Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S

What Drives Patients Affected by Depression to Share in Online Depression Communities? A Social Capital Perspective

Online depression communities give people additional opportunities to share their experiences and exchange social support to care for themselves in fighting against depression. We aimed to explore what drives patients to share in online depression communities. We used three dimensions of social capital (structural, relational, and cognitive) to explain their sharing behaviors. We further proposed that five factors (social interaction ties, a sense of shared identity, trust, expertise, and a sense of shared values) will have significant, positive effects on sharing behaviors and that there are differences among patients who have spent different lengths of time participating in online depression communities. We then chose a popular online depression community in China as our data source and obtained a dataset consisting of 31,440 posts from 197 members. Then, we employed panel data regression analyses to test all six hypotheses. The results revealed that all five factors had significant, positive effects (p < 0.01) on patients’ sharing behaviors, and the effects were significantly different across groups. Our empirical results help designers and managers of online depression communities take specific measures to facilitate community members’ access to social capital resources. Meanwhile, our results have implications for existing health management and e-health literature

Mechanism and Prevention of Rockburst in Deep Multipillar Gob-Side Entry

AbstractRockburst frequently occurs in deep multipillar gob-side entry in Inner Mongolia and Shaanxi, China. A case study of the Hongqinghe coal mine was analyzed to reveal the occurrence mechanism of rockburst in deep multipillar gob-side entry through theoretical and applied research. And new control methods were put forward. The results show that there is an essential difference between the surrounding rock load characteristics of multipillar gob-side entry and single-pillar gob-side entry. Within the influence area of the lateral goaf, more coal pillars will promote stress concentration, and the high stress concentration area will migrate to the coal pillar of mining roadway. The hanging roof of the working face can cause the stress concentration in the area of leading coal pillar to reach 5 times. The multipillar gob-side entry is less affected by the dynamic load of the roof of the lateral goaf. The ‘F-shaped and L-shaped’ stress source structure model of the deep gob-side multipillar entry was established, and the occurrence mechanism of rockburst is revealed. On the one hand, the static load of rockburst start foundation is formed under the superposition of lateral goaf, rear goaf, and multicoal pillar. On the other hand, the superposition of the foundation static load and the opportune static load induces the start of rockburst. Aiming at the two types of load sources, the fracturing method of kilometer bedding drilling in the roof of coal pillar area is proposed to reduce the foundation static load. The treatment method of periodic tendency blasting fracture of leading roadway roof in a working face reduces opportune static load. The field test has been carried out, and the effect was good

A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision

Selective laser melting (SLM) is a forming technology in the field of metal additive manufacturing. In order to improve the quality of formed parts, it is necessary to monitor the selective laser melting forming process. At present, most of the research on the monitoring of the selective laser melting forming process focuses on the monitoring of the melting pool, but the quality of forming parts cannot be controlled in real-time. As an indispensable link in the SLM forming process, the quality of powder spreading directly affects the quality of the formed parts. Therefore, this paper proposes a detection method for SLM powder spreading defects, mainly using industrial cameras to collect SLM powder spreading surfaces, designing corresponding image processing algorithms to extract three common powder spreading defects, and establishing appropriate classifiers to distinguish different types of powder spreading defects. It is determined that the multilayer perceptron (MLP) is the most accurate classifier. This detection method has high recognition rate and fast detection speed, which cannot only meet the SLM forming efficiency, but also improve the quality of the formed parts through feedback control

Characteristics of In Situ Desorption Gas and their Relations to Shale Components: A Case Study of the Wufeng-Longmaxi Shales in Eastern Sichuan Basin, China

AbstractIn situ desorption gas measurement can be used to evaluate shale gas potential, sweet spot prediction, and production strategy optimization. However, gas contents and carbon isotope compositions of in situ desorption gas and the relationship to reservoir properties and shale compositions are not systematically studied from the actual production situation. In this study, 63 core shales of Wufeng-Longmaxi formation from the YY1 well in the eastern Sichuan Basin were subjected to TOC (total organic carbon), solid bitumen reflectance (Rb), maceral fractions of kerogen analysis, and X-ray diffraction (XRD) analysis to obtain shale compositions, and 10 selected samples were conducted on low-pressure N2/CO2 (N2/CO2GA), mercury injection capillary pressure (MICP), and field emission scanning electron microscopy (FE-SEM) tests to acquire reservoir properties. Meanwhile, 60 samples were also subjected to in situ desorption tests to obtain shale gas content, and 5 selected samples were used to investigate variations in shale gas compositions and carbon isotopes during the desorption process. Results indicated that transient rates of shale gas during desorption process are significantly controlled by desorption time and temperature. In terms of in situ desorption process, total gas is divided into desorbed gas and lost gas. Desorbed gas is mainly comprised of CH4, N2, CO2, and C2H6, with desorption priorities of N2 > CH4 > CO2 ≈ C2H6, which are consistent with their adsorption capacities. The δ13CH4 values tend to become heavier during desorption process, varying from -37.7‰ to -16.5‰, with a maximum increase amplitude of 18.8‰, whereas the change of δ13C2H6 value, from -38.2‰ to -34.8‰, is minor. Desorbed gas shows carbon isotope reversals, due to that preferential desorption of 12C-CH4 during desorption process results in δ13C value less negative in CH4. The tested desorbed gas, lost gas, and total gas ranged 0.088 to 1.63 cm3/g, 0.15 to 3.64 cm3/g, and 0.23 to 5.20 cm3/g, respectively. Shale gas content, i.e., desorbed gas and lost gas, is controlled primarily by TOC content and organic matter (OM)-hosted nanometer-size pores. Clay mineral content is negatively correlated with shale gas content, due to that, clay mineral pores are more easily compacted during burial and occupied by water molecules. Compared with shale gas reservoirs in North America, the studied shale reservoir has high brittle mineral content and permeability, which is considered to have great potential of shale gas resource and to be the next commercial development zone in south China

Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion

Given the problems of low accuracy and complex process steps currently faced by the field of fault diagnosis, a fault diagnosis method based on multi-sensor data weighted fusion (MSDWF) combined with depth-wise separable convolutions (DWSC) is proposed. The method takes into account the temporal and spatial information contained in multi-sensor data and can realize end-to-end bearing fault diagnosis. MSDWF is committed to comprehensively characterizing the state information of bearings, and the weighted operation of the multi-sensor data is to establish the interactive information to tap into the inline relationship in the data; DWSC equipped with residual connection is used to realize the decoupling of the channel and spatial correlation of the data. In order to verify the proposed method, the data obtained by a different number of sensors with weighted and unweighted states are used as the input of DWSC, respectively, for comparison, and finally, the effectiveness of MSDWF is verified. Through the comparison between different fault diagnosis methods, the method based on MSDWF and DWSC shows better stability and higher accuracy. Finally, when facing different experimental datasets, the method has similar performance, which shows the stability of the method on different datasets

Rolling Bearing Fault Diagnosis Based on Depth-Wise Separable Convolutions with Multi-Sensor Data Weighted Fusion

Given the problems of low accuracy and complex process steps currently faced by the field of fault diagnosis, a fault diagnosis method based on multi-sensor data weighted fusion (MSDWF) combined with depth-wise separable convolutions (DWSC) is proposed. The method takes into account the temporal and spatial information contained in multi-sensor data and can realize end-to-end bearing fault diagnosis. MSDWF is committed to comprehensively characterizing the state information of bearings, and the weighted operation of the multi-sensor data is to establish the interactive information to tap into the inline relationship in the data; DWSC equipped with residual connection is used to realize the decoupling of the channel and spatial correlation of the data. In order to verify the proposed method, the data obtained by a different number of sensors with weighted and unweighted states are used as the input of DWSC, respectively, for comparison, and finally, the effectiveness of MSDWF is verified. Through the comparison between different fault diagnosis methods, the method based on MSDWF and DWSC shows better stability and higher accuracy. Finally, when facing different experimental datasets, the method has similar performance, which shows the stability of the method on different datasets