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Objective When mining travel characteristics from urban rail transit travel trajectory data, passenger travel activity patterns are found closely related to the stations near their residence and workplace. This is reflected in their travel trajectories by fixed boarding/alighting routes and concentrated entry/exit stations. Meanwhile, passenger travel trajectories associated with residence and workplace exhibit certain fluctuations, and the travel data strongly related to workplace-residence often differ significantly from other travel data. To accurately extract passenger travel trajectory characteristics, it is essential to clarify the residence-workplace relationship within urban rail transit passenger travel data. Method A mechanism for mining travel characteristics and classifying passengers based on residence-workplace relationship is proposed. The preliminary workplace-residence judgments are formed by identifying the hotspots of passenger trajectory activities related to residence, workplace and social activity locations. Then, the spatio-temporal patterns strongly associated with workplace-residence are explored. A K-means clustering algorithm extracted on the basis of passenger travel trajectory characteristic values is used to cluster passengers according to their residence-workplace judgment, resulting in a passenger classification method that precisely matches the passenger travel characteristics. Result & Conclusion Simulation validation results indicate that passengers with clearly defined residence-workplace relationship exhibit four distinct travel patterns of two-point, two-point-two-line, three-point, and multi-point. These travel pattern classification results make up a crucial technical foundation for constructing detailed passenger profiles and developing intelligent security check strategies.

It is well known that DNA damage can cause apoptosis. However, whether apoptosis and its metabolites contribute to DNA repair is largely unknown. In this study, we found that apoptosis‐deficient Fasmut and Bim−/− mice show significantly elevated DNA damage and premature cellular senescence, along with a significantly reduced number of 16,000 g apoptotic vesicles (apoVs). Intravenous infusion of mesenchymal stromal cell (MSC)‐derived 16,000 g apoVs rescued the DNA damage and premature senescence in Fasmut and Bim−/− mice. Moreover, a sublethal dose of radiation exposure caused more severe DNA damage, reduced survival rate, and loss of body weight in Fasmut mice than in wild‐type mice, which can be recovered by the infusion of MSC‐apoVs. Mechanistically, we showed that apoptosis can assemble multiple nuclear DNA repair enzymes, such as the full‐length PARP1, into 16,000 g apoVs. These DNA repair components are directly transferred by 16,000 g apoVs to recipient cells, leading to the rescue of DNA damage and elimination of senescent cells. Finally, we showed that embryonic stem cell‐derived 16,000 g apoVs have superior DNA repair capacity due to containing a high level of nuclear DNA repair enzymes to rescue lethal dose‐irradiated mice. This study uncovers a previously unknown role of 16,000 g apoVs in safeguarding tissues from DNA damage and demonstrates a strategy for using stem cell‐derived apoVs to ameliorate irradiation‐induced DNA damage. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compound (BVOC) emissions have long been known to play vital roles in modulating the formation of ozone and secondary organic aerosols (SOAs). While early studies have evaluated their impact globally or regionally, the BVOC emissions emitted from urban green spaces (denoted as U-BVOC emissions) have been largely ignored primarily due to the failure of low-resolution land cover in resolving such processes, but also because their important contribution to urban BVOCs was previously unrecognized. In this study, by utilizing a recently released high-resolution land cover dataset, we develop the first set of emission inventories of U-BVOCs in China at spatial resolutions as high as 1 km. This new dataset resolved densely distributed U-BVOCs in urban core areas. The U-BVOC emissions in megacities could account for a large fraction of total BVOC emissions, and the good agreement of the interannual variations between the U-BVOC emissions and ozone concentrations over certain regions stresses their potentially crucial role in influencing ozone variations. The newly constructed U-BVOC emission inventory is expected to provide an improved dataset to enable the research community to re-examine the modulation of BVOCs on the formation of ozone, SOA, and atmospheric chemistry in urban environments.

Background: Dysmenorrhea is one of the most common symptoms in gynecology. It refers to people who experience lower abdominal pain, swelling, backache, or another discomfort before and after menstruation or during menstruation, which seriously affects the quality of life and work. Clinically, there are many methods to treat primary dysmenorrhea, among which acupuncture and traditional Chinese medicine (TCM) are beneficial. This work aimed to test the efficacy of acupuncture and TCM in the treatment of primary dysmenorrhea and hope to supply more reliable evidence for clinical treatment., Methods: We searched articles from the Wanfang database and China National Knowledge Infrastructure and collected the clinical, randomized, or quasi-randomized controlled trials of acupuncture compared with TCM for primary dysmenorrhea. We tested the quality and data of the included studies according to the Cochrane criteria and compiled detailed systematic reviews and meta-analysis., Results: Seven articles (including 492 patients) on acupuncture and TCM for primary dysmenorrhea were included in the systematic review. Seven studies were included in the final analysis, and there was no heterogeneity among the studies (P=0.98, I2=0%). The results showed that the clinical effectiveness of acupuncture was better than TCM (OR: 4.86, 95% CI: 2.84-8.33, Z =5.75, P<0.00001)., Conclusions: The efficacy of acupuncture is superior to TCM in the treatment of primary dysmenorrhea.

In this study, the seasonal mean PM 2.5 concentration in Qingdao, a coastal city, during 2014-2018 was first analyzed and the winter, in particular of 2015, showed the highest concentration. To elucidate the sources and control factors of PM 2.5 , three dimensional model Weather Research and Forecasting (WRF), Community Multiscale Air Quality model (CMAQ), as well as Flexible Particle model (FLEXPART), were used. During December 2015 and January 2016, modeling results showed that the mean contribution to PM 2.5 mass concentrations from local emissions in Qingdao was 25%, and the transport from north and west accounted for almost half. Over the two episodically polluted periods (29-31 December 2015; 15-17 January 2016), the local emissions in Qingdao surprisingly contributed to only 18% and 24% to PM 2.5 mass concentrations, respectively, indicating the dominant contributions from other regions, such as areas outside Qingdao in Shandong and Beijing-Tianjin-Hebei (BTH). The results show the sources region and contribution may vary remarkably along with the change in the pathways of the air parcel, inferred by the FLEXPART, while the near-surface PM 2.5 enhancement is largely caused by downward vertical advection and enhanced aerosol chemistry reactions, accompanied by simultaneous drop in the boundary layer height. This study also reveals that the transport contribution is sensitive to the air parcel trajectories. We, therefore, recommend the efficient emission control based on transport trajectories in short-term air quality improvement in Qingdao., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2020 Elsevier Ltd. All rights reserved.)