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Carbon market is the most effective market tool for carbon emission reduction. China, the largest carbon emitter in the world, established the national carbon market in 2021, covering over 2000 key units in the power sector. Therefore, the forecasting of carbon price has profound implications for environmental and energy policies. In this paper, two traditional econometric model and three kinds of machine learning (ML) algorithms are used to predict carbon returns in the Chinese carbon trading market based on carefully selected predictors. Among all forecasting models, the Random Forest (RF) has the best forecasting performance, followed by some GARCH models using various factors. Compared with the traditional benchmark of ARMA model, the BP neural network (BP) and the GA improved BP method (GA-BP) are less competitive in predicting carbon returns in China because of their large forecasting errors. According to the optimal model, social attention to the carbon market, the international crude oil returns and the overall performance of the stock market are the most important predictors. The findings are robust to the change in the sample set. Overall, the ML approach shows an advantage in forecasting carbon returns, but the selection of predictors is also important. • Machine learning and econometric models are used to forecast carbon returns in China. • The selection of predictors is based on economic analysis. • The Random Forest (RF) outperforms the other ML and econometric methods. • The energy market is extremely important for predicting carbon returns. • Social concerns and stock market performance improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

Background: The rising prevalence of herpes simplex type 2 (HSV-2) infection poses a growing global public health challenge. A comprehensive understanding of its epidemiology and burden disparities in China is crucial for informing targeted and effective intervention strategies in the future., Methods: We followed Cochrane and PRISMA guidelines for a systematic review and included publications published in Chinese and English bibliographic systems until March 31 st , 2024. We synthesized HSV-2 seroprevalence data across different population types. We used random-effects models for meta-analyses and conducted meta-regression to assess the association between population characteristics and seroprevalence., Results: Overall, 23,999 articles were identified, and 402 publications (1,203,362 participants) that reported the overall seroprevalence rates (858 stratified measures) were included. Pooled HSV-2 seroprevalence among the general population (lower risk) was 7.7% (95% CI: 6.8-8.7%). Compared to the general population, there is a higher risk of HSV-2 prevalence among intermediate-risk populations (14.8%, 95% CI: 11.0-19.1%), and key populations (31.7%, 95% CI: 27.4-36.1%). Female sexual workers (FSWs) have the highest HSV-2 risk (ARR:1.69, 95% CI: 1.61-1.78). We found northeastern regions had a higher HSV-2 seroprevalence than other regions (17.0%, 95% CI: 4.3-35.6%, ARR: 1.38, 95% CI: 1.26-1.50, Northern China as the reference group). This highlighted the disparity by population risk levels and regions. We also found lower HSV-2 prevalence estimates in publications in Chinese bibliographic databases than those in English databases among key populations (such as MSM and HIV-discordant populations)., Conclusion: There is a gradient increase in HSV-2 prevalence risk stratification. We also identified region, population, and age disparities and heterogeneities by publication language in the HSV-2 burden. This study provides guidance for future HSV-2 prevention to eliminate disparities of HSV-2 infection and reduce overall HSV-2 burden., Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=408108, identifier CRD42023408108., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Yan, Ai, Jia, Fan, Hu, Dai, Xue, Li and Tang.)

Pig to human xenotransplantation is considered to be a possible approach to alleviate the shortage of human allografts. Porcine endogenous retrovirus (PERV) is the most significant pathogen in xenotransplantation. We screened for pigs that consistently did not transmit human-tropic replication competent PERVs (HTRC PERVs), namely, non-transmitting pigs. Then, we conducted whole-genome resequencing and full-length transcriptome sequencing to further investigate the sequence characteristics of one non-transmitting pig. Using in vitro transmission assays, we found 5 (out of 105) pigs of the Chinese Wuzhishan minipig inbred line that did not transmit PERV to human cells, i.e., non-transmitting pigs. Whole-genome resequencing and full-length transcriptome sequencing of one non-transmitting pig showed that all of the pol genes were defective at both the genome and transcript levels. We speculate that the defective PERV pol genes in this pig might be attributable to the long-term inbreeding process. This discovery is promising for the development of a strain of highly homozygous and genetically stable pigs with defective PERV pol genes as a source animal species for xenotransplantation., Competing Interests: Authors SF and JY are employed by Grand Life Science and Technology, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Ma, Jia, Fan, Lu, Zhao, Zhong, Yang, Ma, Wang, Lv, Yang, Mou, Dai, Feng and Zhang.)