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β-Thalassemia (β-thal) is one of the most common monogenic recessive inherited diseases worldwide. The mutation spectrum of β-thal has been increasingly broadened by various genetic testing methods. The discovery and identification of novel and rare pathogenic thalassemia variants enable better disease prevention, especially in high prevalence regions. In this study, a Chinese thalassemia family with an unclear etiology was recruited to the Thalassemia Screening Program. Blood samples collected from them were primarily screened by hematology analysis and clinical routine genetic screening. Subsequently, targeted next-generation sequencing (NGS) and Sanger sequencing were performed to find and identify a novel deletion variant. The deletion, discovered by targeted NGS, was validated through real-time quantitative polymerase chain reaction (qPCR). First, a large novel β-thal deletion (3488 bp) related to a high Hb F level, NC_000011.9: g.5245533_5249020del (Chongqing deletion) (GRCh37/hg19), was found and identified in the proband and her mother. The deletion removed the entire β-globin gene and led to absent β-globin (β0). We then validated this large novel deletion in the proband and her mother by qPCR. We first discovered and identified a large novel β-thal deletion related to elevated Hb F level, it helps broaden the spectrum of pathogenic mutants that may cause β-thal intermedia (β-TI) or β-thal major (β-TM), paving the way for effective thalassemia screening. Next-generation sequencing has the potential of finding rare and novel thalassemia mutants. [ABSTRACT FROM AUTHOR]

In order to realize low-carbon and high-quality development, this study took the carbon emissions of each district and county in the Chengdu–Chongqing region from 2005 to 2017 as the research object and used the spatial autocorrelation model to analyze the spatial and temporal evolution characteristics of carbon emissions in the counties of the Chengdu–Chongqing region, so as to fill in the research blank of carbon emissions in the counties of the Chengdu–Chongqing region. Then, the geographical detector model is used to explore the interaction among influencing factors of carbon emissions and reveal the time changes and regional differences of influencing factors, so as to improve the lack of spatial and temporal heterogeneity of influencing factors of carbon emissions by geographical detector. The results show the following: (i) The overall carbon emissions of counties show a year-on-year growth trend with the main urban areas of Chengdu and Chongqing as the core, but the growth rate slows down after 2010. (ii) The carbon emissions showed a significant positive spatial autocorrelation, and the neighboring counties showed a spatial clustering characteristic of "high-high" or "low-low", and the clustering status tended to be enhanced. (iii) The carbon emissions are strongly influenced by industrial structure, economic development, investment level, financial situation, urbanization rate and social consumption, and their interactions are all enhanced, but the influence mostly tends to rise first and then fall. (iv) County carbon emissions can be divided into four types of geographical types, such as population size influencing type, urbanization rate influencing type, economic development influencing type and industrial structure influencing type. Therefore, a variety of factors need to be considered comprehensively, a multi-pronged approach, and a comprehensive policy to realize low-carbon transformation in the Chengdu–Chongqing region. [ABSTRACT FROM AUTHOR]