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The rational use of land is an important guarantee for the sustainable development of resource-based cities. First, this paper uses the panel data of 115 resource-based cities in China from 2004 to 2018 to measure green land use efficiency (GLUE) by SBM model and Metafrontier-Malmquist productivity index model. Secondly, it analyzes the industrial upgrading from the perspectives of the optimization of industrial structure and the rationalization of industrial structure to explore the impact mechanism of industrial upgrading on GLUE. Furtherly, the heterogeneity analysis is carried out from the following two perspectives: exploring the impact of industrial upgrading on GLUE under different types of urban samples; using the quantile model to analyze the impact of industrial upgrading under different GLUE quantiles. The main conclusions of this study are as follows: (1) both the optimization of industrial structure and the rationalization of industrial structure promote the improvement of GLUE. The robustness test and instrumental variable method support this conclusion. (2) For different types of resource-based cities, the rationalization and optimization of industrial structure have positive coefficients in regeneration cities, growing cities, and declining cities, but have a negative effect on mature cities. (3) In all quantiles, the effects of industrial structure optimization and industrial structure rationalization are positive. However, with the increase of quantiles, the role of industrial structure optimization gradually decreases. The role of industrial structure rationalization gradually increases. Meanwhile, there is an obvious threshold effect when taking resource endowment as the threshold variable. This paper provides a theoretical reference for the transformation and development of resource-based cities.

Industrial transformation (IT) is the inevitable course for the sustainable development of resource-based cities, while digital inclusive finance (DIF) provides essential capital elements for industrial transformation. Based on the panel data of 115 resource-based cities in China from 2011 to 2019, this paper discusses the influence mechanism of digital inclusive finance on industrial transformation from the perspectives of the optimization of industrial structure (OIS) and the rationalization of industrial structure (RIS). The empirical results show that digital inclusive finance has a positive effect on both the optimization of industrial structure and the rationalization of industrial structure. Digital inclusive finance influences industrial transformation through residents’ income and technological innovation. In addition, in the analysis of income gap and innovation gap, low-income regions have a better effect on the industrial transformation of industrial structure optimization, while high-income regions have a better effect in manufacturing upgrading, thus resulting in a more significant effect of industrial transformation on the rationalization of industrial structure. Obviously, the development of high-innovation regions has relative advantages with more channels for industrial transformation, which have significant effect of industrial transformation. Therefore, it is necessary to provide differentiated reform on the basis of unified development reform.

Backgroud Diabetes mellitus is a common chronic disease and a severe public health issue. The incidence trends for type 1 diabetes (TIDM) and type 2 diabetes (T2DM) have rarely been studied on a global scale. We aimed to determine the temporal and geographical trends of diabetes globally. Methods Data on diabetes mellitus, including incidence, prevalence from 1990 to 2017 were obtained from the 2017 Global Burden of Disease study. We calculated the estimated annual percentage changes (EAPCs) in age-standardized incidence rate (ASIR) of diabetes mellitus according to sex, region, and disease type. Results The worldwide incident cases of diabetes mellitus has increased by 102.9% from 11,303,084 cases in 1990 to 22,935,630 cases in 2017 worldwide, while the ASIR increased from 234 /100,000 persons (95% UI, 219–249) to 285/100,000 persons (95% UI, 262–310) in this period [EAPC = 0.87, 95% confidence interval (CI):0.79–0.96]. The global ASIRs of T1DM and T2DM both demonstrated significant increase during 1990–2017, with EAPCs of 0.34 (95% CI,0.30–0.39) and 0.89 (95% CI,0.80–0.97), respectively. The ASIR trends also varied considerably by regions and countries. The increase in ASIR was greatest in high sociodemographic index regions (EAPC = 1.05, 95% CI:0.92–1.17) and lowest in low-SDI regions (EAPC = 0.79, 95% CI:0.71–0.88). Conclusions Both the number of incident cases and ASIR of diabetes mellitus increased significantly during 1990–2017 worldwide, but the temporal trends varied markedly across regions and countries.

Aims/hypothesisThe study aims to quantify the global trend of the disease burden of type 2 diabetes caused by various risks factors by country income tiers.MethodsData on type 2 diabetes, including mortality and disability-adjusted life years (DALYs) during 1990–2019, were obtained from the Global Burden of Disease Study 2019. We analysed mortality and DALY rates and the population attributable fraction (PAF) in various risk factors of type 2 diabetes by country income tiers.ResultsGlobally, the age-standardised death rate (ASDR) attributable to type 2 diabetes increased from 16.7 (15.7, 17.5)/100,000 person-years in 1990 to 18.5 (17.2, 19.7)/100,000 person-years in 2019. Similarly, age-standardised DALY rates increased from 628.3 (537.2, 730.9)/100,000 person-years to 801.5 (670.6, 954.4)/100,000 person-years during 1990–2019. Lower-middle-income countries reported the largest increase in the average annual growth of ASDR (1.3%) and an age-standardised DALY rate (1.6%) of type 2 diabetes. The key PAF attributing to type 2 diabetes deaths/DALYs was high BMI in countries of all income tiers. With the exception of BMI, while in low- and lower-middle-income countries, risk factors attributable to type 2 diabetes-related deaths and DALYs are mostly environment-related, the risk factors in high-income countries are mostly lifestyle-related.Conclusions/interpretationType 2 diabetes disease burden increased globally, but low- and middle-income countries showed the highest growth rate. A high BMI level remained the key contributing factor in all income tiers, but environmental and lifestyle-related factors contributed differently across income tiers.Data availabilityTo download the data used in these analyses, please visit the Global Health Data Exchange at http://ghdx.healthdata.org/gbd-2019.

Industrial transformation (IT) is the inevitable course for the sustainable development of resource-based cities, while digital inclusive finance (DIF) provides essential capital elements for industrial transformation. Based on the panel data of 115 resource-based cities in China from 2011 to 2019, this paper discusses the influence mechanism of digital inclusive finance on industrial transformation from the perspectives of the optimization of industrial structure (OIS) and the rationalization of industrial structure (RIS). The empirical results show that digital inclusive finance has a positive effect on both the optimization of industrial structure and the rationalization of industrial structure. Digital inclusive finance influences industrial transformation through residents’ income and technological innovation. In addition, in the analysis of income gap and innovation gap, low-income regions have a better effect on the industrial transformation of industrial structure optimization, while high-income regions have a better effect in manufacturing upgrading, thus resulting in a more significant effect of industrial transformation on the rationalization of industrial structure. Obviously, the development of high-innovation regions has relative advantages with more channels for industrial transformation, which have significant effect of industrial transformation. Therefore, it is necessary to provide differentiated reform on the basis of unified development reform.

Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, we applied three tree-based machine learning (ML) techniques, i.e., random forest (RF), gradient boost decision tree (GBDT), and adaptive boosting (AdaBoost), to analyze the factors contributing to the severity of large truck crashes. Besides, a mixed logit model was developed as a baseline model to compare with the factors identified by the ML models. The analysis was performed based on the crash data collected from the Texas Crash Records Information System (CRIS) from 2011 to 2015. The results of this research demonstrated that the GBDT model outperforms other ML methods in terms of its prediction accuracy and its capability in identifying more contributing factors that were also identified by the mixed logit model as significant factors. Besides, the GBDT method can effectively identify both categorical and numerical factors, and the directions and magnitudes of the impacts of the factors identified by the GBDT model are all reasonable and explainable. Among the identified factors, driving under the influence of drugs, alcohol, and fatigue are the most important factors contributing to the severity of large truck crashes. In addition, the exists of curbs and medians and lanes and shoulders with sufficient width can prevent severe large truck crashes.

Background The genetic basis underlying spawning abilities in the Pacific white shrimp, Penaeus vannamei , remains largely unexplored. To investigate genetic variations potentially related to reproductive performance, a systematic bioinformatic analysis was conducted to identify structural variations (SVs) with different polymorphic spectra in P. vannamei with high fertility (HF) and low fertility (LF). Results A total of 2,323 and 1,859 SV events were identified exclusively in the HF and LF groups, respectively. These SVs were mapped to 277 genes in the HF group and 231 genes in the LF group. Gene Ontology (GO) enrichment analysis based on SNPs (single nucleotide polymorphism) and SVs revealed several neural-related processes, suggesting the importance of neural regulation in reproduction. Notably, we identified a set of promising genes, including Cttn, Spast, Ppp4c, Spire1, Lhcgr, and Ftz-f1 , which may enhance fertility in shrimp. Conclusion In conclusion, this study is the first to establish a link between SVs and reproductive traits in P. vannamei . The promising genes discovered have the potential to serve as crucial markers for enhancing reproductive traits through targeted genotyping.

The sialic acid binding Ig like lectin 15 (Siglec-15) was previously identified as tumor immune suppressor gene in some human cancers with elusive molecular mechanism to be elucidated. The continuous focus on both clinical and basic biology of bladder cancer leads us to characterize aberrant abundance of BACH1-IT2 associating with stabilization of Siglec-15, which eventually contributes to local immune suppressive microenvironment and therefore tumor advance. This effect was evidently mediated by miR-4786-5p. BACH1-IT2 functions in this scenario as microRNA sponge, and competitively conceals miR-4786 and up-regulates cancer cell surface Siglec-15. The BACH1-IT2-miR-4786-Siglec-15 axis significantly influences activation of immune cell co-culture. In summary, our data highlights the critical involvements of BACH1-IT2 and miR-4786 in immune evasion in bladder cancer, which hints the potential for both therapeutic and prognostic exploitation.

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