Spatio-temporal variability, driving factors, and the health risk assessment of inhalable particulate matter in Germany

In dieser Studie wurden die räumlich-zeitliche Variabilität und die treibenden Faktoren der Luftverschmutzung auf der Grundlage kontinuierlicher Daten von Messstationen in Deutschland (Meso-Skala) und verschiedener Mikroumgebungen in städtischen und kleinstädtischen Gebieten (Mikro-Skala) untersucht, um die Auswirkungen von Luftschadstoffen auf die menschliche Gesundheit zu bewerten. Der Forschungsprozess umfasste zwei Arten von Analysen: Bewertungen von Daten, die an festen Standorten gesammelt wurden, und mobile Messungen.In this study, the spatio-temporal variability and driving factors of air pollution were investigated based on continuous data from monitoring stations in Germany (meso-scale), and different micro-environments across urban and small-town areas (micro-scale), to assess the impact of air pollutants on human health

Cigarette smoke-induced chronic obstructive pulmonary disease is attenuated by CCL20-blocker: a rat model

Aim To evaluate whether the effect of dendritic cells (DCs) on chronic obstructive pulmonary disease (COPD) can be relieved by blocking CCL20. Methods 30 Wistar rats were randomly divided into three groups: control, COPD, and COPD treated with CCL20 monoclonal antibody. In the latter two groups, COPD was induced by four-week cigarette smoke exposure and trachea injection of lipopolysaccharide solution on two occasions. CCL20 monoclonal antibody was injected intraperitoneally on the first day. All animals were sacrificed on the 29th day. Pathomorphology of the lung and bronchiole was analyzed using hematoxylin and eosin staining. The CCR6 content in the bronchoalveolar lavage fluid was detected using ELISA. DC distribution in the lung was examined by immunohistochemistry for OX62. Results COPD rat models showed pathological alterations similar to those in COPD patients. DCs, CCR6, and the severity of emphysema were significantly increased in the COPD group than in controls (all P values <0.001), and they were significantly reduced after anti-CCL20 treatment compared with the COPD group (all P values <0.05). Conclusion The interaction between CCR6 and its ligand CCL20 promotes the effect of DCs in the COPD pathogenesis, which can be reduced by blocking CCL20

The effects of anti-sense interleukin-5 gene transferred by recombinant adeno-associated virus in allergic rats

The accumulation and infiltration of eosinophils in airways is one of the most important characteristics of asthma, and is mediated partly by secretion of IL-5 from Th2 lymphocytes. It is well known that interleukin-5 (IL-5) played an important role in the regulation of eosinophils. In this study, an anti-sense IL-5 gene transferred by recombinant adeno-associated virus (rAAV-ASIL-5) was prepared to transfect allergic rats. It was found that the expression of IL-5 protein in plasma and BALF were inhibited significantly. The rAAV-ASIL-5-mediated suppression of total cell counts in peripheral blood and BALF were also observed. Moreover, rAAV-ASIL-5 remarkably reduced the eosinophil counts in peripheral blood and BALF, as well as the expression of ECP protein in plasma and BALF. The inflammation in lungs of rAAV-ASIL-5 pretreated rats also became slighter when compared with allergic rats. Otherwise, no apparent pathological damage to vital organs of rats was found. In conclusion, recombinant adeno-associated virus-mediated delivery of anti-sense IL-5 gene inhibited the accumulation of eosinophils and the airways inflammation in rat model of allergic asthma via suppressing IL-5 expression. It suggested the feasibility of rAAV-ASIL-5 in the gene therapy for allergic asthma and other eosinophilic diseases

Non-weight modules over the super-BMS3_3 algebra

In the present paper, a class of non-weight modules over the super-BMS$_3$ algebras $\S^{\epsilon}$ ($\epsilon=0$ or $\frac{1}{2}$) are constructed. These modules when regarded as $\S^{0}$-modules and further restricted as modules over the Cartan subalgebra $\mathfrak{h}$ are free of rank $1$, while when regarded as $\S^{\frac{1}{2}}$-modules and further restricted as modules over the Cartan subalgebra $\mathfrak{H}$ are free of rank $2$. We determine the necessary and sufficient conditions for these modules being simple, as well as determining the necessary and sufficient conditions for two $\S^{\epsilon}$-modules being isomorphic. At last, we present that these modules constitute a complete classification of free $U(\mathfrak{h})$-modules of rank $1$ over $\S^{0}$, and also constitute a complete classification of free $U(\mathfrak{H})$-modules of rank $2$ over $\S^{\frac{1}{2}}$.Comment: arXiv admin note: text overlap with arXiv:1906.07129 by other author

Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe

This study is supported by the RI-URBANS project (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & amp; Industrial Areas, European Union's Horizon 2020 research and innovation program, Green Deal, European Commission, contract 101036245). This study is also supported by National Natural Science Foundation of China (42101470, 72242106) and in part by the Chunhui Project Foundation of the Education Department of China under Grant HZKY20220053. This study benefited from the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS), especially the so-called ACTRIS-2 H2020 research project (grant no 654109), and the authors would like to thank ACTRIS (The Aerosol, Clouds and Trace Gases Research Infrastructure), especially the ACTRIS in situ EBAS Data Centre (EBAS), for providing datasets to the study. This study is also partly funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Environmental Exposures and Health, a partnership between UK Health Security Agency (UKHSA) and Imperial College London, and the UK Natural Environment Re-search Council, and the views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA or the Department of Health and Social Care. The research was also supported by the Hungarian Research, Development and Innovation Office (grant no. K132254). We thank also the support from "Agencia Estatal de Investigacion" from the Spanish Ministry of Science and Innovation, and FEDER funds under the projects CAIAC (PID2019-108990RB-I00); and the Generalitat de Catalunya (AGAUR 2017 SGR41) and the Direccio General de Territori. IMT Nord Europe and LOA acknowledge financial support from the Labex CaPPA project, funded by the French National Research Agency (ANR-11-LABX-0005-01), and the CLIMIBIO and ECRIN projects, both financed by the Regional Council "Hauts-de-France" and the European Regional Development Fund (ERDF).This study aims to picture the phenomenology of urban ambient total lung deposited surface area (LDSA) (including head/throat (HA), tracheobronchial (TB), and alveolar (ALV) regions) based on multiple path particle dosimetry (MPPD) model during 2017-2019 period collected from urban background (UB, n = 15), traffic (TR, n = 6), suburban background (SUB, n = 4), and regional background (RB, n = 1) monitoring sites in Europe (25) and USA (1). Briefly, the spatial-temporal distribution characteristics of the deposition of LDSA, including diel, weekly, and seasonal pat-terns, were analyzed. Then, the relationship between LDSA and other air quality metrics at each monitoring site was investigated. The result showed that the peak concentrations of LDSA at UB and TR sites are commonly observed in the morning (06:00-8:00 UTC) and late evening (19:00-22:00 UTC), coinciding with traffic rush hours, biomass burning, and atmospheric stagnation periods. The only LDSA night-time peaks are observed on weekends. Due to the variability of emission sources and meteorology, the seasonal variability of the LDSA concentration revealed sig-nificant differences (p = 0.01) between the four seasons at all monitoring sites. Meanwhile, the correlations of LDSA with other pollutant metrics suggested that Aitken and accumulation mode particles play a significant role in the total LDSA concentration. The results also indicated that the main proportion of total LDSA is attributed to the ALV fraction (50 %), followed by the TB (34 %) and HA (16 %). Overall, this study provides valuable information of LDSA as a predictor in epidemiological studies and for the first time presenting total LDSA in a variety of European urban environments.RI-URBANS project (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban amp; Industrial Areas, European Union's Horizon 2020 research and innovation program, Green Deal, European Commission, 101036245)National Natural Science Foundation of China (NSFC)Chunhui Project Foundation of the Education Department of ChinaAerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS)National Institute for Health Research (NIHR) Health Protection Research Unit in Environmental Exposures and HealthUK Research & Innovation (UKRI) Natural Environment Research Council (NERC)National Research, Development & Innovation Office (NRDIO) - Hungary"Agencia Estatal de Investigacion" from the Spanish Ministry of Science and InnovationGeneralitat de Catalunya 42101470, 72242106Direccio General de Territori HZKY20220053Agence Nationale de la Recherche (ANR) 654109Regional Council "Hauts-de-France"European Union (EU) K132254, PID2019-108990RB-I00, AGAUR 2017 SGR41, ANR-11-LABX-0005-01ERD

Box-supervised Instance Segmentation with Level Set Evolution

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently attracted a lot of research attentions. In this paper, we propose a novel single-shot box-supervised instance segmentation approach, which integrates the classical level set model with deep neural network delicately. Specifically, our proposed method iteratively learns a series of level sets through a continuous Chan-Vese energy-based function in an end-to-end fashion. A simple mask supervised SOLOv2 model is adapted to predict the instance-aware mask map as the level set for each instance. Both the input image and its deep features are employed as the input data to evolve the level set curves, where a box projection function is employed to obtain the initial boundary. By minimizing the fully differentiable energy function, the level set for each instance is iteratively optimized within its corresponding bounding box annotation. The experimental results on four challenging benchmarks demonstrate the leading performance of our proposed approach to robust instance segmentation in various scenarios. The code is available at: https://github.com/LiWentomng/boxlevelset.Comment: 17 page, 4figures, ECCV202

Theory and experiments on driving stability of tank trucks under dangerous working conditions

To study the factors affecting the driving stability of tank trucks under dangerous working conditions, a full vehicle dynamics model and an equivalent test bench for liquid sloshing were designed. On the test bench, two dangerous working conditions were simulated to study liquid sloshing, i.e. braking and turning. The results show that the liquid sloshing force have a major impact on driving stability and the forces depended on the tank geometry, the fill level and the natural sloshing frequency of the liquid. The results of this study still provide a theoretical and experimental basis for studying further the factors that affect the driving stability of tank trucks

SSAH: Semi-supervised Adversarial Deep Hashing with Self-paced Hard Sample Generation

Deep hashing methods have been proved to be effective and efficient for large-scale Web media search. The success of these data-driven methods largely depends on collecting sufficient labeled data, which is usually a crucial limitation in practical cases. The current solutions to this issue utilize Generative Adversarial Network (GAN) to augment data in semi-supervised learning. However, existing GAN-based methods treat image generations and hashing learning as two isolated processes, leading to generation ineffectiveness. Besides, most works fail to exploit the semantic information in unlabeled data. In this paper, we propose a novel Semi-supervised Self-pace Adversarial Hashing method, named SSAH to solve the above problems in a unified framework. The SSAH method consists of an adversarial network (A-Net) and a hashing network (H-Net). To improve the quality of generative images, first, the A-Net learns hard samples with multi-scale occlusions and multi-angle rotated deformations which compete against the learning of accurate hashing codes. Second, we design a novel self-paced hard generation policy to gradually increase the hashing difficulty of generated samples. To make use of the semantic information in unlabeled ones, we propose a semi-supervised consistent loss. The experimental results show that our method can significantly improve state-of-the-art models on both the widely-used hashing datasets and fine-grained datasets