Detection and genetic evolution analysis of porcine parvovirus type 7 (PPV7) in Fujian Province.

Porcine parvovirus (PPV) is an important pathogen causing reproductive disorders in sows, with clinical symptoms including stillbirth, mummified fetuses, embryonic dysplasia and death, and sow infertility. Porcine parvovirus 7 (PPV7) is a recently discovered type of PPV and its widespread distribution and rapid evolution has caused huge economic losses in the pig industry. To investigate the molecular epidemiology of PPV7 in Fujian Province, China, we collected 491 blood samples and 72 tissue samples from diseased pigs in large-scale pig farms across selected areas of Fujian Province from 2019 to 2022. PPV7 infection was determined using real-time quantitative PCR, and positive samples underwent whole-genome amplification, sequencing, and subsequent homology, phylogenetic, and recombination analyses. The PPV7 positive detection rate was 25.73% (145/563) in Fujian Province, among which the positive rate of blood and tissue samples was 26.47% (130/491) and 20.83% (15/72), respectively. The nucleotide sequence homology among the 29 PPV7 whole-genome sequences obtained in this study was 90.0%-97.2%, whereas that with 128 reference strains from China and other countries was 88.9%-98.1%. Six strains had partial nucleotide deletions or insertions. Phylogenetic analysis based on the whole-genome sequences classified the 29 PPV7 strains and 128 reference strains into eight subtypes (PPV7a-PPV7h), and PPV7h was the predominant subtype in Fujian Province. Recombination analysis revealed evidence of inferred recombination events in the genomes of four strains. This study provides significant insights into the molecular characteristics of PPV7 in Fujian Province and serves as a crucial foundation for further advancements in PPV7 prevention and control strategies.

Genetic and epidemic characteristics of porcine parvovirus 7 in the Fujian and Guangdong regions of southern China.

Porcine parvovirus (PPV) is the primary cause of reproductive disorders in pigs. The porcine parvovirus 7 (PPV7) subtype was first identified in the United States in 2016. In this study, PPV7 was detected in different porcine samples, including serum, feces, saliva, and milk, from 69 pig farms in the Fujian and Guangdong regions of South China, and its coinfection with porcine circovirus 2 (PCV2), porcine circovirus 3 (PCV3), and porcine reproductive and respiratory syndrome virus (PRRSV) was determined. Whole-genome sequencing, phylogenetic analysis, and recombination analysis were performed on seven isolates, with each selected isolate originating from a different farm. There was a high rate of PPV7 positivity in blood, stool, and saliva but PPV7 DNA was absent from breast milk. The findings also showed that PPV7-positive samples had a high rate of coinfection with PCV2, PCV3, and PRRSV. Real-time PCR was used to determine the viral copy numbers of PCV2, PCV3, PRRSV, and PPV7 in serum samples and to assess whether PPV7 affected PCV2, PCV3, and PRRSV viral loads. Phylogenetic analysis showed that PPV7e and PPV7f were the most prevalent and widespread subtypes in the Fujian and Guangdong regions, respectively. While the PPV7a, PPV7b, PPV7c, and PPV7f subtypes were most prevalent in Fujian Province, PPV7a-e subtypes were prevalent in Guangdong, indicating that PPV7 has rich genetic diversity in these regions. A putative recombinant strain, 21FJ09, was identified using SimPlot and the Recombination Detection Program 4 software.

Inspection Method of Rope Arrangement in the Ultra-Deep Mine Hoist Based on Optical Projection and Machine Vision.

Due to the importance of safety detection of the drum's rope arrangement in the ultra-deep mine hoist and the current situation whereby the speed, accuracy and robustness of rope routing detection are not up to the requirements, a novel machine-vision-detection method based on the projection of the drum's edge is designed in this paper. (1) The appropriate position of the point source corresponding to different reels is standardized to obtain better projection images. (2) The corresponding image processing and edge curve detection algorithm are designed according to the characteristics of rope arrangement projection. (3) The Gaussian filtering algorithm is improved to adapt to the situation that the curve contains wavelet peak noise when extracting the eigenvalues of the edge curve. (4) The DBSCAN (density-based spatial clustering of applications with noise) method is used to solve the unsupervised classification problem of eigenvalues of rope arrangement, and the distance threshold is calculated according to the characteristics of this kind of data. Finally, we can judge whether there is a rope arranging fault just through one frame and output the location and number of the fault. The accuracy and robustness of the method are verified both in the laboratory and the ultra-deep mine simulation experimental platform. In addition, the detection speed can reach 300 fps under the premise of stable detection.

Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM.

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization-support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

[Present study on the forensic medicine diagnosis of the sudden erethistic death].

The death caused of anaphylactic shock is common in clinical medicine and medicolegal expertise, but it is a nodus to diagnose sudden death from allergy. In recent years, to provide objective and precise morphological evidence and index of diagnosis for sudden death from allergy, scholars of internal and overseas studied the content of IgE, HT, mast cell tryptase and SP in the serum of the death died of anaphylactic shock, and their immune express in lung and stomach intestine. In this text we reviewed the present study and existing problems of the forensic medicine diagnosis of the sudden erethistic death.