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This meta-analysis aimed to systematically review and analyze parental awareness of human papillomavirus (HPV) and its vaccine, as well as parental willingness of the HPV vaccine in China. The literature search selected studies that met the following criteria: study published between 2009 and 2023, study design involving parents with at least one child aged ≤ 18 years, sample sizes exceeding 300, availability of data on parental willingness of the HPV vaccine or sufficient information to calculate effect sizes, and studies published in either English or Chinese. Studies that did not meet one of the above points were excluded. From an initial pool of 660 papers, 33 studies were included, encompassing a total sample size of 92,802. The analysis revealed that the pooled awareness rates of HPV and the HPV vaccine among Chinese parents were 45.0% (95% CI: 36.1-54.0%) and 41.4% (95%CI: 30.7-52.5%), respectively. The overall parental willingness for vaccinating children against HPV was 61.0% (95% CI: 53.5-68.3%). Both parental awareness and willingness of the HPV vaccine in China were found to remain low when compared to other countries.

Background: Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures. Methods: We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance. Results: During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance. Conclusion: In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

The discrete grey modelling technique is a novel methodology of grey prediction models, which is effective to improve the effectiveness and applicability of grey models. In order to build a more general and effective univariate grey prediction model, the discrete grey modelling technique is utilised in this paper to build a Quadratic Polynomial Discrete Grey Model (QPDGM). The properties of the QPDGM have been discussed, which indicate that the new model can be regarded as an extension of the conventional discrete grey model and nonhomogeneous grey model, and it is also coincidence with three classes of exponential sequences. The QPDGM is finally applied to predict the energy consumption of China, including the electric power, crude oil and natural gas consumptions. The results have been compared to some commonly used univariate grey prediction models, which indicates the QPDGM is generally more accurate than other models. [ABSTRACT FROM AUTHOR]

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Trans/Form/Ação is the property of Trans/Form/Acao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Heavy metal(loid)s (HMs) pollution has become a common and complex problem in industrial parks due to rapid industrialization and urbanization. Here, soil and groundwater were sampled from a retired industrial park to investigate the pollution characteristics of HMs. Results show that Ni, Pb, Cr, Zn, Cd, and Cu were the typical HMs in the soil. Source analysis with the positive matrix factorization model indicates that HMs in the topsoil stemmed from industrial activities, traffic emission, and natural source, and the groundwater HMs originated from industrial activities, groundwater-soil interaction, groundwater-rock interaction, and atmosphere deposition. The sequential extraction of soil HMs reveals that As and Hg were mainly distributed in the residue fraction, while Ni, Pb, Cr, Zn, Cd, and Cu mainly existed in the mobile fraction. Most HMs either in the total concentration or in the bioavailable fraction preferred to retain in soil as indicated by their high soil-water partitioning coefficients (K d ), and the K d values were correlated with soil pH, groundwater redox potential, and dissolved oxygen. The relative stable soil-groundwater circumstance and the low active fraction contents limited the vertical migration of soil HMs and their release to groundwater. These findings increase our knowledge about HMs pollution characteristics of traditional industrial parks and provide a protocol for HMs pollution scrutinizing in large zones., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Aerobic anoxygenic phototrophic bacteria (AAPB) are significant bacterial groups in aquatic ecosystems, known for their rapid growth and photoheterotrophic characteristics. However, the distribution and ecological assembly process of AAPB in low irradiation freshwater basins remain unclear, warranting further investigation. In this study, we present the diversity, abundance, spatial variations, ecological process, and community interaction of AAPB in sediment of Three Gorges Reservoir (TGR) under low irradiation. Our findings demonstrate the dominant genera of AAPB community that exist in the TGR area also are appeared in different waters, with some regional preference. Moreover, the concentration of pufM gene, an indicator for AAPB, maintains a consistently high numerical level ranging from (2.21 ± 0.44) × 10 4 to (9.98 ± 0.30) × 10 7 gene copies/g. Although solar irradiation is suggested as the major factor affecting AAPB, it remains unclear whether and how AAPB differ between regions due to varying solar irradiation levels. Our results show spatial differences between total bacteria and AAPB communities, with significant differences observed only in AAPB. Geographical and environmental factor contributed less than 10% to the spatial difference of community, with sediment type and environmental factors being the key factors influencing microbial community structure. The stochastic process plays a dominant role in the aggregation and replacement of AAPB communities, among which the most contribution is dispersal limitation. For AAPB network, Yoonia and Gemmobacter are the hubs for modules. Those results valuable insights into the AAPB communities in TGR with low irradiation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)