Isolation and identification of a pigeonpox virus strain and study on the integration of reticuloendotheliosis virus sequence.

In order to study the integration of reticuloendotheliosis virus (REV) in pigeonpox virus (PPV), we collected suspected pigeonpox disease material, amplified the 4b core protein gene of PPV, the gp90 gene of REV, and the integrated sequence fragments from the end of the ORF201 segment of PPV to the beginning of the LTR of REV, and sequenced these genes. The results showed that a 4b core protein fragment of 332 bp was amplified and identified as pigeonpox virus, which was named SX/TY/LTR 01/2023. Sequence analysis showed that the pigeonpox virus isolate belonged to genotype A2, which was the closest to the domestic CVL strain, with a identity of 99.4%. A band of 1191 bp was amplified from the gp90 gene of REV, named SX/TY/PPV-REV01/2023, and sequence analysis indicated that REV belonged to genotype III. The sequence analysis showed that REV belonged to genotype III, and belonged to the same large branch as the domestic isolates JSRD0701 and LNR0801, with 99.3% identity. The integrated sequence fragment was amplified to a band of 637 bp, which determined that the REV sequence was integrated in the PPV rather than a mixed infection of the two viruses. This indicates that REV was integrated in this isolation of PPV, suggesting that pigeon farms need to prevent reticuloendotheliosis at the same time when preventing pigeonpox.

Asymmetric double networks mutual teaching for unsupervised person Re-identification.

Unsupervised person re-identification (Re-ID) has always been challenging in computer vision. It has received much attention from researchers because it does not require any labeled information and can be freely deployed to new scenarios. Most unsupervised person Re-ID research studies produce and optimize pseudo-labels by iterative clustering algorithms on a single network. However, these methods are easily affected by noisy labels and feature variations caused by camera shifts, which will limit the optimization of pseudo-labels. In this paper, we propose an Asymmetric Double Networks Mutual Teaching (ADNMT) architecture that uses two asymmetric networks to generate pseudo-labels for each other by clustering, and the pseudo-labels are updated and optimized by alternate training. Specifically, ADNMT contains two asymmetric networks. One network is a multiple granularity network, which extracts pedestrian features of multiple granularity that correspond to numerous classifiers, and the other network is a conventional backbone network, which extracts pedestrian features that correspond to a classifier. Furthermore, because the camera style changes seriously affect the generalization ability of the proposed model, this paper designs Similarity Compensation of Inter-Camera (SCIC) and Similarity Suppression of Intra-Camera (SSIC) according to the camera ID of the pedestrian images to optimize the similarity measure. Extensive experiments on multiple Re-ID benchmark datasets show that our proposed method achieves superior performance compared with the state-of-the-art unsupervised person re-identification methods.

Effect of sanxan gel on the quality of salt-free noodles during cooking.

The effect and mechanism of sanxan on the quality of salt-free noodles (SFNs) were investigated from different cooking stages (initial stage, 1 min; optimum cooking time, OCT; overcooked time, OT). The results showed significant changes in the cooking process with the addition of 1.2% sanxan. The OCT for noodles with 1.2% sanxan (experimental group, EG) was extended from 5 to 7 min compared to the non-added noodles (blank group, BG) and 1.5% salt-containing noodles (control group, CG). The hardness and adhesiveness of BG, EG, and CG all decreased significantly during cooking. In contrast, the springiness, maximum tensile strength, and tensile fracture distance trended first to increase and then to decrease. At OCT, EG had the highest hardness (3971.69 ± 94.49 g), adhesiveness (372.26 ± 33.56 g s), and maximum tensile strength (41.51 ± 2.76 g), which remained large even after overcooking. However, those in BG and CG showed a significant reduction (p < 0.05). The proportion of free water increased progressively as cooking progressed, with CG showing the largest increase, from 82.29% to 91.19%, whereas EG showed the smallest increase, from 78.34% to 86.02%. During the cooking process, the addition of sanxan delayed the water migration, whereas salt promoted it. Sensory evaluation showed that EG was smoother in appearance than BG and tasted malty with a slight stickiness. Moreover, EG had the smallest k1 and C∞ values. Thus, sanxan is an effective additive to enhance the quality of SFNs and can replace the role of salt in noodles in some properties, which is beneficial for the development of SFNs.