Acute effect of particulate matter pollution on hospital admissions for cause-specific respiratory diseases among patients with and without type 2 diabetes in Beijing, China, from 2014 to 2020

BACKGROUND: Scientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated. METHODS: A two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM and PM were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. RESULTS: A total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM corresponded to 1.91% (95% CI: 1.33-2.49%), 2.16% (95% CI: 1.08-3.25%), and 1.92% (95% CI: 1.46-2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0-8 days, lag 8 days, and lag 8 days. The effect size of PM was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P \u3c 0.01). The effect sizes of PM were 3.86% (95% CI: 2.48-5.27%), 3.73% (95% CI: 1.72-5.79%), and 3.92% (95% CI: 2.65-5.21%), respectively, at lag 0-13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions. CONCLUSIONS: Short-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM was higher in patients with T2D than those without T2D

Transport emissions in Beijing: A scenario planning approach

This paper explores and analyses how to reduce smog-related air pollutants and carbon dioxide emissions generated by passenger transport systems in Beijing. In-depth surveys with experts and practitioners in China are used to examine the current business-as-usual projection for emissions in Beijing, the drivers and trends affecting current projections, and to develop alternative scenarios that might help reduce projected emissions significantly. These are based around different variants of population and migration growth and environmental stewardship. Current levels of smog caused by transport emissions are much higher in Beijing than internationally accepted safety standards, partly because of high levels of motorised traffic. Carbon dioxide emissions always tend to be overlooked because economic growth is prioritised. The sustainable model represents one of the best models for Beijing to follow; however, Beijing faces major challenges in becoming more environmentally sustainable over the next few years, mainly due to population growth and increased migration, even if there is powerful top-down government environmental stewardship. The aspiration to reduce smog-related air pollutants and carbon dioxide emissions in Beijing by implementing sustainable transport mitigation measures seems very ambitious; however, it is perhaps in this context that the real innovations in transport planning will emerge

Towards sustainability: An assessment of an urbanisation bubble in China using a hierarchical - stochastic multicriteria acceptability analysis - Choquet integral method

Urbanisation bubbles have become an increasingly serious problem. Attention has been paid to the speed of urbanisation; however, the issue of quality has been neglected, particularly in the case of China. Therefore, the aim of this research is to evaluate China’s urbanisation bubbles by employing a hierarchical - stochastic multicriteria acceptability analysis (SMAA) - Choquet integral method. In order to highlight regional disparities, we measure the urbanisation bubbles at a provincial level. Our study aggregates the urbanisation bubble indices using the Choquet integral preference model, and considers the interactions between various indicators. Furthermore, robust ordinal regression and SMAA are applied to resolve the robustness issues associated with the entire set of weights assigned to the urbanisation bubble composite indicator. In addition, by employing a multiple criteria hierarchy process, the study aggregates urbanisation bubble indices not only at the comprehensive level, but also at the intermediate levels of the hierarchy. Our findings suggest that the ranking of urbanisation bubbles is positively related to the level of regional development. This study contributes to the evaluation of regional urbanisation and sustainable development

The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data

With the rapid development of the remote sensing monitoring and computer vision technology, the deep learning method has made a great progress to achieve applications such as earth observation, climate change and even space exploration. However, the model trained on existing data cannot be directly used to handle the new remote sensing data, and labeling the new data is also time-consuming and labor-intensive. Unsupervised Domain Adaptation (UDA) is one of the solutions to the aforementioned problems of labeled data defined as the source domain and unlabeled data as the target domain, i.e., its essential purpose is to obtain a well-trained model and tackle the problem of data distribution discrepancy defined as the domain shift between the source and target domain. There are a lot of reviews that have elaborated on UDA methods based on natural data, but few of these studies take into consideration thorough remote sensing applications and contributions. Thus, in this paper, in order to explore the further progress and development of UDA methods in remote sensing, based on the analysis of the causes of domain shift, a comprehensive review is provided with a fine-grained taxonomy of UDA methods applied for remote sensing data, which includes Generative training, Adversarial training, Self-training and Hybrid training methods, to better assist scholars in understanding remote sensing data and further advance the development of methods. Moreover, remote sensing applications are introduced by a thorough dataset analysis. Meanwhile, we sort out definitions and methodology introductions of partial, open-set and multi-domain UDA, which are more pertinent to real-world remote sensing applications. We can draw the conclusion that UDA methods in the field of remote sensing data are carried out later than those applied in natural images, and due to the domain gap caused by appearance differences, most of methods focus on how to use generative training (GT) methods to improve the model’s performance. Finally, we describe the potential deficiencies and further in-depth insights of UDA in the field of remote sensing

Evolution of the Microstructure and Mechanical Performance of As-Sprayed and Annealed Silicon Coating on Melt-Infiltrated Silicon Carbide Composites

In this study, silicon coating was deposited on melt-infiltrated SiC composites using atmospheric plasma spraying and then annealed at 1100 and 1250 °C for 1–10 h to investigate the effect of annealing on the layer. The microstructure and mechanical properties were evaluated using scanning electron microscopy, X-ray diffractometry, transmission electron microscopy, nano-indentation, and bond strength tests. A silicon layer with a homogeneous polycrystalline cubic structure was obtained without phase transition after annealing. After annealing, three features were observed at the interface, namely β-SiC/nano-oxide film/Si, Si-rich SiC/Si, and residual Si/nano-oxide film/Si. The nano-oxide film thickness was ≤100 nm and was well combined with SiC and silicon. Additionally, a good bond was formed between the silicon-rich SiC and silicon layer, resulting in a significant bond strength improvement from 11 to >30 MPa

The Synergistic Mechanism of Total Saponins and Flavonoids in Notoginseng–Safflower against Myocardial Infarction Using a Comprehensive Metabolomics Strategy

Notoginseng and safflower are commonly used traditional Chinese medicines for benefiting qi and activating blood circulation. A previous study by our group showed that the compatibility of the effective components of total saponins of notoginseng (NS) and total flavonoids of safflower (SF), named NS–SF, had a preventive effect on isoproterenol (ISO)-induced myocardial infarction (MI) in rats. However, the therapeutic effect on MI and the synergistic mechanism of NS–SF are still unclear. Therefore, integrated metabolomics, combined with immunohistochemistry and other pharmacological methods, was used to systematically research the therapeutic effect of NS–SF on MI rats and the synergistic mechanism of NS and SF. Compared to NS and SF, the results demonstrated that NS–SF exhibited a significantly better role in ameliorating myocardial damage, apoptosis, easing oxidative stress and anti-inflammation. NS–SF showed a more significant regulatory effect on metabolites involved in sphingolipid metabolism, glycine, serine, and threonine metabolism, primary bile acid biosynthesis, aminoacyl-tRNA biosynthesis, and tricarboxylic acid cycle, such as sphingosine, lysophosphatidylcholine (18:0), lysophosphatidylethanolamine (22:5/0:0), chenodeoxycholic acid, L-valine, glycine, and succinate, than NS or SF alone, indicating that NS and SF produced a synergistic effect on the treatment of MI. This study will provide a theoretical basis for the clinical development of NS–SF

TP53RK Drives the Progression of Chronic Kidney Disease by Phosphorylating Birc5

Abstract Renal fibrosis is a common characteristic of various chronic kidney diseases (CKDs) driving the loss of renal function. During this pathological process, persistent injury to renal tubular epithelial cells and activation of fibroblasts chiefly determine the extent of renal fibrosis. In this study, the role of tumor protein 53 regulating kinase (TP53RK) in the pathogenesis of renal fibrosis and its underlying mechanisms is investigated. TP53RK is upregulated in fibrotic human and animal kidneys with a positive correlation to kidney dysfunction and fibrotic markers. Interestingly, specific deletion of TP53RK either in renal tubule or in fibroblasts in mice can mitigate renal fibrosis in CKD models. Mechanistic investigations reveal that TP53RK phosphorylates baculoviral IAP repeat containing 5 (Birc5) and facilitates its nuclear translocation; enhanced Birc5 displays a profibrotic effect possibly via activating PI3K/Akt and MAPK pathways. Moreover, pharmacologically inhibiting TP53RK and Birc5 using fusidic acid (an FDA‐approved antibiotic) and YM‐155(currently in clinical phase 2 trials) respectively both ameliorate kidney fibrosis. These findings demonstrate that activated TP53RK/Birc5 signaling in renal tubular cells and fibroblasts alters cellular phenotypes and drives CKD progression. A genetic or pharmacological blockade of this axis serves as a potential strategy for treating CKDs