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AIM: To explore the characteristics of astigmatism in preschool children before, during and after the COVID-19 epidemic, so as to provide a reference for further prevention and control of children's vision. *METHODS: In the consecutive four years from January 2018 to December 2021, a retrospective analysis of vision data was conducted on 2 273 preschool children (4546 eyes) younger than 4 years old who participated in children' s vision screening test in Baiyun district, Guangzhou. They were divided into 1 - year old group (ages<1-year old, 420 cases), 2-year old group (1-year <= ages < 2- year, 543 cases), 3- year old group (2- year <=ages <3-year, 614 cases), and 4 - year old group (3 - year <= ages< 4- year, 696 cases) according to ages. The analysis included astigmatic degrees of children's eyes as well as their conditions of astigmatism. *RESULTS: In 2018, the astigmatic degrees of the both eyes of 1-year-old group were higher than those of other groups (P<0.05). The binocular astigmatic degrees of the preschool children in four groups were obviously higher in 2020 than 2019 (P < 0. 05), while they were significantly decreased in 2021 when compared with 2020 (P < 0. 05). From 2019 to 2020, the increase of astigmatic degrees of the right eye is more considerable than the left eye of preschool children in those four groups (P < 0. 001). Furthermore, the morbidity of astigmatism basically echoes with the changing tendency of astigmatic degrees from 2018 to 2021. *CONCLUSIONS: Preschool children in Baiyun district, Guangzhou, have the highest degree of astigmatism and the fastest progression rate within 1 year old. Before COVID - 19 epidemic, the changes in astigmatism and prevalence were relatively stable;during COVID - 19 epidemic, the astigmatism and prevalence increased significantly and the astigmatic degrees of right eye increased more than that of the left eye;after the normalization of epidemic prevention and control, the astigmatism and prevalence decreased significantly.Copyright © 2023 International Journal of Ophthalmology (c/o Editorial Office). All rights reserved.
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Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from "hydropower–gold” to "smart grid–zinc”, and the systematically influential markets correspondingly become smart grid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits. © 2023 Elsevier Ltd
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With the purpose of risk management for fossil energy investors, this paper examines the dynamic spillover effect and asymmetric connectedness between fossil energy, green financial and major traditional financial markets in China. By employing the spillover index model of Diebold and Yilmaz, a weak correlation between green financial and fossil energy markets is verified, and the market connectedness remains relatively calm despite the COVID-19 pandemic outbreak. Specifically, green bonds receives fewer shocks from crude oil than coal, green stocks receive fewer shocks from coal than crude oil. In addition, rather than the safe-haven characteristics presented by gold, this paper further proves that green bonds also have the potential to act as safe-haven assets, due to the fact that the connectedness between green bonds and energy markets is at low levels. Finally, the magnitude of return spillovers between markets would vary significantly during different periods. The results obtained in this paper have practical implications for both investors and policymakers. Copyright © 2022 Deng, Guan, Zheng, Xing and Liu.
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The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models have been developed to augment the training datasets. Previously proposed generative models usually require manually adjusted annotations (e.g., segmentation masks) or need pre-labeling. However, studies have found that these pre-labeling based methods can induce hallucinating artifacts, which might mislead the downstream clinical tasks, while manual adjustment could be onerous and subjective. To avoid manual adjustment and pre-labeling, we propose a novel controllable and simultaneous synthesizer (dubbed CS$$
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Clinical omics, especially gene expression data, have been widely studied and successfully applied for disease diagnosis using machine learning techniques. As genes often work interactively rather than individually, investigating co-functional gene modules can improve our understanding of disease mechanisms and facilitate disease state prediction. To this end, we in this paper propose a novel Multi-Level Enhanced Graph ATtention (MLE-GAT) network to explore the gene modules and intergene relational information contained in the omics data. In specific, we first format the omics data of each patient into co-expression graphs using weighted correlation network analysis (WGCNA) and then feed them to a well-designed multi-level graph feature fully fusion (MGFFF) module for disease diagnosis. For model interpretation, we develop a novel full-gradient graph saliency (FGS) mechanism to identify the disease-relevant genes. Comprehensive experiments show that our proposed MLE-GAT achieves state-of-the-art performance on transcriptomics data from TCGA-LGG/TCGA-GBM and proteomics data from COVID-19/non-COVID-19 patient sera. © 2021 IEEE.
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Objective: To analyze the spatio-temporal expression profile of angiotensin-converting enzyme 2 (ACE2), the receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in human and mouse testes based on single-cell RNA sequencing (scRNA-Seq). Methods: Ten testicular tissues from humans and nine testicular tissues from C57BL/6 mice with normal developmental stages were collected and digested into single cell suspensions by enzyme, and then the cell-gene expression matrixes were obtained by scRNA-Seq standard processing procedures. After quality control, data standardization, batch effect processing, clustering, and dimensionality reduction, each subgroup of cells was annotated based on known testicular cell bio-markers to clarify the expression patterns and differences of ACE2 in human and mouse testes with normal developmental stages. Results: In this study, nine testicular cell subgroups found in human and mice were identified, including three subgroups of germ cells (spermatogonia, spermatocytes, and spermatids/sperm) and six subgroups of somatic cells (Sertoli cells, macrophages, vascular smooth muscle cells, endothelial cells, Leydig cells, and peritubular myoid cells). In terms of spatial distribution, ACE2 was predominantly expressed in human Sertoli cells in adult testes, and also expressed in Leydig cells, peritubular myoid cells, and germ cells. In terms of time scale, the transcription abundance of ACE2 in human Sertoli cells increased with the development of testis, and the expression level of ACE2 in Sertoli cells after puberty was significantly higher than that in infancy and childhood (P=0.000). Judging from the stages of mouse testicular development, the expression patterns of Ace2 were both significantly different from those in humans. In testis of the 5-week-old adult C57BL/6 mouse, the transcription level of Ace2 was low and it was mainly expressed in vascular smooth muscle cells (P=0.000), while the number of Ace2 positive cell in Sertoli cells was extremely low. Conclusion: SARS-CoV-2 may mainly infects human testis through Sertoli cells, and the conventional C57BL/6 mouse model is not suitable to simulate the effect of SARS-CoV-2 infection on human testicular function.
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OBJECTIVES: Genotyping of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been instrumental in monitoring viral evolution and transmission during the pandemic. The quality of the sequence data obtained from these genotyping efforts depends on several factors, including the quantity/integrity of the input material, the technology, and laboratory-specific implementation. The current lack of guidelines for SARS-CoV-2 genotyping leads to inclusion of error-containing genome sequences in genomic epidemiology studies. We aimed to establish clear and broadly applicable recommendations for reliable virus genotyping. METHODS: We established and used a sequencing data analysis workflow that reliably identifies and removes technical artefacts;such artefacts can result in miscalls when using alternative pipelines to process clinical samples and synthetic viral genomes with an amplicon-based genotyping approach. We evaluated the impact of experimental factors, including viral load and sequencing depth, on correct sequence determination. RESULTS: We found that at least 1000 viral genomes are necessary to confidently detect variants in the SARS-CoV-2 genome at frequencies of >=10%. The broad applicability of our recommendations was validated in over 200 clinical samples from six independent laboratories. The genotypes we determined for clinical isolates with sufficient quality cluster by sampling location and period. Our analysis also supports the rise in frequencies of 20A.EU1 and 20A.EU2, two recently reported European strains whose dissemination was facilitated by travel during the summer of 2020. CONCLUSIONS: We present much-needed recommendations for the reliable determination of SARS-CoV-2 genome sequences and demonstrate their broad applicability in a large cohort of clinical samples.