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Increasing tobacco control and public health awareness have increased smokers’ attempts to quit smoking. However, some smokers also seek alternative products claimed to pose less risks. The use of smokeless tobacco (ST) products may thus increase in some countries which are not traditionally ST markets. To provide a cross-sectional picture on ST usage in China, a survey was conducted from December 2019 to March 2020 in representative metropolitan cities (divided into three tiers by their populations and gross domestic product (GDP) sizes), from which 3,000 tobacco users and 801 ST users were randomly recruited to provide a snapshot of ST usage behaviour and other pertinent factors for Chinese tobacco users. The study included questionnaires designed to probe potential reasons behind ST use, usage habits, and nicotine dependence attributes. These questions were devised to cover the type of tobacco products used, users’ age, gender, city of residence, residence time, household monthly income, etc., and was supported by some verification questions. Mann-Whitney-analysis was used for significance analysis between different groups. The results showed that ST prevalence for Chinese tobacco users was around 2.1%. The proportion of exclusively ST use was about 8.99%, and the mean conversion time to habitual ST use was about three months. Demographic information such as the city tiers where ST users lived, their age, gender, educational and income levels appeared to correlate with ST use habits although more studies are needed to verify the observations. The fact that a small but measurable population of Chinese ST users exists has important implications for tobacco control. This study provides the first large-scale, single-time-point survey on Chinese ST user profiles, which may help the future research on tobacco control policy regarding ST products in China.

Patients with COVID-19 have been reported to experience neurological complications, although the main cause of death in these patients was determined to be lung damage. Notably, SARS-CoV-2-induced pathological injuries in brains with a viral presence were also found in all fatal animal cases. Thus, an appropriate animal model that mimics severe infections in the lungs and brain needs to be developed. In this paper, we compared SARS-CoV-2 infection dynamics and pathological injuries between C57BL/6Smoc-Ace2em3(hACE2-flag-Wpre-pA)Smoc transgenic hACE2-C57 mice and Syrian hamsters. Importantly, the greatest viral distribution in mice occurred in the cerebral cortex neuron area, where pathological injuries and cell death were observed. In contrast, in hamsters, viral replication and distribution occurred mainly in the lungs but not in the cerebrum, although obvious ACE2 expression was validated in the cerebrum. Consistent with the spread of the virus, significant increases in IL-1β and IFN-γ were observed in the lungs of both animals. However, in hACE2-C57 mice, the cerebrum showed noticeable increases in IL-1β but only mild increases in IFN-γ. Notably, our findings revealed that both the cerebrum and the lungs were prominent infection sites in hACE2 mice infected with SARS-CoV-2 with obvious pathological damage. Furthermore, hamsters exhibited severe interstitial pneumonia from 3 dpi to 5 dpi, followed by gradual recovery. Conversely, all the hACE2-C57 mice experienced severe pathological injuries in the cerebrum and lungs, leading to mortality before 5 dpi. According to these results, transgenic hACE2-C57 mice may be valuable for studying SARS-CoV-2 pathogenesis and clearance in the cerebrum. Additionally, a hamster model could serve as a crucial resource for exploring the mechanisms of recovery from infection at different dosage levels. [ABSTRACT FROM AUTHOR]

The current oil recovery of the Lucaogou shale oil reservoir is predicted to be about 7.2%. It is crucial to explore improved oil recovery (IOR) technologies, and further experimental and field research needs to be conducted to study the complex mechanism. In this study, laboratory experiments were carried out to investigate the performance of one-step and multi-step depletion, CO2 huff-n-puff, and surfactant imbibition based on nuclear magnetic resonance (NMR). The sweep efficiencies were assessed via NMR imaging. In addition, hybrid methods of combining surfactant with CO2 huff-n-puff and the performance of injection sequence on oil recovery were investigated. The experimental results indicate that oil recoveries of depletion development at different initial pressures range from 4% to 11%. CO2 huff-n-puff has the highest oil recovery (30.45% and 40.70%), followed by surfactant imbibition (24.24% and 20.89%). Pore size distribution is an important factor. After three more cycles of surfactant imbibition and CO2 huff-n-puff, the oil recovery can be increased by 11.27% and 26.27%, respectively. Surfactant imbibition after CO2 huff-n-puff shows a viable method. Our study can provide guidance and theoretical support for shale oil development in the Lucaogou shale oil reservoir. [ABSTRACT FROM AUTHOR]

Currently prevalent multi-modal 3D detection methods rely on dense detectors that usually use dense Bird's-Eye-View (BEV) feature maps. However, the cost of such BEV feature maps is quadratic to the detection range, making it not scalable for long-range detection. Recently, LiDAR-only fully sparse architecture has been gaining attention for its high efficiency in long-range perception. In this paper, we study how to develop a multi-modal fully sparse detector. Specifically, our proposed detector integrates the well-studied 2D instance segmentation into the LiDAR side, which is parallel to the 3D instance segmentation part in the LiDAR-only baseline. The proposed instance-based fusion framework maintains full sparsity while overcoming the constraints associated with the LiDAR-only fully sparse detector. Our framework showcases state-of-the-art performance on the widely used nuScenes dataset, Waymo Open Dataset, and the long-range Argoverse 2 dataset. Notably, the inference speed of our proposed method under the long-range perception setting is 2.7× faster than that of other state-of-the-art multimodal 3D detection methods.