Genetic variation and evolutionary characteristics of Echovirus 11: new variant within genotype D5 associated with neonatal death found in China.

Echovirus 11 (E11) has gained attention owing to its association with severe neonatal infections. From 2018 to 2023, a surge in severe neonatal cases and fatalities linked to a novel variant of genotype D5 was documented in China, France, and Italy. However, the prevention and control of E11 variants have been hampered by limited background data on the virus circulation and genetic variance. Therefore, the present study investigated the circulating dynamics of E11 and the genetic variation and molecular evolution of genotype D5 through the collection of strains from the national acute flaccid paralysis (AFP) and hand, foot, and mouth disease (HFMD) surveillance system in China during 2000-2022 and genetic sequences published in the GenBank database. The results of this study revealed a prevalent dynamic of E11 circulation, with D5 being the predominant genotype worldwide. Further phylogenetic analysis of genotype D5 indicated that it could be subdivided into three important geographic clusters (D5-CHN1: 2014-2019, D5-CHN2: 2016-2022, and D5-EUR: 2022-2023). Additionally, variant-specific (144) amino acid mutation sites and positive-selection pressure sites (132, 262) were identified in the VP1 region. Cluster-specific recombination patterns were also identified, with CVB5, E6, and CVB4 as the major recombinant viruses. These findings provide a preliminary landscape of E11 circulation worldwide and basic scientific data for further study of the pathogenicity of E11 variants.

Multi-step-ahead and interval carbon price forecasting using transformer-based hybrid model.

Accurate and stable carbon price forecasts serve as a reference for assessing the stability of the carbon market and play a vital role in enhancing investment and operational decisions. However, realizing this goal is still a significant challenge, and researchers usually ignore multi-step-ahead and interval forecasting due to the non-linear and non-stationary characteristics of carbon price series and its complex fluctuation features. In this study, a novel hybrid model for accurately predicting carbon prices is proposed. The proposed model combines multi-step-ahead and interval carbon price forecasting based on the Hampel identifier (HI), time-varying filtering-based empirical mode decomposition (TVFEMD), and transformer model. First, HI identifies and corrects outliers in carbon price. Second, TVFEMD decomposes carbon price into several intrinsic mode functions (imfs) to reduce the non-linear and non-stationarity of carbon price to obtain more regular features in series. Next, these imfs are reconstructed by sample entropy (SE). Subsequently, the orthogonal array tuning method is used to optimize the transformer model's hyperparameters to obtain the optimal model structure. Finally, after hyperparameter optimization and quantile loss function, the transformer is used to perform multi-step-ahead and interval forecasting on each part of the reconstruction, and the final prediction result is obtained by summing them up. Five pilot carbon trading markets in China were selected as experimental objects to verify the proposed model's prediction performance. Various benchmark models and evaluation indicators were selected for comparison and analysis. Experimental results show that the proposed HI-TVFEMD-transformer hybrid model achieves an average MAE of 0.6546, 1.3992, 1.6287, and 2.2601 for one-step, three-step, five-step, and ten-step-ahead forecasting, respectively, which significantly outperforms other models. Furthermore, interval forecasts almost always have a PICI above 0.95 at a confidence interval of 0.1, thereby indicating the effectiveness of the hybrid model in describing the uncertainty in the forecasts. Therefore, the proposed hybrid model is a reliable carbon price forecasting tool that can provide a dependable reference for policymakers and investors.

Aquaporin 5 maintains lens transparency by regulating the lysosomal pathway using circRNA.

The lens is transparent, non-vascular, elastic and wrapped in a transparent capsule. The lens oppacity of AQP5-/- mice was increased more than that of wild-type (AQP5+/+ ) mice. In this study, we explored the potential functional role of circular RNAs (circRNAs) and transcription factor HSF4 in lens opacity in aquaporin 5 (AQP5) knockout (AQP5-/- ) mice. Autophagy was impaired in the lens tissues of AQP5-/- mice. Autophagic lysosomes in lens epithelial cells of AQP5-/- mice were increased compared with AQP5+/+ mice, based on analysis by transmission electron microscopy. The genetic information of the mice lens was obtained by high-throughput sequencing, and then the downstream genes were analysed. A circRNA-miRNA-mRNA network related to lysosomal pathway was constructed by the bioinformatics analysis of the differentially expressed circRNAs. Based on the prediction of the TargetScan website and the validation by dual luciferase reporter assay and RNA immunoprecipitation-qPCR, we found that circRNA (Chr16: 33421321-33468183+) inhibited the function of HSF4 by sponging microRNA (miR-149-5p), and it downregulated the normal expression of lysosome-related mRNAs. The accumulation of autophagic lysosome may be one of the reasons for the abnormal development of the lens in AQP5-/- mice.

Transcriptomic and metabolomic analyses reveal how girdling promotes leaf color expression in Acer rubrum L.

BACKGROUND: Acer rubrum L. (red maple) is a popular tree with attractive colored leaves, strong physiological adaptability, and a high ornamental value. Changes in leaf color can be an adaptive response to changes in environmental factors, and also a stress response to external disturbances. In this study, we evaluated the effect of girdling on the color expression of A. rubrum leaves. We studied the phenotypic characteristics, physiological and biochemical characteristics, and the transcriptomic and metabolomic profiles of leaves on girdled and non-girdled branches of A. rubrum. RESULTS: Phenotypic studies showed that girdling resulted in earlier formation of red leaves, and a more intense red color in the leaves. Compared with the control branches, the girdled branches produced leaves with significantly different color parameters a*. Physiological and biochemical studies showed that girdling of branches resulted in uneven accumulation of chlorophyll, carotenoids, anthocyanins, and other pigments in leaves above the band. In the transcriptomic and metabolomic analyses, 28,432 unigenes including 1095 up-regulated genes and 708 down-regulated genes were identified, and the differentially expressed genes were mapped to various KEGG (kyoto encyclopedia of genes and genomes) pathways. Six genes encoding key transcription factors related to anthocyanin metabolism were among differentially expressed genes between leaves on girdled and non-girdled branches. CONCLUSIONS: Girdling significantly affected the growth and photosynthesis of red maple, and affected the metabolic pathways, biosynthesis of secondary metabolites, and carbon metabolisms in the leaves. This resulted in pigment accumulation in the leaves above the girdling site, leading to marked red color expression in those leaves. A transcriptome analysis revealed six genes encoding anthocyanin-related transcription factors that were up-regulated in the leaves above the girdling site. These transcription factors are known to be involved in the regulation of phenylpropanoid biosynthesis, anthocyanin biosynthesis, and flavonoid biosynthesis. These results suggest that leaf reddening is a complex environmental adaptation strategy to maintain normal metabolism in response to environmental changes. Overall, the results of these comprehensive phenotype, physiological, biochemical, transcriptomic, and metabolomic analyses provide a deeper and more reliable understanding of the coevolution of red maple leaves in response to environmental changes.

Screening and Bioinformatics Analysis of IgA Nephropathy Gene Based on GEO Databases.

PURPOSE: To identify novel biomarkers of IgA nephropathy (IgAN) through bioinformatics analysis and elucidate the possible molecular mechanism. METHODS: The GSE93798 and GSE73953 datasets containing microarray data from IgAN patients and healthy controls were downloaded from the GEO database and analyzed by the GEO2R web tool to obtain different expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI), and Biological Networks Gene Oncology tool (BiNGO) were then performed to elucidate the molecular mechanism of IgAN. RESULTS: A total of 223 DEGs were identified, of which 21 were hub genes, and involved in inflammatory response, cellular response to lipopolysaccharide, transcription factor activity, extracellular exosome, TNF signaling pathway, and MAPK signaling pathway. CONCLUSIONS: TNF and MAPK pathways likely form the basis of IgAN progression, and JUN/JUNB, FOS, NR4A1/2, EGR1, and FOSL1/2 are novel prognostic biomarkers of IgAN.