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The arrival of the dual-carbon era indicates that it is imperative to reduce industrial carbon emissions with high carbon emissions. The implementation of carbon emission reduction will inevitably have an impact on industrial development. Three different carbon emission scenarios were established. Based on the average annual carbon emission growth rate of the past decade, it is set as a medium carbon scenario. According to the economic development stage and actual social development situation of each region, the corresponding annual average carbon emission growth rate is increased and decreased to set up high carbon and low carbon scenarios. The adjacent accumulation gray multivariate model for industries is established based on different scenarios. The results indicate that the carbon emission scenarios most suitable for industrial development in various regions are different. Heavy industrial provinces and cities such as Beijing, Tianjin and Hebei have the highest industrial output value under high carbon scenarios; Provinces and cities with high levels of industrialization, such as Jiangsu, Shanghai, Guangdong and Chongqing have the highest industrial output value under low-carbon scenarios. The industrial output value of Hebei is expected to grow the fastest under the high carbon scenario, reaching 2.15 trillion yuan by 2030. The industrial output value of Guangdong achieves the greatest improvement under low-carbon scenario. It is expected that the industrial output value of which will reach 6.86 trillion yuan by 2030, ranking first in the country. Therefore, the setting of carbon emission scenarios needs to be tailored to local conditions, increase technological innovation efforts, and further promote industrial development. [ABSTRACT FROM AUTHOR]

Influenza viruses have been posing a great threat to public health and animal industry. The developed vaccines have been widely used to reduce the risk of potential pandemic; however, the ongoing antigenic drift makes influenza virus escape from host immune response and hampers vaccine efficacy. Until now, the genetic basis of antigenic variation remains largely unknown. In this study, we used A/swine/Guangxi/18/2011 (GX/18) and A/swine/Guangdong/104/2013 (GD/104) as models to explore the molecular determinant for antigenic variation of Eurasian avian‐like H1N1 (EA H1N1) swine influenza viruses (SIVs) and found that the GD/104 virus exhibited 32‐ to 64‐fold lower antigenic cross‐reactivity with antibodies against GX/18 virus. Therefore, we generated polyclonal antibodies against GX/18 or GD/104 virus and a monoclonal antibody (mAb), named mAb102‐95, targeted to the haemagglutinin (HA) protein of GX/18 virus and found that a single amino acid substitution at position 158 in HA protein substantially altered the antigenicity of the virus. The reactivity of GX/18 virus containing G158E mutation with the mAb102‐95 decreased eightfold than that of the parental strain. Contrarily, the reactivity of GD/104 virus bearing E158G mutation with the mAb102‐95 increased by 32 times as compared with that of the parental virus. Structural analysis showed that the amino acid mutation from G to E was accompanied with the R group changing from ‐H to ‐(CH2)2‐COOH. The induced steric effect and increased hydrophilicity of HA protein surface probably jointly contributed to the antigenic drift of EA H1N1 SIVs. Our study provides experimental evidence that G158E mutation in HA protein affects the antigenic property of EA H1N1 SIVs and widens our horizon on the antigenic drift of influenza virus. [ABSTRACT FROM AUTHOR]

Purpose: Gestational diabetes mellitus (GDM) negatively affects the quality of life of pregnant women and is influenced by several factors. Research to date treats pregnant women with gestational diabetes as a homogeneous group based on their quality of life. We attempted to identify subgroups based on self-reported quality of life and explored variables associated with subgroups. Methods: From September 1, 2020 to November 29, 2020, pregnant women with GDM from two hospitals in Guangdong Province were selected as subjects by convenience sampling method. Medical records provided sociodemographic data, duration of GDM, pregnancy status, and family history of diabetes. Participants completed validated questionnaires for quality of life, anxiety and depression. Latent profile analysis was used to identify profiles of quality of life in pregnant women with GDM, and then a mixed regression method was used to analyze the influencing factors of different profiles. Results: A total of 279 valid questionnaires were collected. The results of the latent profile analysis showed that the quality of life of pregnant women with GDM could be divided into two profiles: C1 "high worry-high support" group (75.6%) and C2 "low worry-low support" group (24.4%). Daily exercise duration and depression degree are negative influencing factors, making it easier to enter the C1 group (p < 0.05). Disease duration and family history of diabetes are positive influencing factors, making it easier to enter the C2 group (p < 0.05). Conclusion: The quality of life of pregnant women with GDM had obvious classification characteristics. Pregnant women with exercise habits and depression are more likely to enter the "high worry-high support" group, and health care providers should guide their exercise according to exercise guidelines during pregnancy and strengthen psychological intervention. Pregnant women with a family history of diabetes and a longer duration of the disease are more likely to fall into the "low worry-low support" group. Healthcare providers can strengthen health education for them and improve their disease self-management abilities. [ABSTRACT FROM AUTHOR]