based on a modified M-net convolutional neural network. Our method regards the binarization of fringe patterns as a segmentation problem. The M-net is an excellent network for segmentation and has proven to be a useful tool for skeleton extraction in our previous work. Here we further modify the structure of the previous network a bit to suit our task. We train the network by pairs of ESPI fringe patterns and corresponding binary images. After training, we test our method on 20 computer-simulated and three groups of experimentally obtained ESPI fringe patterns. The results show that even for fringe patterns with high noise and intensity inhomogeneity, our method can obtain good binarization results without image preprocessing. We also compare the modified M-net with a classic segmentation network, the U-net, and a residual encoder-decoder network (RED-net). The RED-net was used for binarization of document images. The experimental results prove the effectiveness of our method.

Skeleton extraction and inpainting from poor, broken ESPI fringe with an M-net convolutional neural network.

Extracting skeletons from fringe patterns is the key to the fringe skeleton method, which is used to extract phase terms in electronic speckle pattern interferometry (ESPI). Because of massive inherent speckle noise, extracting skeletons from poor, broken ESPI fringe patterns is challenging. In this paper, we propose a method based on a modified M-net convolutional neural network for skeleton extraction from poor, broken ESPI fringe patterns. In our method, we pose the problem as a segmentation task. The M-net performs excellent segmentation, and we modify its loss function to suit our task. The broken ESPI fringe patterns and corresponding complete skeleton images are used to train the modified M-net. The trained network can extract and inpaint the skeletons simultaneously. We evaluate the performance of the network on two groups of computer-simulated ESPI fringe patterns and two groups of experimentally obtained ESPI fringe patterns. Two related recent methods, the gradient vector fields based on variational image decomposition and the U-net based method, are compared with our method. The results demonstrate that our method can obtain accurate, complete, and smooth skeletons in all cases, even where fringes are broken. It outperforms the two compared methods quantitatively and qualitatively.

Comparative genomic analysis of Proteus spp. isolated from tree shrews indicated unexpectedly high genetic diversity.

Proteus spp. are commensal gastrointestinal bacteria in many hosts, but information regarding the mutual relationships between these bacteria and their hosts is limited. The tree shrew is an alternative laboratory animal widely used for human disease research. However, little is known about the relationship between Proteus spp. and tree shrews. In this study, the complete genome sequencing method was used to analyse the characteristics of Proteus spp. isolated from tree shrews, and comparative genomic analysis was performed to reveal their relationships. The results showed that 36 Proteus spp. bacteria were isolated, including 34 Proteus mirabilis strains and two Proteus vulgaris strains. The effective rate of sequencing was 93.53%±2.73%, with an average GC content of 39.94%±0.25%. Briefly, 3682.89±90.37, 2771.36±36.01 and 2832.06±42.49 genes were annotated in the NCBI non-redundant nucleotide database (NR), SwissProt database and KEGG database, respectively. The high proportions of macrolide-, vancomycin-, bacitracin-, and tetracycline-resistance profiles of the strains were annotated in the Antibiotic Resistance Genes Database (ARDB). Flagella, lipooligosaccharides, type 1 fimbriae and P fimbriae were the most abundantly annotated virulence factors in the Virulence Factor Database (VFDB). SNP variants indicated high proportions of base transitions (Ts), homozygous mutations (Hom) and non-synonymous mutations (Non-Syn) in Proteus spp. (P<0.05). Phylogenetic analysis of Proteus spp. and other references revealed high genetic diversity for strains isolated from tree shrews, and host specificity of Proteus spp. bacteria was not found. Overall, this study provided important information on characteristics of genome for Proteus spp. isolated from tree shrews.

The characteristics of gut microbiota and commensal Enterobacteriaceae isolates in tree shrew (Tupaia belangeri).

BACKGROUND: Tree shrew is a novel laboratory animal with specific characters for human disease researches in recent years. However, little is known about its characteristics of gut microbial community and intestinal commensal bacteria. In this study, 16S rRNA sequencing method was used to illustrate the gut microbiota structure and commensal Enterobacteriaceae bacteria were isolated to demonstrate their features. RESULTS: The results showed Epsilonbacteraeota (30%), Proteobacteria (25%), Firmicutes (19%), Fusobacteria (13%), and Bacteroidetes (8%) were the most abundant phyla in the gut of tree shrew. Campylobacteria, Campylobacterales, Helicobacteraceae and Helicobacter were the predominant abundance for class, order, family and genus levels respectively. The alpha diversity analysis showed statistical significance (P < 0.05) for operational taxonomic units (OTUs), the richness estimates, and diversity indices for age groups of tree shrew. Beta diversity revealed the significant difference (P < 0.05) between age groups, which showed high abundance of Epsilonbacteraeota and Spirochaetes in infant group, Proteobacteria in young group, Fusobacteria in middle group, and Firmicutes in senile group. The diversity of microbial community was increased followed by the aging process of this animal. 16S rRNA gene functional prediction indicated that highly hot spots for infectious diseases, and neurodegenerative diseases in low age group of tree shrew (infant and young). The most isolated commensal Enterobacteriaceae bacteria from tree shrew were Proteus spp. (67%) and Escherichia coli (25%). Among these strains, the antibiotic resistant isolates were commonly found, and pulsed-field gel electrophoresis (PFGE) results of Proteus spp. indicated a high degree of similarity between isolates in the same age group, which was not observed for other bacteria. CONCLUSIONS: In general, this study made understandings of the gut community structure and diversity of tree shrew.

Characteristics of the tree shrew gut virome.

The tree shrew (Tupaia belangeri) has been proposed as an alternative laboratory animal to primates in biomedical research in recent years. However, characteristics of the tree shrew gut virome remain unclear. In this study, the metagenomic analysis method was used to identify the features of gut virome from fecal samples of this animal. Results showed that 5.80% of sequence reads in the libraries exhibited significant similarity to sequences deposited in the viral reference database (NCBI non-redundant nucleotide databases, viral protein databases and ACLAME database), and these reads were further classified into three major orders: Caudovirales (58.0%), Picornavirales (16.0%), and Herpesvirales (6.0%). Siphoviridae (46.0%), Myoviridae (45.0%), and Podoviridae (8.0%) comprised most Caudovirales. Picornaviridae (99.9%) and Herpesviridae (99.0%) were the primary families of Picornavirales and Herpesvirales, respectively. According to the host types and nucleic acid classifications, all of the related viruses in this study were divided into bacterial phage (61.83%), animal-specific virus (34.50%), plant-specific virus (0.09%), insect-specific virus (0.08%) and other viruses (3.50%). The dsDNA virus accounted for 51.13% of the total, followed by ssRNA (33.51%) and ssDNA virus (15.36%). This study provides an initial understanding of the community structure of the gut virome of tree shrew and a baseline for future tree shrew virus investigation.