ObjectiveMaxing Shigan Tang， as a traditional prescription for treating pneumonia， has a remarkable clinical effect. This study aims to systematically investigate the molecular mechanisms of Maxing Shigan Tang in treating pneumonia by integrating its structural and transcriptomic data at the target level. MethodsNP-TCMtarget， a developed systematic network pharmacological model focusing on drug targets， was used to mine the effect targets of Maxing Shigan Tang for treating pneumonia based on the transcriptome data. The structural targets of chemical components in Maxing Shigan Tang were predicted based on the structural information. The intersection of effect targets and structural targets was taken as the direct targets of Maxing Shigan Tang for treating pneumonia， and the remaining effect targets except direct targets were taken as indirect targets. Finally， functional enrichment analysis was performed on these targets to explore the molecular mechanism of Maxing Shigan Tang in treating pneumonia. ResultsA total of 1 604 effect targets and 816 structural targets of Maxing Shigan Tang for treating pneumonia were identified. Maxing Shigan Tang exerted its therapeutic effects through 164 direct targets and 1 440 indirect targets. The functional analysis of 1 604 effect targets predicted 19 significantly enriched pathways. Comprehensive analysis of these pathways showed that these targets were mainly linked to immune and inflammatory responses， such as cytokine-cytokine receptor interaction， necrosis factor （NF）-κB signaling pathway， and helper T cell 17 differentiation. ConclusionFocusing on the hierarchical feature of drug targets and the structural and transcriptomic data， this study systematically reveals the path of herbal component-direct target-indirect target-biological effects of Maxing Shigan Tang in treating pneumonia.

ObjectiveMaxing Shigan Tang， as a traditional prescription for treating pneumonia， has a remarkable clinical effect. This study aims to systematically investigate the molecular mechanisms of Maxing Shigan Tang in treating pneumonia by integrating its structural and transcriptomic data at the target level. MethodsNP-TCMtarget， a developed systematic network pharmacological model focusing on drug targets， was used to mine the effect targets of Maxing Shigan Tang for treating pneumonia based on the transcriptome data. The structural targets of chemical components in Maxing Shigan Tang were predicted based on the structural information. The intersection of effect targets and structural targets was taken as the direct targets of Maxing Shigan Tang for treating pneumonia， and the remaining effect targets except direct targets were taken as indirect targets. Finally， functional enrichment analysis was performed on these targets to explore the molecular mechanism of Maxing Shigan Tang in treating pneumonia. ResultsA total of 1 604 effect targets and 816 structural targets of Maxing Shigan Tang for treating pneumonia were identified. Maxing Shigan Tang exerted its therapeutic effects through 164 direct targets and 1 440 indirect targets. The functional analysis of 1 604 effect targets predicted 19 significantly enriched pathways. Comprehensive analysis of these pathways showed that these targets were mainly linked to immune and inflammatory responses， such as cytokine-cytokine receptor interaction， necrosis factor （NF）-κB signaling pathway， and helper T cell 17 differentiation. ConclusionFocusing on the hierarchical feature of drug targets and the structural and transcriptomic data， this study systematically reveals the path of herbal component-direct target-indirect target-biological effects of Maxing Shigan Tang in treating pneumonia.

Objective:To explore the effect and mechanism of different processing motivation on college students' intergroup interaction willingness by technology of eye movement.Methods:Sixty college students conforming to the study conditions were selected from Northwest Normal University and randomly divided into ingroup motivation group ( n=30) and outgroup motivation group ( n=30) according to the random number table method. Subjects of the two groups first participated in the eye movement task, and then participated in the partner selection task.In the eye movement task, the percentage of time that subjects looked at faces of different groups was recorded.And in the partner selection task, the number of selections that subjects selected faces from different groups was recorded.SPSS 20.0 software was used for repeated measurement analysis of variance. Results:(1) In the eye movement task, there was a significant interaction of group and face group type ( F=13.37, P<0.001), but the main effects of group( F=3.23, P=0.077), and face group type ( F=0.09, P>0.05) were not significant. Further simple effect analysis showed that the percentage of time that the ingroup motors looked at the yellow race((16.00±0.06)%) was significantly higher than that of the outgroup motors ((12.00±0.04)%), and the percentage of time that the outgroup motors looked at the white race((17.00±0.06)%) was significantly higher than that of the ingroup motors ((9.00±0.04)%). (2) In the task of partner selection, there was a significant interaction among group, face group type and face old and new types( F=4.38, P=0.041), and the main effect of face group type was significant( F=14.87, P<0.001). The main effect of old and new types of face was significant( F=8.88, P=0.004), but the main effect of group was not significant ( P>0.05). Further simple effect analysis showed that the two groups of college students had statistically significant differences in the selection times of familiar faces from different groups( F=11.51, P=0.001). The number of times that the ingroup generator (5.51±1.14) selected the familiar yellow race as its partner was significantly greater than that of the outgroup generator (2.30±0.65). The number of times that the outgroup generator (5.40±1.00) selected the familiar white race as its partner was significantly greater than that of the ingroup generator (3.47±0.94). (3)Preferential attention to the ingroup members was a mediator between processing motivation and intergroup interaction willingness (mediating effect=0.20, 95% CI=0.02-0.31). Conclusion:Ingroup processing motivation has a threatening effect on college students' intergroup interaction willingness, outgroup processing motivation has a promoting effect on college students' intergroup interaction willingness, and processing motivation affects intergroup interaction willingness through ingroup bias.