Betaine-assisted recombinase polymerase assay for rapid hepatitis B virus detection.

Hepatitis B virus (HBV) is a worldwide epidemic pathogen that causes hepatitis B. On-site screening the HBV infection is of critical importance for preventing and diagnosing HBV infection. In this paper, a simple, visual, and rapid method for on-site detection of HBV-DNA has been developed. This method is based on betaine-assisted recombinase polymerase assay and followed with naked-eye detection via lateral flow assay (BRPA-LF). Result show that nonspecific amplification is prone to occur in recombinase polymerase amplification (RPA) if the assay was performed with serum sample without purification. This problem has been addressed by adding 0.8 M of betaine to the RPA reactions. It was demonstrated that BRPA-LF can detect 1,000 copies of HBV-DNA in 50 µL mixture, and achieved 90% sensitivity and 100% specificity for serum sample detection. These results demonstrated that BRPA-LF can resist serum interference and has great potential for on-site screening of HBV infection.

Dyella caseinilytica sp. nov., Dyella flava sp. nov. and Dyella mobilis sp. nov., isolated from forest soil.

Three strains, DHOB09T, DHOC52T and DHON07T, were isolated from the forest soil of Dinghushan Biosphere Reserve, Guangdong Province, PR China. They were all Gram-stain-negative, aerobic, rod-shaped cells. The ranges (optimum) for the temperature, pH and NaCl concentration for growth of DHOB09T, DHOC52T and DHON07T were 10-42 (25-28) °C, pH 5.5-9.0 (7.0-7.5) and 0-4.0 (0-0.5) % (w/v); 10-42 (28) °C, pH 4.0-7.0 (4.5-6.5) and 0-2.0 (0) % (w/v) and 10-37 (25-28) °C, pH 4.0-7.5 (5.5-6.0) and 0-2.5 (0)  % (w/v), respectively. Phylogenetic analysis based on 16S rRNA gene sequences showed that DHOB09T, DHOC52T and DHON07T formed a phyletic cluster with seven species of the genus Dyella within the major clade of Dyella with sequence similarities ranged from 96.9 to 98.6 %. This indicated that the three strains may represent three novel species of the genus Dyella. This result was also strongly supported by the concatenated analysis of partial gyrB, lepA and recA gene sequences. DNA-DNA hybridization between strains DHON07T and DHOB09T, as well as DHON07T and Dyella koreensis BB4T was much lower than 70 %. The G+C content of strains DHOB09T, DHOC52T and DHON07T were 59.4, 60.7 and 59.5 %, respectively. The major fatty acids of the three strains were iso-C15 : 0, iso-C16 : 0 and iso-C17 : 0 and the predominant respiratory lipoquinone was ubiquinone-8. All of the physiological, phylogenetic and chemotaxonomic data showed that strains DHOB09T, DHOC52T and DHON07T are distinctive from each other and from all species of the genus Dyellawith validly published names. Therefore, we suggest that they represent three novel species of the genus, for which the names Dyella caseinilytica sp. nov. (type strain DHOB09T=CGMCC 1.15434T=LMG 29202T), Dyella flava sp. nov. (type strain DHOC52T=NBRC 111979T=KCTC 52128T) and Dyella mobilis sp. nov. (type strain DHON07T=CGMCC 1.15400T=NBRC 111475T) are proposed.

Robust image hashing via visual attention model and ring partition.

Robustness is an important property of image hashing. Most of the existing hashing algorithms do not reach good robustness against large-angle rotation. Aiming at this problem, we jointly exploit visual attention model and ring partition to design a novel image hashing, which can make good rotation robustness. In the proposed image hashing, a visual attention model called PFT (Phase spectrum of Fourier Transform) model is used to detect saliency map of preprocessed image. The LL sub-band of saliency map is then divided into concentric circles invariant to rotation by ring partition, and the means and variances of DWT coefficients on concentric circles are taken as image features. Next, these features are encrypted by a chaotic map and the Euclidean distances between normalized encrypted features are finally exploited to generate hash. Similarity between hashes is measured by L 1 norm. Many experimental tests show that our image hashing is robust to digital operations including rotation and reaches good discrimination. Comparisons demonstrate that classification performance of our image hashing outperforms those of some well-known hashing algorithms in terms of receiver operating characteristics curves. Simulation of image copy detection is carried out on an open image database called UCID and the result validates effectiveness of our hashing.