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Background: Measles and rubella are vaccine-preventable diseases targeted for elimination in most World Health Organization regions, and China is considered to have momentum towards measles elimination. Therefore, this study aimed to assess the population immunity levels against measles and rubella in Zhejiang Province in China in order to provide valuable insights for informing future public health measures and contributing to the ongoing global campaign against these diseases. Materials and methods: A cross-sectional serological survey was conducted in 2022. A total of 2740 blood samples were collected from healthy individuals spanning the age range of 0–59 years, representing diverse demographic strata across 11 prefectures in Zhejiang Province in China. The sera were tested for measles and rubella IgG antibodies to determine positivity rates and geometric mean concentrations (GMCs). Results: The overall positivity rate for the measles IgG antibody was 85.3%, with a GMC of 588.30 mIU/mL. The positivity rate for the rubella IgG antibody was 70.9%, and the GMC was 35.30 IU/mL. Measles IgG antibody positivity rates across the 0–11 months, 12–23 months, 24–35 months, 3–5 years, 6–9 years, 10–14 years, 15–19 years, 20–29 years, and 30–59 years age groups were 63.1%, 92.5%, 97.0%, 94.0%, 85.8%, 77.3%, 86.9%, 84.9%, and 88.7%, respectively (trend χ2 = 118.34, p < 0.001). Correspondingly, rubella antibody positivity rates for these same age brackets were 55.9%, 87.9%, 94.7%, 88.2%, 69.9%, 54.2%, 72.6%, 67.5%, and 74.3% (trend χ2 = 199.18, p < 0.001). Both univariate and multivariate analyses consistently demonstrated that age, immunization history, and differing economic levels were significant factors contributing to variations in antibody levels. Conclusions: The seroprevalence of measles and rubella was lower than that required for herd immunity. Periodic vaccination campaigns should be launched to increase immunity.

The human papillomavirus (HPV) vaccine reduces the prevalence of genital warts and the cancers they are associated with in males. However, the vaccination of males has always been neglected. Here, we performed a meta-analysis to comprehend the acceptability of the HPV vaccine in men and the factors impacting vaccination intentions. We searched PubMed, Web of Science, Embase, Ovid, CNKI, and Wan Fang up to 5 July 2023 for studies that reported HPV vaccine acceptance among men. A random effects model was used to obtain the pooled acceptance rate, and subgroup analysis was performed. Then, the influencing factors of HPV vaccination in males were analyzed. A total of 57 studies with 32,962 samples were included in the analysis. The overall acceptance rate of the HPV vaccine in general men was 47.04% (95% confidence interval [95%CI]: 39.23–54.93%), and 62.23% (95% CI: 52.93–71.10%) among those whose sexual orientation contained men. HPV vaccine acceptance rates differed significantly between the two populations (p = 0.01). The population with a medical background (46.23%, 95% CI: 40.28–52.24%) was more willing to be vaccinated. In comparison to the employed population (66.93%, 95% CI: 48.79–82.81%) and the unemployed (68.44%, 95% CI: 52.82–82.23%), vaccination acceptance rates were lower among students (47.35%, 95% CI: 37.00–57.81%) (p = 0.04). The most significant barriers to vaccination were perceived low risk of infection for themselves (45.91%, 95% CI: 31.73–60.43%), followed by vaccine cost (43.46%, 95% CI: 31.20–56.13%). Moreover, the recommendations from medical professionals (60.90%, 95%CI: 44.23–76.37%) and sexual partners (60.09%, 95%CI: 27.11–88.67%) were significant factors in promoting vaccination. Overall, acceptance of the HPV vaccine among general men was at a lower level, despite being slightly higher among men with a same-sex orientation. Popularizing knowledge about diseases and vaccines, encouraging medical professionals to recommend vaccines to men, or reducing the cost of vaccines may promote HPV vaccination.

Lemon is an important economic crop in the world and can bloom several times each year. The Dehong prefecture of Yunnan province is one of the main lemon-producing areas in China, and is a suitable area for planting high-quality lemons in China. However, the hot and rainy summers in Yunnan are not conducive to flower bud differentiation, which leads to low lemon yield. Therefore, normal flower bud differentiation is important to guarantee lemon production and quality. In this study, we selected some lemon trees for a pot experiment, and we sprayed the lemon leaves with gibberellin and paclobutrazol to regulate lemon flower formation. We set four separate concentration gradients for each regulator: 50, 100, 150, and 200 mg/L. The PBZ concentration gradients were 200, 400, 600, and 1200 mg/L. After the experiment, we determined and analyzed the morphological index and fruit quality of the lemon trees. The results showed that under the same cultivation and management conditions, spraying paclobutrazol substantially inhibited the growth of lemon shoots, increased the fruit setting rate, and improved the fruit yield. However, gibberellin considerably reduced the number of lemon flowering branches and promoted the vegetative growth of the lemons. When the concentration of paclobutrazol was 600 mg/L, the amount of lemon fruits reached the maximum, which remarkably increased the titratable acid and soluble solids contents of the fruit and ultimately increased the fruit quality compared with the control. Altogether, selecting the appropriate concentration of regulators to control the flowering and fruit setting of fruit trees is important and has value in guiding actual production.

Potato common scab, an economically important disease worldwide, is caused by pathogenic Streptomyces strains mainly through the effects of thaxtomin. The cello-oligosaccharides binding protein CebE is proposed as a gateway to the pathogenic development of Streptomyces scabiei. In this study, two functional CebE encoding genes, GEO5601 and GEO7671, were identified in pathogenic Streptomyces sp. AMCC400023. With a higher binding affinity towards signal molecules, the deletion of GEO5601 severely impaired thaxtomin-producing capacity and reduced the strain’s pathogenicity. Transcriptional analysis confirmed that CebE5601 is also responsible for the import and provision of carbon sources for cell growth. With lower binding affinity, the pathogenicity island (PAI)-localized CebE7671 may assume a new function of mediating the biological process of sporulation, given the significantly impaired formation of ΔGEO7671 spores. The mechanisms of action of CebE proteins unraveled in Streptomyces sp. AMCC400023 will help pave the way for more effective prevention of the potato common scab disease.

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have been widely employed and achieved promising performance. However, CNN-based methods face difficulties in achieving both accurate and efficient HSI classification due to their limited receptive fields and deep architectures. To alleviate these limitations, we propose an effective HSI classification network based on multi-head self-attention and spectral-coordinate attention (MSSCA). Specifically, we first reduce the redundant spectral information of HSI by using a point-wise convolution network (PCN) to enhance discriminability and robustness of the network. Then, we capture long-range dependencies among HSI pixels by introducing a modified multi-head self-attention (M-MHSA) model, which applies a down-sampling operation to alleviate the computing burden caused by the dot-product operation of MHSA. Furthermore, to enhance the performance of the proposed method, we introduce a lightweight spectral-coordinate attention fusion module. This module combines spectral attention (SA) and coordinate attention (CA) to enable the network to better weight the importance of useful bands and more accurately localize target objects. Importantly, our method achieves these improvements without increasing the complexity or computational cost of the network. To demonstrate the effectiveness of our proposed method, experiments were conducted on three classic HSI datasets: Indian Pines (IP), Pavia University (PU), and Salinas. The results show that our proposed method is highly competitive in terms of both efficiency and accuracy when compared to existing methods.

Infrared thermography (IRT), is one of the most interesting techniques to identify different kinds of defects, such as delamination and damage existing for quality management of material. Objective detection and segmentation algorithms in deep learning have been widely applied in image processing, although very rarely in the IRT field. In this paper, spatial deep-learning image processing methods for defect detection and identification were discussed and investigated. The aim in this work is to integrate such deep-learning (DL) models to enable interpretations of thermal images automatically for quality management (QM). That requires achieving a high enough accuracy for each deep-learning method so that they can be used to assist human inspectors based on the training. There are several alternatives of deep Convolutional Neural Networks for detecting the images that were employed in this work. These included: 1. The instance segmentation methods Mask–RCNN (Mask Region-based Convolutional Neural Networks) and Center–Mask; 2. The independent semantic segmentation methods: U-net and Resnet–U-net; 3. The objective localization methods: You Only Look Once (YOLO-v3) and Faster Region-based Convolutional Neural Networks (Fast-er-RCNN). In addition, a regular infrared image segmentation processing combination method (Absolute thermal contrast (ATC) and global threshold) was introduced for comparison. A series of academic samples composed of different materials and containing artificial defects of different shapes and nature (flat-bottom holes, Teflon inserts) were evaluated, and all results were studied to evaluate the efficacy and performance of the proposed algorithms.

Ceramic-coated materials used in different engineering sectors are the focus of world-wide interest and have generated a need for inspection techniques that detect very small structural anomalies. Non-destructive testing is increasingly being used to evaluate coating thickness and to test for coating flaws. The main pros of non-destructive testing is that the tested object remains intact and available for continued use afterward. This paper reports on an integrated, non-destructive testing approach that combines infrared thermography and acousto-ultrasonics to evaluate advanced aerospace sandwich structure materials with the aim of exploring any potential for detecting defects of more than one type. Combined, these two techniques successfully detected fabrication defects, including inclusions and material loss.