Binocular measurement method for a continuous casting slab based on the one-dimensional probabilistic Hough transform and local sub-pixel sifting.

Due to the low measurement accuracy of the continuous casting slab model caused by difficulty in detecting ideal corners, a binocular measurement method based on the one-dimensional probabilistic Hough transform and local sub-pixel sifting is proposed. First, the one-dimensional probabilistic Hough transform based on inclination angle voting and the Freeman chain code is used to detect the line segments of the exterior outline. Next, sub-pixel points are extracted in each region of interest (ROI) by using Zernike moments, and sifted in the overlapping area of adjacent ROIs. Then the orthogonal total least squares (TLS) method is applied to fitting sub-pixel edges. Finally, after the key points are matched, three-dimensional localization and measurement are completed according to the binocular vision measurement principle. The experimental results show that the minimum relative error and average relative error of length reach 0.3401% and 0.3945%, respectively, satisfying the measurement requirement. Compared with scale-invariant feature transform (SIFT) and the oriented FAST and rotated BRIEF (ORB), the measurement error of the proposed algorithm is reduced by 80.01% and 74.63%, respectively. Compared with another edge fitting method based on k-means clustering and least squares fitting, its measurement error is reduced by 34.11%, and the time consumption is shortened by 39.07%, verifying its excellent performance in accuracy and efficiency.

Characterization of G-type Clostridium perfringens bacteriophages and their disinfection effect on chicken meat.

OBJECTIVE: Clostridium perfringens is one of most important bacterial pathogens in the poultry industry and mainly causes necrotizing enteritis (NE). This pathogen and its toxins can cause foodborne diseases in humans through the food chain. In China, with the rise of antibiotic resistance and the banning of antibiotic growth promoters (AGPs) in poultry farming, food contamination and NE are becoming more prevalent. Bacteriophages are a viable technique to control C. perfringens as an alternative to antibiotics. We isolated Clostridium phage from the environment, providing a new method for the prevention of NE and C. perfringens contamination in meat. METHODS: In this study, we selected C. perfringens strains from various regions and animal sources in China for phage isolation. The biological characteristics of Clostridium phage were studied in terms of host range, MOI, one-step curve, temperature and pH stability. We sequenced and annotated the genome of the Clostridium phage and performed phylogenetic and pangenomic analyses. Finally, we studied its antibacterial activity against bacterial culture and its disinfection effect against C. perfringens in meat. RESULTS: A Clostridium phage, named ZWPH-P21 (P21), was isolated from chicken farm sewage in Jiangsu, China. P21 has been shown to specifically lyse C. perfringens type G. Further analysis of basic biological characteristics showed that P21 was stable under the conditions of pH 4-11 and temperature 4-60 °C, and the optimal multiple severity of infection (MOI) was 0.1. In addition, P21 could form a "halo" on agar plates, suggesting that the phage may encode depolymerase. Genome sequence analysis showed that P21 was the most closely related to Clostridium phage CPAS-15 belonging to the Myoviridae family, with a recognition rate of 97.24% and a query coverage rate of 98%. No virulence factors or drug resistance genes were found in P21. P21 showed promising antibacterial activity in vitro and in chicken disinfection experiments. In conclusion, P21 has the potential to be used for preventing and controlling C. perfringens in chicken food production.

Sifting through the core-genome to identify putative cross-protective antigens against Riemerella anatipestifer.

Infectious serositis of ducks, caused by Riemerella anatipestifer, is one of the main infectious diseases that harm commercial ducks. Whole-strain-based vaccines with no or few cross-protection were observed between different serotypes of R. anatipestifer, and so far, control of infection is hampered by a lack of effective vaccines, especially subunit vaccines with cross-protection. Since the concept of reverse vaccinology was introduced, it has been widely used to screen for protective antigens in important pathogens. In this study, pan-genome binding reverse vaccinology, an emerging approach to vaccine candidate screening, was used to screen for cross-protective antigens against R. anatipestifer. Thirty proteins were identified from the core-genome as potential cross-protective antigens. Three of these proteins were recombinantly expressed, and their immunoreactivity with five antisera (anti-serotypes 1, 2, 6, 10, and 11) was demonstrated by Western blotting. Our study established a method for high-throughput screening of cross-protective antigens against R. anatipestifer in silico, which will lay the foundation for the development of a cross-protective subunit vaccine controlling R. anatipestifer infection. KEY POINTS: • Pan-genome binding reverse vaccine approach was first established in R. anatipestifer to screen for subunit vaccine candidates. • Thirty potential cross-protective antigens against R. anatipestifer were identified by this method. • The reliability of the method was verified preliminarily by the results of Western blotting of three of these potential antigens.

Identification of Subunits for Novel Universal Vaccines against Three Predominant Serogroups and the Emerging O145 among Avian Pathogenic Escherichia coli by Pan-RV Pipeline.

Avian pathogenic Escherichia coli, a causative agent of avian colibacillosis, has been causing serious economic losses in the poultry industry. The increase in multidrug-resistant isolates and the complexity of the serotypes of this pathogen, especially the recently reported emergence of a newly predominant serogroup of O145, make the control of this disease difficult. To address this challenge, a high-throughput screening approach, called Pan-RV (Reverse vaccinology based on pangenome analysis), is proposed to search for universal protective antigens against the three traditional serogroups and the newly emerged O145. Using this approach, a total of 61 proteins regarded as probable antigens against the four important serogroups were screened from the core genome of 127 Avian pathogenic Escherichia coli (APEC) genomes, and six were verified by Western blots using antisera. Overall, our research will provide a foundation for the development of an APEC subunit vaccine against avian colibacillosis. Given the exponential growth of whole-genome sequencing (WGS) data, our Pan-RV pipeline will make screening of bacterial vaccine candidates inexpensive, rapid, and efficient. IMPORTANCE With the emergence of drug resistance and the newly predominant serogroup O145, the control of Avian pathogenic Escherichia coli is facing a serious challenge; an efficient immunological method is urgently needed. Here, for the first time, we propose a high-throughput screening approach to search for universal protective antigens against the three traditional serogroups and the newly emerged O145. Importantly, using this approach, a total of 61 proteins regarded as probable antigens against the four important serogroups were screened, and three were shown to be immunoreactive with all antisera (covering the four serogroups), thereby providing a foundation for the development of APEC subunit vaccines against avian colibacillosis. Further, our Pan-RV pipeline will provide immunological control strategies for pathogens with complex and variable genetic backgrounds such as Escherichia coli and will make screening of bacterial vaccine candidates more inexpensive, rapid, and efficient.

Multi-View Learning for Material Classification.

Material classification is similar to texture classification and consists in predicting the material class of a surface in a color image, such as wood, metal, water, wool, or ceramic. It is very challenging because of the intra-class variability. Indeed, the visual appearance of a material is very sensitive to the acquisition conditions such as viewpoint or lighting conditions. Recent studies show that deep convolutional neural networks (CNNs) clearly outperform hand-crafted features in this context but suffer from a lack of data for training the models. In this paper, we propose two contributions to cope with this problem. First, we provide a new material dataset with a large range of acquisition conditions so that CNNs trained on these data can provide features that can adapt to the diverse appearances of the material samples encountered in real-world. Second, we leverage recent advances in multi-view learning methods to propose an original architecture designed to extract and combine features from several views of a single sample. We show that such multi-view CNNs significantly improve the performance of the classical alternatives for material classification.

Binocular measurement method for the continuous casting slab model based on the improved BRISK algorithm.

Due to the low accuracy of the traditional image feature matching algorithm in binocular vision measurement, a binocular measurement method for the continuous casting slab model based on the improved binary robust invariant scalable keypoints (BRISK) algorithm is proposed. First, the feature points of the image are detected. After that, local area sampling and sub-area division are carried out with the feature points as the center, sub-areas with low offset values are removed, and the main direction is obtained by using the centroid of the remaining sub-areas. Then, the gray difference threshold is used to replace the traditional gray value intensity comparison to generate descriptors. Finally, the Hamming distance is used to match the feature points, and the three-dimensional coordinates of the matching points are calculated to complete the measurement. Through comparative experiments, the lowest relative error of the improved algorithm in this paper reaches 0.4723%, which meets the requirement of measurement accuracy.